Aerosol optical thickness inversion method and device and electronic equipment

Abstract

The invention discloses an aerosol optical thickness inversion method and device and electronic equipment, and relates to the technical field of remote sensing, and the method comprises the steps: determining at least one highlight image block in a to-be-processed target remote sensing image; simulating the simulated surface reflectance of the highlight image block under a plurality of different aerosol optical thicknesses by using an atmospheric radiation transmission model; determining a target surface reflectance curve of a target highlight surface block corresponding to the highlight image block from a surface reflectance library of the highlight surface; determining the reference surface reflectance of the highlight image block at the acquisition time point by using the target surface reflectance curve; determining at least one target simulated surface reflectance matched with the reference surface reflectance; and fitting a target aerosol optical thickness corresponding to the highlight image block based on the aerosol optical thickness corresponding to the at least one target simulated surface reflectance. According to the method, the aerosol optical thickness of the highlight earth surface can be accurately inverted.

Description

Technical Field

[0001] The present application relates to the field of remote sensing technology, and in particular to an aerosol optical thickness inversion method, device and electronic equipment. Background Art

[0002] In the field of remote sensing technology, in order to eliminate the influence of factors such as atmosphere and light on the reflection of objects, it is necessary to perform atmospheric correction on remote sensing images to obtain parameters such as the true surface reflectivity. The key to atmospheric correction of remote sensing images is to accurately invert the aerosol optical thickness (AOT).

[0003] At present, the dark pixel method (also called the dark target method) can be used to invert the aerosol optical depth. However, the dark pixel method has a relatively high accuracy in dense vegetation areas (i.e., low-brightness bright surface areas), but has a lower accuracy in high-brightness surface areas such as urban areas. Based on this, how to accurately invert the aerosol optical depth in high-brightness surface areas is a technical problem that technicians in this field need to solve. Summary of the invention

[0004] Based on the above problems, the present application provides an aerosol optical thickness inversion method, device and electronic equipment to achieve a relatively accurate inversion of the aerosol optical thickness of the highlighted surface.

[0005] On the one hand, the present application provides an aerosol optical thickness inversion method, comprising:

[0006] Determine at least one highlighted image block belonging to a highlighted surface in a target remote sensing image to be processed;

[0007] For each highlighted image block, based on the pixel value of each pixel in the highlighted image block, the simulated surface reflectance of the highlighted image block under multiple different aerosol optical thicknesses is simulated using an atmospheric radiation transfer model;

[0008] Based on the longitude and latitude information corresponding to the highlighted image block, a target surface reflectance curve of a target highlighted surface block corresponding to the highlighted image block is determined from a surface reflectance library of the highlighted surface, wherein the target surface reflectance curve is a curve fitted based on the average surface reflectance of each of the target highlighted surface blocks at multiple reference time points, wherein the average surface reflectance of the target highlighted surface block at the reference time point is the average surface reflectance of each pixel belonging to the target highlighted surface block in the benchmark remote sensing image collected at the reference time point;

[0009] Based on the acquisition time point corresponding to the highlighted image block, the reference surface reflectance of the highlighted image block at the acquisition time point is determined using the target surface reflectance curve;

[0010] Determining at least one target simulated surface reflectivity matching the reference surface reflectivity from the simulated surface reflectivities corresponding to the multiple different aerosol optical thicknesses;

[0011] Based on the aerosol optical thickness corresponding to the at least one target simulated surface reflectivity, the target aerosol optical thickness corresponding to the highlighted image block is fitted.

[0012] In a possible implementation, determining at least one target simulated surface reflectivity that matches the reference surface reflectivity from the simulated surface reflectivities corresponding to the multiple different aerosol optical thicknesses includes:

[0013] For each aerosol optical thickness, the spectral angle is calculated based on the simulated surface reflectance of the highlighted image block under the aerosol optical thickness and the reference surface reflectance to obtain the spectral angle corresponding to the aerosol optical thickness;

[0014] From the spectral angles corresponding to a plurality of different aerosol optical thicknesses, a minimum first spectral angle and a second spectral angle and a third spectral angle having a minimum angular difference from the first spectral angle are determined.

[0015] In another possible implementation, the step of fitting the target aerosol optical thickness corresponding to the highlighted image block based on the aerosol optical thickness corresponding to the at least one target simulated surface reflectivity includes:

[0016] Based on the first spectral angle α 1 The corresponding first aerosol optical thickness AOT 1 , the second spectral angle α 2 The corresponding second aerosol optical thickness AOT 2 and the third spectral angle α 3 The corresponding third aerosol optical thickness AOT 3 , the first thickness parameter a and the second thickness parameter b are fitted using the following formula:

[0017]

[0018] Based on the first thickness parameter a and the second thickness parameter b, the target aerosol optical thickness corresponding to the highlighted image block is determined by the following formula:

[0019]

[0020] Among them, AOT 4is the target aerosol optical thickness corresponding to the highlighted image block.

[0021] In another possible implementation, based on the pixel value of each pixel in the highlighted image block, simulating the simulated surface reflectance of the highlighted image block under multiple different aerosol optical thicknesses using an atmospheric radiation transfer model includes:

[0022] Determining a target aerosol mode and a target atmosphere mode corresponding to the highlighted image block based on the latitude and longitude information corresponding to the highlighted image block;

[0023] Based on the target aerosol model, the target atmospheric model and the pixel value of each pixel in the highlighted image block, the simulated surface reflectivity of the highlighted image block under a plurality of different aerosol optical thicknesses is simulated using an atmospheric radiation transfer model.

[0024] In yet another possible implementation, the surface reflectance library of the highlighted surface includes a surface reflectance curve of at least one highlighted surface block;

[0025] The surface reflectivity curve of the highlighted surface block is obtained by:

[0026] Obtaining an image time series, wherein the image time series includes reference remote sensing images collected at multiple reference time points, wherein the reference remote sensing images are remote sensing images that have been atmospherically corrected, and each pixel in the reference remote sensing images has its own surface reflectivity;

[0027] For each frame of the reference remote sensing image, removing pixels in the reference remote sensing image that are covered by clouds or snow, to obtain a processed reference remote sensing image;

[0028] For each processed reference remote sensing image, determining at least one reference image block belonging to at least one highlighted surface block in the reference remote sensing image, wherein each reference image block belongs to one highlighted surface block;

[0029] For each reference image block corresponding to each highlighted surface block in the benchmark remote sensing image, based on the surface reflectance of each pixel in the reference image block corresponding to the highlighted surface block, determine the average surface reflectance of the highlighted surface block at the reference time point corresponding to the benchmark remote sensing image;

[0030] For each highlighted surface block, a surface reflectivity curve of the highlighted surface block is fitted based on the average surface reflectivity of the highlighted surface block at the multiple reference time points.

[0031] In yet another possible implementation, the determining at least one reference image block belonging to at least one highlighted surface block in the benchmark remote sensing image includes:

[0032] Determine at least one candidate reference image block belonging to a highlighted surface in the reference remote sensing image;

[0033] At least one reference image block having a ratio of valid pixels exceeding a set threshold is determined from the at least one candidate reference image block, each reference image block corresponds to a highlighted surface block, wherein the valid pixels are pixels having pixel values.

[0034] In yet another possible implementation, after fitting the surface reflectance curve of the highlighted surface block, the method further includes:

[0035] For each highlighted surface block, determining a change rate of the surface reflectivity corresponding to each unit time point in a surface reflectivity curve of the highlighted surface block;

[0036] Determine a maximum change rate and a minimum change rate based on the change rate corresponding to each unit time point in the surface reflectivity curve;

[0037] Determining a maximum change rate threshold based on the maximum change rate and the minimum change rate;

[0038] The surface reflectivity corresponding to the target unit time point that meets the conditions is removed from the surface reflectivity curve, wherein the rate of change of the surface reflectivity corresponding to the target unit time point that meets the conditions exceeds the maximum rate of change threshold, and the rate of change of the surface reflectivity corresponding to two adjacent unit time points adjacent to the target unit time point does not exceed the maximum rate of change threshold.

[0039] In yet another possible implementation, the aerosol optical thickness inversion method further includes:

[0040] Determine at least one low-brightness image block belonging to a low-brightness surface in the target remote sensing image;

[0041] The aerosol thickness of each low-brightness image block is inverted using the dark pixel method;

[0042] Based on the target aerosol thickness of each highlighted image block and the aerosol thickness of each low-brightness image block, atmospheric correction is performed on the target remote sensing image.

[0043] On the other hand, the present application also provides an aerosol optical thickness inversion device, comprising:

[0044] A bright block determination unit, used to determine at least one bright image block belonging to a bright surface in a target remote sensing image to be processed;

[0045] A reflectivity simulation unit, for simulating, for each highlighted image block, the simulated surface reflectivity of the highlighted image block under a plurality of different aerosol optical thicknesses using an atmospheric radiation transfer model based on the pixel value of each pixel in the highlighted image block;

[0046] A library query unit is used to determine, based on the latitude and longitude information corresponding to the highlighted image block, a target surface reflectance curve of a target highlighted surface block corresponding to the highlighted image block from a surface reflectance library of the highlighted surface, wherein the target surface reflectance curve is a curve fitted based on the average surface reflectance of each target highlighted surface block at a plurality of reference time points, wherein the average surface reflectance of the target highlighted surface block at the reference time point is the average surface reflectance of each pixel belonging to the target highlighted surface block in the benchmark remote sensing image collected at the reference time point;

[0047] A reflectivity query unit, configured to determine a reference surface reflectivity of the highlighted image block at the acquisition time point using the target surface reflectivity curve based on the acquisition time point corresponding to the highlighted image block;

[0048] A reflectivity matching unit, configured to determine at least one target simulated surface reflectivity that matches the reference surface reflectivity from among the simulated surface reflectivities corresponding to the plurality of different aerosol optical thicknesses;

[0049] The aerosol determination unit is used to fit the target aerosol optical thickness corresponding to the highlighted image block based on the aerosol optical thickness corresponding to the at least one target simulated surface reflectivity.

[0050] In another aspect, the present application further provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0051] The memory is used to store computer programs;

[0052] The processor is used to execute the computer program so that the electronic device can implement the aerosol optical thickness inversion method as described in any one of the above.

[0053] On the other hand, the present application also provides a computer program product, including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements any aerosol optical thickness inversion method provided in the embodiments of the present application.

[0054] On the other hand, the present application also provides a computer-readable storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any aerosol optical thickness inversion method provided in the embodiments of the present application.

[0055] From the above content, it can be seen that the present application constructs a surface reflectance library for the highlighted surface. For each highlighted image block in the target remote sensing image that belongs to the highlighted surface, the target surface reflectance curve of the target highlighted surface block corresponding to the highlighted image block will be determined from the surface reflectance library. Since the target surface reflectance curve of the target highlighted surface block is a curve fitted based on the average surface reflectance of the target highlighted surface block at multiple reference time points, by performing curve fitting on the average surface reflectance of each of the multiple reference time points, the influence of noise on the surface reflectance can be effectively eliminated, so that the fitted target surface reflectance curve can accurately reflect the change in the surface reflectance of the highlighted surface block. On this basis, after using the atmospheric radiation transfer model to simulate the simulated surface reflectance of the highlighted image block under different aerosol optical thicknesses, combined with the reference surface reflectance corresponding to the acquisition time point of the highlighted image area determined from the target surface reflectance curve, the aerosol optical height corresponding to the highlighted image block can be determined more accurately, thereby improving the accuracy of determining the aerosol optical thickness of the highlighted surface area. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0057] Figure 1 A schematic diagram of a process of an aerosol optical thickness inversion method provided in this application;

[0058] Figure 2 A schematic diagram of an implementation process for determining a surface reflectance curve of each highlighted surface block in a surface reflectance library of a highlighted surface in this application;

[0059] Figure 3 A schematic diagram of another process of the aerosol optical thickness inversion method provided in this application;

[0060] Figure 4 A schematic diagram of the composition structure of the aerosol optical thickness inversion device provided in this application;

[0061] Figure 5 A schematic diagram of the composition architecture of an electronic device provided in this application. DETAILED DESCRIPTION

[0062] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation mode of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application. It is known to those skilled in the art that with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0063] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0064] like Figure 1 , shows a schematic flow chart of the aerosol optical thickness inversion method provided by the present application. The method of this embodiment may include:

[0065] S101, determining at least one highlighted image block belonging to a highlighted surface in a target remote sensing image to be processed.

[0066] In this application, for the sake of distinction, the remote sensing image to be processed is referred to as a target remote sensing image.

[0067] Among them, the highlighted surface refers to the surface area with relatively high brightness values ​​of the pixels presented in the remote sensing image. It can be understood that the highlighted surface generally refers to the surface of the urban area. Since the reflectivity of the urban area belongs to the type of land object with a larger reflectivity value among all land object types, the urban area shows brighter features in the remote sensing image, so the urban area is also called a high reflectivity area or a highlighted surface area. Correspondingly, each highlighted image block can be a remote sensing image block corresponding to a town area.

[0068] In the present application, at least one highlighted image block belonging to the highlighted surface can be determined based on the brightness value of each pixel in the target remote sensing image. For example, the brightness values ​​of pixels exceeding a set percentage or all pixels in each highlighted image block exceed a set brightness threshold. There is no restriction on the specific implementation of determining each highlighted image block in the target remote sensing image.

[0069] S102, for each highlighted image block, based on the pixel value of each pixel in the highlighted image block, using an atmospheric radiation transfer model to simulate the simulated surface reflectance of the highlighted image block under multiple different aerosol optical thicknesses.

[0070] The image header file of the target remote sensing image includes the pixel value of each pixel, which may include but is not limited to: the brightness value of the pixel, angle information, sensor position and attitude information corresponding to the pixel.

[0071] The atmospheric radiation transfer model may be a 6S (Second Simulation of Satellite Signal in the Solar Spectrum) radiation transfer model. Of course, it may also be other atmospheric radiation transfer models, without limitation.

[0072] In the present application, the number of multiple different aerosol optical thicknesses required to simulate the surface reflectivity using the atmospheric radiation transfer model and the specific values ​​of the aerosol optical thickness can be set according to actual needs. For example, the atmospheric radiation transfer model can be used to simulate the surface reflectivity of the highlighted image block when the aerosol optical thickness values ​​are 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9 respectively.

[0073] For the sake of distinction, the surface reflectivity simulated by the atmospheric radiation transfer model is called simulated surface reflectivity.

[0074] There are many possibilities for simulating the surface reflectivity based on the atmospheric radiation transfer model, and this application does not impose any restrictions on this.

[0075] For example, in a possible implementation, the target aerosol pattern and the target atmospheric pattern corresponding to the highlighted image block can be determined based on the latitude and longitude information corresponding to the highlighted image block. Accordingly, based on the target aerosol pattern, the target atmospheric pattern and the pixel value of each pixel in the highlighted image block, the atmospheric radiation transfer model is used to simulate the simulated surface reflectance of the highlighted image block at multiple different aerosol optical thicknesses. For example, based on the target aerosol pattern, the target atmospheric pattern and the pixel value of each pixel in the highlighted image block, the 6S radiation transfer model can be used to simulate the surface reflectance of the highlighted image block at different aerosol optical thicknesses.

[0076] The latitude and longitude information of the highlighted image block may also be stored in the image header file of the target remote sensing image.

[0077] Based on the longitude and latitude information corresponding to the highlighted image block, the aerosol mode and the atmospheric mode corresponding to the highlighted image block can be determined. The aerosol mode can be divided into model types such as particle mode and desert mode, and the atmospheric mode can be divided into mid-latitude summer and mid-latitude autumn modes, without restriction.

[0078] In the present application, for the sake of distinction, the atmospheric mode corresponding to the highlighted image block is referred to as the target atmospheric mode, and the aerosol mode corresponding to the highlighted image block is referred to as the target aerosol mode.

[0079] S103, based on the longitude and latitude information corresponding to the highlighted image block, determine a target surface reflectance curve of a target highlighted surface block corresponding to the highlighted image block from a surface reflectance library of the highlighted surface.

[0080] In the present application, a surface reflectance library of a highlighted surface is pre-constructed, which includes surface reflectance curves of a plurality of highlighted surface blocks.

[0081] The surface reflectance curve of the highlighted surface block is a curve fitted based on the average surface reflectance of the highlighted surface block at multiple reference time points. The average surface reflectance of the highlighted surface block at the reference time point is the average surface reflectance of each pixel belonging to the highlighted surface block in the reference remote sensing image collected at the reference time point.

[0082] For the sake of easy distinction, the highlighted surface block corresponding to the highlighted image block is called the target highlighted surface block, and the surface reflectance curve of the target highlighted surface block is called the target surface reflectance curve. The target highlighted surface block refers to the surface block whose longitude and latitude information matches the highlighted image block. Therefore, the highlighted image block is the remote sensing image block corresponding to the target highlighted surface block. Correspondingly, the target surface reflectance curve is a curve fitted based on the average surface reflectance of the target highlighted surface block at multiple reference time points. The average surface reflectance of the target highlighted surface block at the reference time point is the average value of the surface reflectance of each pixel belonging to the target highlighted surface block in the benchmark remote sensing image collected at the reference time point.

[0083] The reference remote sensing image is a remote sensing image that has been atmospherically corrected, so the surface reflectance of each pixel in the reference remote sensing image is a determined and relatively accurate surface reflectance. The specific implementation of atmospheric correction of the reference remote sensing image can be selected as needed and is not limited thereto.

[0084] In the present application, a surface reflectance library is constructed specifically for the highlighted surface, so that the surface reflectance curve of each highlighted surface block in the surface reflectance library can more accurately reflect the change of the surface reflectance of the highlighted surface block at different time points. Therefore, based on the surface reflectance library, reliable data support can be provided for the inversion of the aerosol optical thickness of each highlighted image block, which is conducive to the subsequent accurate determination of the aerosol optical thickness of each highlighted image block.

[0085] Moreover, what is stored in the surface reflectivity library in the present application is the surface reflectivity curve of each highlighted surface block, and the surface reflectivity curve is a curve fitted based on the average surface reflectivity of the highlighted surface block at multiple reference time points. The influence of noise on the surface reflectivity can be effectively eliminated through curve fitting. Compared with simply listing the discrete surface reflectivity of the highlighted surface block at different time points, the surface reflectivity curve can more accurately represent the actual surface reflectivity of the highlighted surface block.

[0086] S104, based on the acquisition time point corresponding to the highlighted image block, using the target surface reflectivity curve to determine the reference surface reflectivity of the highlighted image block at the acquisition time point.

[0087] For example, the surface reflectivity corresponding to the acquisition time point is queried from the target surface reflectivity curve to obtain the reference surface reflectivity.

[0088] It should be noted that steps S103 and S104 may be executed synchronously with step S102, or step S102 may be executed first and then steps S103 and S104; of course, steps S103 and S104 may be executed first and then step S102, and there is no limitation to this.

[0089] S105, determining at least one target simulated surface reflectivity that matches a reference surface reflectivity from among the simulated surface reflectivities corresponding to a plurality of different aerosol optical thicknesses.

[0090] For example, if there is a simulated surface reflectivity that is the same as the reference surface reflectivity, the simulated surface reflectivity can be determined as the target simulated surface reflectivity.

[0091] For example, considering that in most cases there may not be a simulated surface reflectivity that is exactly the same as the reference surface reflectivity, on this basis, in order to accurately determine the aerosol optical thickness of the highlighted image blocks, two or three target simulated surface reflectivities that are closest to the reference surface reflectivity can be determined.

[0092] S106, fitting a target aerosol optical thickness corresponding to a highlighted image block based on an aerosol optical thickness corresponding to at least one target simulated surface reflectivity.

[0093] It is understandable that if there is only one target simulated surface reflectance, then the target simulated surface reflectance is the same as the reference surface reflectance. In this case, the aerosol optical thickness corresponding to the target simulated surface reflectance is the aerosol optical thickness corresponding to the highlighted image block, and accordingly, the fitted target aerosol optical thickness is actually the target simulated surface reflectance.

[0094] If there are multiple target simulated surface reflectivities, it means that the aerosol optical thickness corresponding to the multiple target simulated surface reflectivities is close to the real aerosol optical thickness corresponding to the highlighted image block, but none of them is the real aerosol optical thickness of the highlighted image block. Based on this, by fitting the aerosol optical thickness corresponding to the multiple target simulated surface reflectivities, the target aerosol optical thickness corresponding to the highlighted image block can be fitted more accurately.

[0095] In the present application, the specific implementation process of fitting the aerosol optical thickness corresponding to at least one target simulated surface reflectivity is not limited.

[0096] From the above content, it can be seen that the present application constructs a surface reflectance library for the highlighted surface. For each highlighted image block in the target remote sensing image that belongs to the highlighted surface, the target surface reflectance curve of the target highlighted surface block corresponding to the highlighted image block will be determined from the surface reflectance library. Since the target surface reflectance curve of the target highlighted surface block is a curve fitted based on the average surface reflectance of the target highlighted surface block at multiple reference time points, by performing curve fitting on the average surface reflectance of each of the multiple reference time points, the influence of noise on the surface reflectance can be effectively eliminated, so that the fitted target surface reflectance curve can accurately reflect the change in the surface reflectance of the highlighted surface block. On this basis, after using the atmospheric radiation transfer model to simulate the simulated surface reflectance of the highlighted image block under different aerosol optical thicknesses, combined with the reference surface reflectance corresponding to the acquisition time point of the highlighted image area determined from the target surface reflectance curve, the aerosol optical height corresponding to the highlighted image block can be determined more accurately, thereby improving the accuracy of determining the aerosol optical thickness of the highlighted surface area.

[0097] It is understandable that, while or after determining each high-brightness image block, the present application may also determine at least one low-brightness image block belonging to the low-brightness surface in the target remote sensing image, and invert the aerosol thickness of each low-brightness image block using the dark pixel method. The proportion of pixels in the low-brightness image block whose brightness value is lower than the set brightness threshold exceeds the set proportion.

[0098] On this basis, the present application can perform atmospheric correction on the target remote sensing image based on the target aerosol thickness of each highlight image block and the aerosol thickness of each low-brightness image block.

[0099] For example, based on the target aerosol thickness of each highlight image block and the aerosol thickness of each low-brightness image block, the aerosol thickness of each pixel in the target remote sensing image is obtained by spatial interpolation, and based on the aerosol thickness of each pixel in the target remote sensing image, the target remote sensing image is atmospherically corrected. This application does not impose any restrictions on the specific implementation process of atmospheric correction.

[0100] In order to facilitate understanding of the solution of the present application, an implementation process of constructing a surface reflectance curve of each highlighted surface block in a surface reflectance library of the highlighted surface in the present application is first introduced below.

[0101] like Figure 2 , shows a schematic diagram of an implementation process of determining a surface reflectivity curve of a highlighted surface block provided in an embodiment of the present application, and the process may include:

[0102] S301, obtaining an image time series.

[0103] The image time series includes reference remote sensing images collected at multiple reference time points. The reference remote sensing image is a remote sensing image that has been atmospherically corrected, and each pixel in the reference remote sensing image has its own surface reflectivity.

[0104] In this application, multiple reference time points for collecting remote sensing images can be determined according to actual needs, and the designated area for collecting remote sensing images can be determined. For example, if remote sensing image analysis of the Beijing-Tianjin-Hebei map is required later, the designated area can be the Beijing-Tianjin-Hebei region.

[0105] S302: for each frame of the reference remote sensing image, remove the pixels belonging to clouds or snow in the reference remote sensing image to obtain a processed reference remote sensing image.

[0106] Among them, removing the pixels belonging to clouds or snow in the reference remote sensing image can detect the pixels belonging to clouds or snow in the reference remote sensing image through a cloud and snow detection algorithm, and remove the corresponding pixels.

[0107] In the present application, there is no limitation on the specific implementation of detecting pixels belonging to clouds or snow.

[0108] For example, let’s identify the pixels belonging to clouds:

[0109] For each frame of the reference remote sensing image, the present application may first use an object-oriented cloud and cloud shadow detection (Fmask) algorithm (also known as a functional masking algorithm) to perform potential cloud detection on the reference remote sensing image to identify potential cloud pixels in the reference remote sensing image. Among them, potential cloud pixels may be pixels belonging to clouds or clear sky pixels. In contrast, other pixels that are not potential cloud pixels are regarded as absolute clear sky pixels.

[0110] Potential cloud area detection includes basic test, brightness test, HOT (Haze Optimized Transformation) test and band test.

[0111] Among them, based on the bright and cold characteristics of clouds, potential cloud areas can be distinguished from snow-covered areas and some vegetation through basic tests. The basic test determines the potential cloud area through the following formula 1, that is, the pixels belonging to the potential cloud area need to meet the conditions shown in the following formula 1:

[0112] Band7>0.03 and BT<27 and NDSI<0.8 and NDVI<0.8 (Formula 1);

[0113] Among them, Band means band, which refers to a specific frequency band or wavelength range in the electromagnetic spectrum. Remote sensing images can correspond to multiple bands, and the number of bands may vary depending on the specific application scenario. In this application, Band represents the surface reflectivity of the pixel in the remote sensing image (such as the benchmark remote sensing image) in the band. Correspondingly, in Formula 1, Band7 represents the surface reflectivity of the pixel in the benchmark remote sensing image in Band 7.

[0114] BT is the bright temperature band of the pixel, and BT < 27 means the bright temperature band is less than 27 degrees.

[0115] NDSI is the Normalized Difference Snow Index, which is an index used to detect snow in remote sensing technology. The NDSI value corresponding to the pixel can be calculated by the following formula 2:

[0116]

[0117] Among them, Band3 is the surface reflectance of the pixel in Band 3; Band6 is the surface reflectance of the pixel in Band 6.

[0118] NDVI is the normalized vegetation index. The NDVI value corresponding to the pixel can be calculated by the following formula 3:

[0119]

[0120] Among them, Band4 is the surface reflectance of the pixel in Band 4; Band5 is the surface reflectance of the pixel in Band 5.

[0121] In Formula 1, and means and, that is, the pixel must simultaneously meet the conditions corresponding to the above four parameters to be determined as a pixel belonging to the potential cloud area.

[0122] On the basis of the above, the whiteness test and HOT test are further used to separate some pixels that are obviously not clouds. Among them, the whiteness test is used to identify the cloud area based on the white feature of clouds in remote sensing images, and the HOT test is used to separate smoke, thin clouds and sunny pixels. For some bright pixels, such as rocks, turbid water or ice and snow surfaces, the HOT test can also be used to identify them.

[0123] For example, in the whiteness test, the whiteness of the pixel belonging to the cloud area is less than 0.7. The whiteness of the pixel can be obtained by the following formula 4:

[0124]

[0125] Among them, Bandj is the surface reflectance of the pixel in the band corresponding to the band number j (ie, band j); in the brightness test process, j can be an integer from 1 to 3.

[0126] Among them, MeanVis is an intermediate parameter, which can be calculated by the following formula 5:

[0127]

[0128] Band2 is the surface reflectance of the pixel in Band 2, and the meanings of Band3 and Band4 are as described above.

[0129] In the HOT test, the HOT value of the pixel is required to be greater than zero, then the pixel is determined to belong to the cloud area. The HOT value of the pixel can be calculated by the following formula 6:

[0130] HOT = (Band2-0.5)*(Band4-0.08) (Formula 6);

[0131] Through the above tests, the pixels belonging to the potential cloud area can be determined. For the pixels belonging to the potential cloud area, the pixels that meet the conditions in the following formula 7 are further determined as clouds over water or land, so as to finally identify the clouds over land and water:

[0132] (NDVI<0.01 and Band5<0.11) or (NDVI<0.1 and Band5<0.05) (Formula 7);

[0133] The relevant parameters in Formula 7 can refer to the previous related introduction. In Formula 7, "and" means and, so it can be seen that if a pixel belonging to a potential cloud area meets the conditions before or after "or" in Formula 7, it can be determined that the pixel belongs to a cloud-covered pixel.

[0134] It is understandable that the above is an example of detecting pixels belonging to clouds, but the Fmask algorithm is also used for snow detection, and the details are similar and will not be repeated. Of course, there are other ways to identify pixels belonging to clouds and snow cover, and there is no limitation on this.

[0135] Furthermore, in order to improve the accuracy of cloud detection, the present application can also use the Fmask algorithm to detect pixels belonging to clouds and snow, and then use the quality assessment (QA) band of the Landsat 8 Operational Land Imager (OLI) imaging tool to perform secondary screening of the benchmark remote sensing image pixel by pixel. The specific implementation process is not restricted.

[0136] After identifying the pixels belonging to clouds and snow in the reference remote sensing image, the present application can remove the pixels belonging to clouds and snow in the reference remote sensing image by masking, etc., such as using the Fmask algorithm to mask the reference remote sensing image, etc., without limitation.

[0137] S303: For each processed reference remote sensing image, determine at least one reference image block belonging to at least one highlighted surface block in the reference remote sensing image.

[0138] Each reference image block is a remote sensing image block belonging to a highlighted surface in the benchmark remote sensing image. Since each reference image block is a remote sensing image of a highlighted surface block, each reference image block corresponds to a highlighted surface block.

[0139] Correspondingly, the longitude and latitude information of the highlighted surface block is the same as the longitude and latitude information of the reference image block corresponding to the highlighted surface block.

[0140] For example, the reference image block may be a continuous image block composed of pixels whose brightness values ​​exceed a set brightness threshold, and at least one reference image block is obtained, and each reference image area corresponds to a highlighted surface block, without specific limitation.

[0141] In a possible implementation, at least one candidate reference image block belonging to a highlighted surface in a reference remote sensing image after removing clouds and snow may be first determined. The proportion of pixels in the candidate reference image block whose brightness value exceeds a set brightness threshold exceeds a set proportion. Accordingly, at least one reference image block whose proportion of valid pixels exceeds a set threshold is determined from the at least one candidate reference image block, and each reference image block corresponds to a highlighted surface block.

[0142] Among them, valid pixels refer to pixels with pixel values. It can be understood that if some pixels belonging to clouds or snow in the processed benchmark remote sensing image are removed, then the pixels belonging to clouds and snow in the processed benchmark remote sensing image no longer have pixel values, resulting in vacancies in the benchmark remote sensing image at these pixel positions.

[0143] Based on this, if there are many missing pixels in the reference image block, the surface reflectance of each pixel in this reference image block cannot accurately reflect the average surface reflectance of the highlighted surface block corresponding to the reference image block. Therefore, it is necessary to screen out the reference image blocks whose proportion of valid pixels exceeds the set threshold.

[0144] S304, for each reference image block corresponding to the benchmark remote sensing image, based on the surface reflectivity of each pixel in the reference image block corresponding to the highlighted surface block, determine the average surface reflectivity of the highlighted surface block at the reference time point corresponding to the benchmark remote sensing image.

[0145] It can be understood that each benchmark remote sensing image corresponds to the reference time point at which the benchmark remote sensing image was collected. Therefore, the average surface reflectance of each pixel in the reference image block is the average surface reflectance of the highlighted surface block corresponding to the reference image block at the reference time point.

[0146] The present application uses the average surface reflectance of the highlighted surface block to replace the surface reflectance of a single cloud-like image, which can effectively measure the number of effective surface reflectances and reduce the inaccurate data in the constructed surface reflectance library due to inaccurate surface reflectance of a single pixel.

[0147] S305 , for each highlighted surface block, fitting a surface reflectance curve of the highlighted surface block based on the average surface reflectance of the highlighted surface block at multiple reference time points.

[0148] It can be understood that the highlighted surface blocks at the same position in different benchmark remote sensing images represent the same surface block, and the highlighted surface blocks at the same position in different benchmark remote sensing images only have different pixel information contained in different benchmark remote sensing images. Correspondingly, the average surface reflectivity of the highlighted surface block in each benchmark remote sensing image is the average surface reflectivity of the highlighted surface block at each reference time point.

[0149] In the present application, the specific implementation of fitting the surface reflectance curve based on the average surface reflectance of the highlighted surface block at multiple reference time points can adopt a variety of fitting algorithms, which are not limited.

[0150] For example, considering that the average surface reflectance of the highlighted surface block at multiple reference time points is a time series, the present application can use any of the three time series models proposed by Zhu and Woodcock for fitting. These three time series models can be divided into simple models, advanced models and complete models. Among them, the simple model, advanced model and complete model are respectively expressed as the following formulas 8, 9 and 10:

[0151]

[0152] in, It represents the function expression of the average surface reflectance curve fitted by a simple model; A function representing the average surface reflectance curve fitted by the advanced model; It represents the function representation of the average surface reflectance curve fitted by the complete model.

[0153] It is understandable that remote sensing images can correspond to multiple bands, and the surface reflectance of remote sensing images in different bands will also be different. Therefore, it is necessary to determine the surface reflectance corresponding to the remote sensing image for each band. Based on this, in the above formulas 8, 9 and 10, i is the band number, and i is an integer from 1 to , n b The total number of bands corresponding to the benchmark remote sensing image.

[0154] Among them, a 0,i 、a 1,i , b 1,i and c 1,i are the different fitting parameters corresponding to band i in the simple simulation model;

[0155] χ is the annual cumulative days, that is, the total number of days corresponding to the first reference time point to the last reference time point among multiple reference time points, and T is the annual cycle number, and its value can be 365.25.

[0156] a 2,i and b 2,i are the two fitting parameters corresponding to band i in the advanced model.

[0157] a 3,i and b 3,i are the two fitting parameters corresponding to band i in the complete model.

[0158] Taking a simple model as an example, the average surface reflectance of the highlighted surface block at multiple reference time points is used as Corresponding to multiple specific values, so that a can be solved 0,i 、a 1,i , b 1,i and c 1,i The specific value of can naturally be obtained The corresponding function representation.

[0159] Of course, the above is only an example, and the same is true for the case where the surface reflectivity curve is fitted in other ways.

[0160] It is understandable that in addition to the noise interference caused by clouds and snow to the baseline remote sensing image, there may be other noise effects, which may cause abnormal points in the fitted surface reflectance curve. Based on this, after fitting the surface reflectance curve of the highlighted surface block, the possible abnormal points in the surface reflectance curve can also be removed.

[0161] Specifically, for each highlighted surface block, the change rate of the surface reflectance corresponding to each unit time point in the surface reflectance curve of the highlighted surface block can be determined. Then, based on the change rate corresponding to each unit time point in the surface reflectance curve, the maximum change rate and the minimum change rate are determined, and based on the maximum change rate and the minimum change rate, the maximum change rate threshold is determined. Accordingly, the surface reflectance corresponding to the target unit time point that meets the conditions in the surface reflectance curve is removed.

[0162] Among them, the change rate of the surface reflectivity corresponding to the target unit time point that meets the conditions exceeds the maximum change rate threshold, and the change rate of the surface reflectivity corresponding to two adjacent unit time points adjacent to the target unit time point does not exceed the maximum change rate threshold.

[0163] The size of the unit time point can be set according to actual needs, for example, each hour can be used as a unit time point, or each day or month can be used as a unit time point, and there is no restriction on this.

[0164] Among them, the rate of change of surface reflectivity corresponding to unit time point t is θ t It can be calculated by the following formula 11:

[0165]

[0166] Among them, N t is the surface reflectivity at unit time point t, N t+1 is the surface reflectivity at unit time point t+1.

[0167] The maximum change rate threshold can be determined in a variety of ways. For example, in one possible implementation, the maximum change rate threshold can be three times the difference between the maximum change rate and the minimum change rate, that is, the maximum change rate threshold θ ε It can be shown as the following formula twelve:

[0168]

[0169] Among them, N Max is the maximum rate of change, N Min is the minimum rate of change; t(N Max ) represents the unit time point corresponding to the maximum rate of change, t(N Min ) represents the unit time point corresponding to the minimum rate of change.

[0170] On this basis, if the change rate corresponding to a unit time point exceeds the maximum change rate threshold, and the change rates corresponding to other unit time points after the unit time point also exceed the maximum change rate threshold, it means that there are new changes in the curve as a whole after the unit time point; if only the change rate corresponding to the unit time point exceeds the maximum change rate threshold, it means that the surface reflectivity corresponding to the unit time point is an abnormal value, and therefore, it is necessary to remove the surface reflectivity corresponding to the unit time point from the surface reflectivity curve.

[0171] Among them, removing the surface reflectance corresponding to the unit time point may be marking the surface reflectance corresponding to the unit time point as an abnormal value, so that the surface reflectance corresponding to the unit time point will not be used as the reference surface reflectance required for atmospheric correction in the future. Of course, there may be other ways to remove abnormal surface reflectance, which are not limited.

[0172] The following introduces the aerosol optical thickness inversion method provided by this application by taking one implementation as an example. Figure 3 , shows another implementation flow diagram of the aerosol optical thickness inversion method provided by the present application. The method of this embodiment may include:

[0173] S401, determining at least one highlighted image block belonging to a highlighted surface in a target remote sensing image to be processed.

[0174] S402 : For each highlighted image block, based on the latitude and longitude information corresponding to the highlighted image block, determine the target aerosol mode and the target atmosphere mode corresponding to the highlighted image block.

[0175] For each highlighted image block, steps S402 to S408 are respectively performed to determine the aerosol optical thickness corresponding to the highlighted image block.

[0176] S403, based on the target aerosol model, the target atmospheric model and the pixel value of each pixel in the highlighted image block, the atmospheric radiation transfer model is used to simulate the simulated surface reflectance of the highlighted image block under multiple different aerosol optical thicknesses.

[0177] It is understandable that steps S402 and S403 are described by simulating a method of simulating the simulated surface reflectance. However, if the simulated surface reflectance of the highlighted image block under multiple different aerosol optical thicknesses is simulated by other methods, it is also applicable to this embodiment, and there is no limitation on this.

[0178] S404, based on the longitude and latitude information corresponding to the highlighted image block, determine a target surface reflectance curve of a target highlighted surface block corresponding to the highlighted image block from a surface reflectance library of the highlighted surface.

[0179] Among them, the target surface reflectivity curve is a curve fitted based on the average surface reflectivity of the target highlighted surface blocks at multiple reference time points, wherein the average surface reflectivity of the target highlighted surface blocks at the reference time point is the average value of the surface reflectivity of each pixel belonging to the target highlighted surface block in the benchmark remote sensing image collected at the reference time point.

[0180] S405 , based on the acquisition time point corresponding to the highlighted image block, determine the reference surface reflectance of the highlighted image block at the acquisition time point using the target surface reflectance curve.

[0181] For the above steps S404 and S405, reference may be made to the related introduction of the previous embodiment, which will not be described again here.

[0182] S406, for each aerosol optical thickness, based on the simulated surface reflectance and the reference surface reflectance of the highlighted image block under the aerosol optical thickness, the spectral angle is calculated to obtain the spectral angle corresponding to the aerosol optical thickness.

[0183] The spectral angle is the generalized angle between the highlighted image block and the highlighted surface block. The smaller the spectral angle, the closer the simulated surface reflectivity is to the reference surface reflectivity.

[0184] This application does not impose any limitation on the specific calculation process for calculating the spectral angle.

[0185] For example, in a possible implementation, for each aerosol optical thickness, the spectral angle α can be calculated by the following formula 13:

[0186]

[0187] Among them, r i is the simulated surface reflectance of the highlighted image block in the i-th band (that is, the band with band number i); s i is the reference surface reflectance of the highlighted surface block corresponding to the highlighted image block in the i-th band (that is, the band with band number i). i and n b The meaning of can be found in the previous introduction and will not be repeated here.

[0188] S407 , determining, from the spectral angles corresponding to the multiple different aerosol optical thicknesses, a minimum first spectral angle and a second spectral angle and a third spectral angle having the minimum angle difference with the first spectral angle.

[0189] For example, the second spectral angle is a spectral angle smaller than the first spectral angle and closest to the first spectral angle among the spectral angles corresponding to multiple different aerosol optical thicknesses; and the third spectral angle is a spectral angle larger than the first spectral angle and closest to the first spectral angle among the multiple spectral angles.

[0190] It can be understood that the simulated surface reflectances corresponding to these three spectral angles are also the three simulated surface reflectances closest to the reference surface reflectance.

[0191] Of course, in this embodiment, three spectral angles are selected as an example for explanation. In practical applications, the minimum two or four spectral angles may also be selected, and there is no limitation to this.

[0192] S408 , fitting a target aerosol optical thickness corresponding to the highlighted image block based on the aerosol optical thicknesses corresponding to the first spectral angle, the second spectral angle, and the third spectral angle.

[0193] There are many ways to combine the aerosol optical thicknesses corresponding to the three spectral angles to fit the target aerosol optical thickness corresponding to the highlighted image block, and there is no restriction on this.

[0194] In a possible implementation, based on the first spectral angle α 1 The corresponding first aerosol optical thickness AOT 1 , the second spectral angle α 2 The corresponding second aerosol optical thickness AOT 2 and the third spectral angle α 3 The corresponding third aerosol optical thickness AOT 3 , the first thickness parameter a and the second thickness parameter b are fitted using the following formula 14:

[0195]

[0196] Based on the first thickness parameter a and the second thickness parameter b, the target aerosol optical thickness AOT corresponding to the highlighted image block is determined by the following formula 15: 4 :

[0197]

[0198] Of course, the above is only one implementation method for fitting the target aerosol optical thickness corresponding to the highlighted image block, and other implementation methods are also applicable to this embodiment.

[0199] Corresponding to an aerosol optical thickness inversion method of the present application, the present application also provides an aerosol optical thickness inversion device.

[0200] like Figure 4 , shows a schematic diagram of the composition structure of the aerosol optical thickness inversion device provided by the present application. The device of this embodiment may include:

[0201] A bright block determination unit 501 is used to determine at least one bright image block belonging to a bright surface in a target remote sensing image to be processed;

[0202] The reflectivity simulation unit 502 is used to simulate the simulated surface reflectivity of each highlighted image block under multiple different aerosol optical thicknesses using an atmospheric radiation transfer model based on the pixel value of each pixel in the highlighted image block.

[0203] The library query unit 503 is used to determine, based on the latitude and longitude information corresponding to the highlighted image block, a target surface reflectance curve of a target highlighted surface block corresponding to the highlighted image block from a surface reflectance library of the highlighted surface, wherein the target surface reflectance curve is a curve fitted based on the average surface reflectance of each target highlighted surface block at multiple reference time points, wherein the average surface reflectance of the target highlighted surface block at the reference time point is the average surface reflectance of each pixel belonging to the target highlighted surface block in the benchmark remote sensing image collected at the reference time point;

[0204] A reflectivity query unit 504 is used to determine the reference surface reflectivity of the highlighted image block at the acquisition time point using the target surface reflectivity curve based on the acquisition time point corresponding to the highlighted image block;

[0205] A reflectivity matching unit 505 is used to determine at least one target simulated surface reflectivity that matches the reference surface reflectivity from the simulated surface reflectivities corresponding to the multiple different aerosol optical thicknesses;

[0206] The aerosol determination unit 506 is used to fit the target aerosol optical thickness corresponding to the highlighted image block based on the aerosol optical thickness corresponding to the at least one target simulated surface reflectivity.

[0207] In a possible implementation, the reflectivity matching unit includes:

[0208] A spectral angle calculation subunit, for calculating the spectral angle for each aerosol optical thickness based on the simulated surface reflectivity of the highlighted image block under the aerosol optical thickness and the reference surface reflectivity, to obtain the spectral angle corresponding to the aerosol optical thickness;

[0209] The spectral angle selection subunit is used to determine the smallest first spectral angle and the second and third spectral angles having the smallest angle difference with the first spectral angle from the spectral angles corresponding to a plurality of different aerosol optical thicknesses.

[0210] In yet another possible implementation, the aerosol determination unit includes:

[0211] A parameter calculation subunit is configured to calculate a parameter based on the first spectral angle α 1 The corresponding first aerosol optical thickness AOT 1 , the second spectral angle α 2 The corresponding second aerosol optical thickness AOT 2 and the third spectral angle α 3 The corresponding third aerosol optical thickness AOT 3 , the first thickness parameter a and the second thickness parameter b are fitted using the following formula:

[0212]

[0213] The thickness calculation subunit is used to determine the target aerosol optical thickness corresponding to the highlighted image block based on the first thickness parameter a and the second thickness parameter b by the following formula:

[0214]

[0215] Among them, AOT 4 is the target aerosol optical thickness corresponding to the highlighted image block.

[0216] In yet another possible implementation, the reflectivity simulation unit includes:

[0217] A mode determination subunit, configured to determine a target aerosol mode and a target atmosphere mode corresponding to the highlighted image block based on the latitude and longitude information corresponding to the highlighted image block;

[0218] The reflectivity simulation subunit is used to simulate the simulated surface reflectivity of the highlighted image block under multiple different aerosol optical thicknesses using an atmospheric radiation transfer model based on the target aerosol model, the target atmospheric model and the pixel value of each pixel in the highlighted image block.

[0219] In yet another possible implementation, the surface reflectance library of the highlighted surface includes a surface reflectance curve of at least one highlighted surface block;

[0220] The device also includes a curve construction unit, which is used to determine the surface reflectance curve of each highlighted surface block in the surface reflectance library in the following manner:

[0221] Obtaining an image time series, wherein the image time series includes reference remote sensing images collected at multiple reference time points, wherein the reference remote sensing images are remote sensing images that have been atmospherically corrected, and each pixel in the reference remote sensing images has its own surface reflectivity;

[0222] For each frame of the reference remote sensing image, removing pixels in the reference remote sensing image that are covered by clouds or snow, to obtain a processed reference remote sensing image;

[0223] For each processed reference remote sensing image, determining at least one reference image block belonging to at least one highlighted surface block in the reference remote sensing image, wherein each reference image block belongs to one highlighted surface block;

[0224] For each reference image block corresponding to each highlighted surface block in the benchmark remote sensing image, based on the surface reflectance of each pixel in the reference image block corresponding to the highlighted surface block, determine the average surface reflectance of the highlighted surface block at the reference time point corresponding to the benchmark remote sensing image;

[0225] For each highlighted surface block, a surface reflectivity curve of the highlighted surface block is fitted based on the average surface reflectivity of the highlighted surface block at the multiple reference time points.

[0226] In a possible implementation manner, when determining at least one reference image block belonging to at least one highlighted surface block in the reference remote sensing image, the curve construction unit is specifically configured to:

[0227] Determine at least one candidate reference image block belonging to a highlighted surface in the reference remote sensing image;

[0228] At least one reference image block having a ratio of valid pixels exceeding a set threshold is determined from the at least one candidate reference image block, each reference image block corresponds to a highlighted surface block, wherein the valid pixels are pixels having pixel values.

[0229] In yet another possible implementation, the device further includes:

[0230] a change rate determination unit, configured to determine, for each highlighted surface block, a change rate of the surface reflectivity corresponding to each unit time point in the surface reflectivity curve of the highlighted surface block after the curve construction unit fits the surface reflectivity curve of the highlighted surface block;

[0231] A change rate screening unit, used to determine a maximum change rate and a minimum change rate based on the change rate corresponding to each unit time point in the surface reflectivity curve;

[0232] a threshold determination unit, configured to determine a maximum change rate threshold based on the maximum change rate and the minimum change rate;

[0233] An abnormal point removal unit is used to remove the surface reflectivity corresponding to the target unit time point that meets the conditions in the surface reflectivity curve, wherein the change rate of the surface reflectivity corresponding to the target unit time point that meets the conditions exceeds the maximum change rate threshold, and the change rate of the surface reflectivity corresponding to two adjacent unit time points adjacent to the target unit time point does not exceed the maximum change rate threshold.

[0234] In yet another possible implementation, the device further includes:

[0235] A low-brightness block determination unit, used to determine at least one low-brightness image block belonging to a low-brightness surface in the target remote sensing image;

[0236] The low-brightness block inversion unit is used to invert the aerosol thickness of each low-brightness image block using the dark pixel method;

[0237] The atmospheric correction unit is used to perform atmospheric correction on the target remote sensing image based on the target aerosol thickness of each high-brightness image block and the aerosol thickness of each low-brightness image block.

[0238] The present application also provides an electronic device. Figure 5 , which shows a schematic diagram of a composition structure of the electronic device, the electronic device at least includes at least one processor 601, and a memory 602 connected to the processor 601;

[0239] The memory 602 is used to store computer programs;

[0240] The processor 601 is used to execute the computer program so that the electronic device can implement the aerosol optical thickness inversion method described in any one of the above embodiments.

[0241] It can be understood that the electronic device may further include a display unit 603 and an input unit 604 .

[0242] Of course, the electronic device may also have Figure 5 There may be more or fewer components, without limitation.

[0243] A computer program product is also provided in an embodiment of the present application, including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements any aerosol optical thickness inversion method provided in the embodiment of the present application.

[0244] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any aerosol optical thickness inversion method provided in the embodiment of the present application.

[0245] It should also be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0246] Through the description of the above implementation mode, the technicians in the field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a training device, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0247] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0248] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a training device, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, training device, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

Claims

1. An aerosol optical thickness inversion method, characterized in that: include: Determine at least one highlighted image block belonging to a highlighted surface in a target remote sensing image to be processed; For each highlighted image block, based on the pixel value of each pixel in the highlighted image block, the simulated surface reflectance of the highlighted image block under multiple different aerosol optical thicknesses is simulated using an atmospheric radiation transfer model; Based on the longitude and latitude information corresponding to the highlighted image block, a target surface reflectance curve of a target highlighted surface block corresponding to the highlighted image block is determined from a surface reflectance library of the highlighted surface, wherein the target surface reflectance curve is a curve fitted based on the average surface reflectance of each of the target highlighted surface blocks at multiple reference time points, wherein the average surface reflectance of the target highlighted surface block at the reference time point is the average surface reflectance of each pixel belonging to the target highlighted surface block in the benchmark remote sensing image collected at the reference time point; Based on the acquisition time point corresponding to the highlighted image block, the reference surface reflectance of the highlighted image block at the acquisition time point is determined using the target surface reflectance curve; Determining at least one target simulated surface reflectivity matching the reference surface reflectivity from the simulated surface reflectivities corresponding to the multiple different aerosol optical thicknesses; Based on the aerosol optical thickness corresponding to the at least one target simulated surface reflectivity, the target aerosol optical thickness corresponding to the highlighted image block is fitted.

2. The aerosol optical thickness inversion method according to claim 1, characterized in that: The step of determining at least one target simulated surface reflectivity that matches the reference surface reflectivity from the simulated surface reflectivities corresponding to the plurality of different aerosol optical thicknesses comprises: For each aerosol optical thickness, the spectral angle is calculated based on the simulated surface reflectance of the highlighted image block under the aerosol optical thickness and the reference surface reflectance to obtain the spectral angle corresponding to the aerosol optical thickness; From the spectral angles corresponding to a plurality of different aerosol optical thicknesses, a minimum first spectral angle and a second spectral angle and a third spectral angle having a minimum angular difference from the first spectral angle are determined.

3. The aerosol optical thickness inversion method according to claim 2, characterized in that: The step of fitting the target aerosol optical thickness corresponding to the highlighted image block based on the aerosol optical thickness corresponding to the at least one target simulated surface reflectivity comprises: Based on the first spectral angle The corresponding first aerosol optical depth , the second spectral angle The corresponding second aerosol optical depth And the third spectral angle The corresponding third aerosol optical depth , the first thickness parameter is fitted using the following formula and the second thickness parameter : ； Based on the first thickness parameter and the second thickness parameter , the target aerosol optical thickness corresponding to the highlighted image block is determined by the following formula: ； in, is the target aerosol optical thickness corresponding to the highlighted image block.

4. The aerosol optical thickness inversion method according to claim 3, characterized in that: Based on the pixel value of each pixel in the highlighted image block, the atmospheric radiation transfer model is used to simulate the simulated surface reflectance of the highlighted image block under multiple different aerosol optical thicknesses, including: Determining a target aerosol mode and a target atmosphere mode corresponding to the highlighted image block based on the latitude and longitude information corresponding to the highlighted image block; Based on the target aerosol model, the target atmospheric model and the pixel value of each pixel in the highlighted image block, the simulated surface reflectivity of the highlighted image block under multiple different aerosol optical thicknesses is simulated using an atmospheric radiation transfer model.

5. The aerosol optical thickness inversion method according to claim 1, characterized in that: The surface reflectance library of the highlighted surface includes a surface reflectance curve of at least one highlighted surface block; The surface reflectivity curve of the highlighted surface block is obtained by: Obtaining an image time series, wherein the image time series includes reference remote sensing images collected at multiple reference time points, wherein the reference remote sensing images are remote sensing images that have been atmospherically corrected, and each pixel in the reference remote sensing images has its own surface reflectivity; For each frame of the reference remote sensing image, removing pixels belonging to clouds or snow in the reference remote sensing image to obtain a processed reference remote sensing image; For each processed reference remote sensing image, determining at least one reference image block belonging to at least one highlighted surface block in the reference remote sensing image, wherein each reference image block belongs to one highlighted surface block; For each reference image block corresponding to each highlighted surface block in the benchmark remote sensing image, based on the surface reflectance of each pixel in the reference image block corresponding to the highlighted surface block, determine the average surface reflectance of the highlighted surface block at the reference time point corresponding to the benchmark remote sensing image; For each highlighted surface block, a surface reflectivity curve of the highlighted surface block is fitted based on the average surface reflectivity of the highlighted surface block at the multiple reference time points.

6. The aerosol optical thickness inversion method according to claim 5, characterized in that: The step of determining at least one reference image block belonging to at least one highlighted surface block in the reference remote sensing image comprises: Determine at least one candidate reference image block belonging to a highlighted surface in the reference remote sensing image; At least one reference image block having a ratio of valid pixels exceeding a set threshold is determined from the at least one candidate reference image block, each reference image block corresponds to a highlighted surface block, wherein the valid pixels are pixels having pixel values.

7. The aerosol optical thickness inversion method according to claim 5, characterized in that: After fitting the surface reflectance curve of the highlighted surface block, the method further includes: For each highlighted surface block, determining a change rate of the surface reflectivity corresponding to each unit time point in a surface reflectivity curve of the highlighted surface block; Determine a maximum change rate and a minimum change rate based on the change rate corresponding to each unit time point in the surface reflectivity curve; Determining a maximum change rate threshold based on the maximum change rate and the minimum change rate; The surface reflectivity corresponding to the target unit time point that meets the conditions is removed from the surface reflectivity curve, wherein the rate of change of the surface reflectivity corresponding to the target unit time point that meets the conditions exceeds the maximum rate of change threshold, and the rate of change of the surface reflectivity corresponding to two adjacent unit time points adjacent to the target unit time point does not exceed the maximum rate of change threshold.

8. The aerosol optical thickness inversion method according to claim 1, characterized in that: Also includes: Determine at least one low-brightness image block belonging to a low-brightness surface in the target remote sensing image; The aerosol thickness of each low-brightness image block is inverted using the dark pixel method; Based on the target aerosol thickness of each highlighted image block and the aerosol thickness of each low-brightness image block, atmospheric correction is performed on the target remote sensing image.

9. An aerosol optical thickness inversion device, characterized in that: include: A bright block determination unit, used to determine at least one bright image block belonging to a bright surface in a target remote sensing image to be processed; A reflectivity simulation unit, for simulating, for each highlighted image block, the simulated surface reflectivity of the highlighted image block under a plurality of different aerosol optical thicknesses using an atmospheric radiation transfer model based on the pixel value of each pixel in the highlighted image block; A library query unit is used to determine, based on the latitude and longitude information corresponding to the highlighted image block, a target surface reflectance curve of a target highlighted surface block corresponding to the highlighted image block from a surface reflectance library of the highlighted surface, wherein the target surface reflectance curve is a curve fitted based on the average surface reflectance of each target highlighted surface block at a plurality of reference time points, wherein the average surface reflectance of the target highlighted surface block at the reference time point is the average surface reflectance of each pixel belonging to the target highlighted surface block in the benchmark remote sensing image collected at the reference time point; A reflectivity query unit, configured to determine a reference surface reflectivity of the highlighted image block at the acquisition time point using the target surface reflectivity curve based on the acquisition time point corresponding to the highlighted image block; A reflectivity matching unit, configured to determine at least one target simulated surface reflectivity that matches the reference surface reflectivity from among the simulated surface reflectivities corresponding to the plurality of different aerosol optical thicknesses; The aerosol determination unit is used to fit the target aerosol optical thickness corresponding to the highlighted image block based on the aerosol optical thickness corresponding to the at least one target simulated surface reflectivity.

10. An electronic device, characterized in that: The method comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the electronic device can implement the aerosol optical thickness inversion method as described in any one of claims 1 to 8.

Images