Ground feature information extraction method based on hyperspectral and high-resolution remote sensing image

Abstract

The invention discloses a surface feature information extraction method based on a hyperspectral and high-resolution remote sensing image, and relates to the technical field of remote sensing. The method comprises the following steps: acquiring a hyperspectral and high-resolution remote sensing image, and preprocessing the hyperspectral and high-resolution remote sensing image; cutting and fusing the preprocessed image to obtain a fused image; performing spectral analysis and wave band screening on the fused image to obtain first image data; performing image enhancement processing on the first image data to obtain a plurality of grayscale images; performing wave band synthesis on the plurality of grayscale images, and performing grid analysis and wave band screening to obtain second image data; performing threshold segmentation on the second image data, and performing grid surface turning to obtain a first vector; obtaining DEM data in a preset elevation range of the research area, and performing grid surface turning to obtain a second vector; and cutting the first vector according to the second vector to obtain a distribution vector result. According to the method provided by the embodiment of the invention, the extraction precision and reliability can be effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of remote sensing images, and in particular to a method for extracting ground object information based on hyperspectral and high-resolution remote sensing images. Background Art

[0002] As an important economic crop, peaches are among the top fruits in my country in terms of planting area and yield. The traditional way to obtain spatial distribution information of peach planting areas requires manual field surveys, which consumes a lot of manpower, material resources and financial resources. Remote sensing technology is an important means of obtaining ground information at present, with fast data updates, wide monitoring range, low cost and rich information. The continuous development of remote sensing technology has provided technical possibilities for quickly and accurately obtaining large-scale fruit tree planting information. The use of remote sensing technology to extract peach planting areas is of great significance for peach planting planning and yield prediction.

[0003] Hyperspectral remote sensing is a new type of remote sensing technology that achieves fine classification and identification of objects by obtaining spectral information of objects in a continuous spectral range. Compared with traditional multispectral remote sensing images, hyperspectral remote sensing images have higher spectral resolution and richer object information, and can provide more accurate and comprehensive data support for peach tree extraction. High-resolution images can clearly display the detailed information of objects. Using high-resolution images, the location characteristics and texture characteristics of peach trees can be extracted more accurately. Fusion of hyperspectral images and high-resolution images can improve the accuracy and reliability of peach tree extraction and reduce the interference of other objects on the extraction of peach planting areas.

[0004] At present, there are few studies on remote sensing extraction of peach trees, and conventional remote sensing image extraction mostly uses classification methods, including supervised classification, unsupervised classification, etc. The supervised classification method requires a large number of training samples, defines different categories based on the characteristics of the training samples, and then classifies each pixel. This method requires multiple manual interventions and has a high time cost. At the same time, it is necessary to ensure the representativeness, integrity and quality of the training samples, otherwise errors may be introduced. Unsupervised classification does not require pre-selection of training samples, but classifies according to the similarity between pixels, but the classification accuracy is relatively low, and it is more sensitive to noise and outliers, and is prone to misclassification. Therefore, there is an urgent need for a fast and effective technical solution to solve the above problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method, electronic device and storage medium for extracting ground object information based on hyperspectral and high-resolution remote sensing images, which can improve the extraction accuracy, efficiency and reliability.

[0006] In a first aspect, a method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to an embodiment of the present invention comprises the following steps:

[0007] Acquire a hyperspectral remote sensing image and a high-resolution remote sensing image of the study area, and preprocess the hyperspectral remote sensing image and the high-resolution remote sensing image;

[0008] Cropping and fusing the preprocessed hyperspectral remote sensing image and the high-resolution remote sensing image to obtain a fused image;

[0009] Performing spectral analysis on the fused image, and performing band screening on the fused image according to the analysis result to obtain single-band first image data;

[0010] Performing multiple image enhancement processes on the first image data, and obtaining a corresponding grayscale image after each image enhancement process;

[0011] Performing band synthesis on the plurality of grayscale images to obtain a multi-band image;

[0012] Performing raster analysis and band screening on the multi-band image to obtain single-band second image data;

[0013] Segmenting the second image data into a grid result of the target object, and performing grid-to-surface conversion on the grid result to obtain a first vector;

[0014] Obtaining DEM data within a preset elevation range of the study area, and performing raster-to-surface conversion on the DEM data to obtain a second vector;

[0015] The first vector is clipped according to the second vector to obtain a distribution vector result of the target object in the study area.

[0016] According to some embodiments of the present invention, the preprocessing of the hyperspectral remote sensing image and the high-resolution remote sensing image includes:

[0017] Performing at least one of band combination, radiation calibration, atmospheric correction and geometric correction on the hyperspectral remote sensing image;

[0018] At least one of radiation calibration, atmospheric correction, image fusion and geometric correction is performed on the high-resolution remote sensing image.

[0019] According to some embodiments of the present invention, the step of cropping and fusing the preprocessed hyperspectral remote sensing image and the high-resolution remote sensing image to obtain a fused image includes:

[0020] According to the boundary vector of the research area, the preprocessed hyperspectral remote sensing image and the high-resolution remote sensing image are cropped;

[0021] All the bands of the cropped hyperspectral remote sensing image and the preset bands of the high-resolution remote sensing image are fused to obtain a fused image.

[0022] According to some embodiments of the present invention, the step of performing spectral analysis on the fused image and performing band screening on the fused image according to the analysis result to obtain single-band first image data includes:

[0023] Acquire spectral information of target objects and spectral information of non-target objects on the fused image, and draw spectral curves respectively;

[0024] According to the spectral curve, a single band having the largest difference in spectral reflectance between the target object and the non-target object is selected as the first image data.

[0025] According to some embodiments of the present invention, the step of performing multiple image enhancement processes on the first image data and obtaining a corresponding grayscale image after each image enhancement process includes:

[0026] The first image data is subjected to multiple image enhancement processing by using at least two of convolution, mean filtering, focus analysis, dilation, erosion, opening operation and closing operation, and a corresponding grayscale image is obtained after each image enhancement processing.

[0027] According to some embodiments of the present invention, the step of performing raster analysis and band screening on the multi-band image to obtain single-band second image data includes:

[0028] Analyzing pixel value distribution patterns of target objects and non-target objects on the multi-band image;

[0029] The grayscale image with the highest separation degree between the target object and the non-target object is selected according to a preset rule as the second image data.

[0030] According to some embodiments of the present invention, the preset rules include:

[0031] The pixel value distribution intervals of the target object and the non-target object do not intersect;

[0032] The minimum pixel value of the target object is smaller than the minimum pixel value of the non-target object, or the maximum pixel value of the target object is larger than the maximum pixel value of the non-target object.

[0033] According to some embodiments of the present invention, the method further comprises:

[0034] Performing spot deletion and smoothing processing on the distribution vector result;

[0035] The distribution vector result after smoothing is corrected.

[0036] In a second aspect, an electronic device according to an embodiment of the present invention includes:

[0037] A memory for storing program instructions;

[0038] The processor is used to call the program instructions stored in the memory, and execute the method for extracting ground object information based on hyperspectral and high-resolution remote sensing images described in the embodiment of the first aspect according to the obtained program instructions.

[0039] In a third aspect, according to a computer-readable storage medium of an embodiment of the present invention, the storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method for extracting ground object information based on hyperspectral and high-resolution remote sensing images described in the embodiment of the first aspect.

[0040] According to the method for extracting ground object information based on hyperspectral and high-resolution remote sensing images, electronic equipment and storage medium of the embodiments of the present invention, there are at least the following beneficial effects: the extraction of a large range of peach planting areas is realized by using hyperspectral remote sensing images and high-resolution remote sensing images, the monitoring range is wide and the workload of manual field investigation is reduced, an automated extraction process is established, and the monitoring efficiency is improved. The spectral information of hyperspectral data is rich, and the ground object texture and structural information of high-resolution images are clear. The image after the fusion of the two has not only high spatial resolution, but also rich spectral information. Extracting peach planting areas based on the fused images can effectively improve the accuracy and reliability of peach extraction, reduce the interference of other ground objects on the extraction of peach planting areas, and provide more accurate and efficient support for application fields such as peach planting management and peach yield estimation.

[0041] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0043] Figure 1 A flowchart of the steps of a method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of a process of extracting ground object information based on hyperspectral and high-resolution remote sensing images according to an embodiment of the present invention;

[0045] Figure 3 is a false color display of the fused image of an embodiment of the present invention;

[0046] Figure 4 is a distribution diagram of pixel values ​​of peach trees and non-peach trees according to an embodiment of the present invention;

[0047] Figure 5 This is a corrosion image processing effect diagram of an embodiment of the present invention;

[0048] Figure 6 is a preliminary extraction vector diagram of a peach tree according to an embodiment of the present invention;

[0049] Figure 7 is a DEM image of the study area of ​​the embodiment of the present invention;

[0050] Figure 8 This is the final vector diagram of the peach tree in the embodiment of the present invention. DETAILED DESCRIPTION

[0051] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limitations on the present application. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0052] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0053] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present invention and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0054] Reference to "embodiments" in the present invention means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0055] On the one hand, the embodiment of the present invention proposes a method for extracting ground object information based on hyperspectral and high-resolution remote sensing images, such as Figure 1 and Figure 2 As shown, the method includes:

[0056] Step S100: obtaining a hyperspectral remote sensing image and a high-resolution remote sensing image of a study area, and preprocessing the hyperspectral remote sensing image and the high-resolution remote sensing image;

[0057] Step S200: cropping and fusing the pre-processed hyperspectral remote sensing image and the high-resolution remote sensing image to obtain a fused image;

[0058] Step S300: performing spectral analysis on the fused image, and performing band screening on the fused image according to the analysis result to obtain single-band first image data;

[0059] Step S400: performing multiple image enhancement processes on the first image data, and obtaining a corresponding grayscale image after each image enhancement process;

[0060] Step S500: synthesizing the multiple grayscale images to obtain a multi-band image;

[0061] Step S600: performing raster analysis and band screening on the multi-band image to obtain single-band second image data;

[0062] Step S700: segmenting the second image data into a grid result of the target object, and performing grid-to-surface conversion on the grid result to obtain a first vector;

[0063] Step S800: obtaining DEM data within a preset elevation range of the study area, and performing raster-to-surface conversion on the DEM data to obtain a second vector;

[0064] Step S900: According to the second vector, the first vector is clipped to obtain the distribution vector result of the target object in the study area.

[0065] Specifically, hyperspectral remote sensing is a new type of remote sensing technology that achieves fine classification and identification of objects by acquiring spectral information of objects in a continuous spectral range. Compared with traditional multispectral remote sensing images, hyperspectral remote sensing images have higher spectral resolution and richer object information, and can provide more accurate and comprehensive data support for the extraction of peach trees (or other fruit trees / planting areas). High-resolution images can clearly display the detailed information of objects. Using high-resolution images, the location characteristics and texture characteristics of peach trees can be more accurately extracted. Fusion of hyperspectral images and high-resolution images can improve the accuracy and reliability of peach tree extraction and reduce the interference of other objects on the extraction of peach planting areas.

[0066] First, obtain the hyperspectral remote sensing images and high-resolution remote sensing images of the study area, and preprocess the hyperspectral remote sensing images and high-resolution remote sensing images. The preprocessing can be band combination, radiation calibration, atmospheric correction and geometric correction for the hyperspectral remote sensing images, and radiation calibration, atmospheric correction, image fusion and geometric correction for the high-resolution remote sensing images. Preprocessing the hyperspectral remote sensing images and high-resolution remote sensing images facilitates subsequent image processing and analysis.

[0067] Then, according to the boundary vector of the study area, the preprocessed hyperspectral remote sensing image and the high-resolution remote sensing image are cropped; all bands of the cropped hyperspectral remote sensing image are fused with the preset bands of the high-resolution remote sensing image to obtain a fused image. In this example, the preset band of the high-resolution remote sensing image is the near-infrared band of the high-resolution remote sensing image, that is, all bands of the cropped hyperspectral remote sensing image are fused with the near-infrared band of the high-resolution remote sensing image to obtain a fused image.

[0068] After obtaining the fused image, the spectral information of the target object and the non-target object on the fused image are obtained, and the spectral curves are drawn respectively; according to the spectral curve, the single band with the largest difference in spectral reflectance between the target object and the non-target object is screened out as the first image data. Assuming that the target object refers to the peach tree planting area, the spectral information of the peach tree and the non-peach tree objects on the fused image is collected, and the spectral curve is drawn. Then, according to the spectral curve law of the peach tree and the non-peach tree, the single band with the largest difference in spectral reflectance between the peach tree and the non-peach tree objects is screened out as the basic image data for extracting the peach tree.

[0069] After obtaining the first image data, the first image data can be subjected to multiple image enhancement processes, and the corresponding grayscale image is obtained after each image enhancement process. For example, the first image data is processed by image enhancement methods such as convolution, mean filtering, focus analysis, dilate, erosion, open operation, and close operation to obtain multiple grayscale images. Then, the multiple grayscale images are synthesized by bands to obtain a multi-band image; and the multi-band image is subjected to raster analysis and band screening to obtain a single-band second image data. Among them, raster analysis and band screening of the multi-band image refers to: analyzing the pixel value distribution law of the target object and the non-target object on the multi-band image; and screening the grayscale image with the highest separation degree between the target object and the non-target object according to the preset rules as the second image data.

[0070] Assuming that the target object is a peach tree planting area, the pixel value distribution pattern of peach trees and non-peach tree objects is analyzed based on the multi-band image, and rules are set to filter out the grayscale image with the highest separation between peach trees and non-peach tree objects as the image data for further extraction of peach trees. The preset rules can be set as:

[0071] (1) The pixel value distribution intervals of the target object (peach tree) and the non-target object (non-peach tree) do not intersect;

[0072] (2) The minimum pixel value of the target object (peach tree) is smaller than the minimum pixel value of the non-target object (non-peach tree), or the maximum pixel value of the target object is larger than the maximum pixel value of the non-target object.

[0073] After obtaining the single-band second image data, the second image data is segmented into a raster result of the target object, and the raster result is converted from raster to surface to obtain a first vector. In this example, the second image data can be segmented by a threshold segmentation method. Specifically, a threshold range is set based on the second image data, and the threshold range is determined according to the maximum pixel value of the target object on the second image data. If the minimum pixel value of the target object is a and the maximum pixel value is b, the threshold range is set to [a, b], and all pixels within the threshold range in the second image data are extracted, and the raster result of the peach planting area is segmented. The raster result of the peach planting area is converted from raster to surface to obtain a preliminary extraction vector of peach trees, that is, the first vector.

[0074] The DEM (Digital Elevation Model) data within the preset elevation range of the study area is obtained, and the DEM data is converted from raster to surface to obtain the second vector. Assuming that the target feature is a peach planting area, since peach trees are mostly planted in plain areas, the area containing the peach planting area is set as the study area. The study area is usually also a plain area. The DEM data of the study area is spatially analyzed, and the pixels within the specified elevation range are extracted to obtain the DEM data within the specified elevation range. The DEM data within the specified elevation range is then converted from raster to surface to obtain the plain area range vector, i.e., the second vector.

[0075] According to the second vector, the first vector is clipped to obtain the distribution vector result of the target object in the research area. Specifically, the second vector is used as the clipping element and the first vector is used as the input element to clip the distribution vector result of the peach planting area within the plain.

[0076] Furthermore, in some embodiments of the present invention, the method for extracting ground object information based on hyperspectral and high-resolution remote sensing images further includes the following steps:

[0077] Perform spot deletion and smoothing on the distribution vector results;

[0078] Correct the distribution vector result after smoothing.

[0079] Specifically, the distribution vector results are subjected to small spot deletion and smoothing. The small spot deletion operation is achieved by calculating the spot area and removing the spots whose area is smaller than the set range value; the smoothing process is to process the sharp corners in the contour of the distribution vector results to improve the vector quality. Finally, the distribution vector results are manually corrected, the wrong spots are deleted, and the missed spots are outlined to obtain the distribution vector of the peach planting area.

[0080] It should be noted that the method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to the embodiment of the present invention is not only applicable to the extraction of peach planting areas, but also applicable to the extraction of other fruit trees / plants / other ground objects.

[0081] According to the method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to the embodiment of the present invention, the extraction of a large range of peach planting areas is realized by using hyperspectral remote sensing images and high-resolution remote sensing images, the monitoring range is wide and the workload of manual field investigation is reduced, an automated extraction process is established, and the monitoring efficiency is improved. The spectral information of hyperspectral data is rich, and the ground object texture and structural information of high-resolution images are clear. The image after the fusion of the two has not only high spatial resolution, but also rich spectral information. Extracting peach planting areas based on the fused images can effectively improve the accuracy and reliability of peach extraction, reduce the interference of other ground objects on the extraction of peach planting areas, and provide more accurate and efficient support for application fields such as peach planting management and peach yield estimation.

[0082] The following is a specific example to illustrate the method for extracting ground object information based on hyperspectral and high-resolution remote sensing images of the present invention. It should be noted that the following example is only an exemplary description and not a specific limitation of the present invention.

[0083] Step 1: The hyperspectral remote sensing images use a certain hyperspectral satellite data with a resolution of 10 meters (abbreviated as: OHS), and the high-resolution remote sensing images use a certain high-resolution satellite data with a resolution better than 1 meter (abbreviated as: GF-2). The OHS and GF-2 data are preprocessed respectively.

[0084] Step 2: After determining the study area, use the boundary vector of the study area to clip the preprocessed OHS and GF-2 data, and then fuse the clipped data. In this example, the Gram-Schmidt Pan Sharpening (GS) fusion algorithm is used to perform the best match of the bands involved in the fusion through statistical analysis, avoiding the problem of excessive concentration of information in certain bands and maintaining the consistency of the image spectral information before and after fusion. It is a high-fidelity remote sensing image fusion method. After preprocessing, the OHS data has 32 bands, and the GF-2 image has four bands: red, green, blue, and near-infrared. The near-infrared band of GF-2 is selected and fused with the 32 bands of OHS using the GS fusion algorithm. The number of image bands after fusion is consistent with the number of OHS bands (32 bands), and the resolution is consistent with the GF-2 resolution (better than 1 meter). The fused image has both the spectral information of the OHS data and the spatial resolution of the GF-2 image. Its false color display effect is as follows: Figure 3 shown.

[0085] Step 3: Based on the fused image, the spectral information of peach trees and non-peach trees is collected and the spectral curve is drawn. By analyzing the spectral curve rules of peach trees and non-peach tree objects, the 26th band with the largest difference in spectral reflectance between peach trees and non-peach tree objects is selected as the basic image data for extracting peach trees.

[0086] Step 4: Image enhancement is to convert the image into a form that is more suitable for human eyes or machines to interpret and analyze. Band 26 is processed using image enhancement methods such as convolution, mean filtering, focus analysis, dilation, erosion, opening, and closing.

[0087] Convolution is to divide the whole pixel into blocks and average them, which is used to change the spatial frequency of the image to enhance the image. It can be regarded as a weighted summation process. Each pixel in the image area is multiplied by each element of the convolution kernel, and the sum of all products is used as the new value of the center pixel of the area. Mean filtering uses the neighborhood averaging method, which replaces the grayscale of each pixel with the average of the grayscale of several pixels. Focus analysis takes the sum, maximum value, minimum value, mean, median or standard deviation of all pixels in the neighborhood as the value of the new pixel to achieve the purpose of image enhancement.

[0088] Dilation is the process of merging all background points that are in contact with an object into the object to expand the boundary outward. It is used to fill certain holes in the target area and eliminate small particle noise contained in the target area. The formula is as follows:

[0089]

[0090] Erosion is a process of eliminating boundary points and shrinking the boundary inward. It can be used to eliminate small and meaningless targets. The formula is as follows:

[0091]

[0092] The closing operation is to dilate the image first and then corrode it for filling. The formula is as follows:

[0093]

[0094] The opening operation is to perform corrosion operation first and then expansion operation to remove isolated small points and eliminate noise. The formula is as follows:

[0095]

[0096] Where A is the input image, B is the structural element, and (x, y) is the coordinate of the pixel.

[0097] Step 5: Band 26 undergoes a series of image enhancement processes to obtain 10 grayscale images. These 10 grayscale images are combined with bands 3, 7, 14, and 26 (or other visible light or near-infrared bands) of the fused image to collect pixel values ​​of peach tree and non-peach tree feature sample points and draw a pixel value distribution curve (e.g. Figure 4), according to the band screening rules, the image after corrosion processing is selected as the image for peach tree extraction ( Figure 5 ).

[0098] The filter rules are set as:

[0099] (1) The distribution interval of the peach tree pixel values ​​in the image does not intersect with the distribution interval of the non-peach tree pixel values;

[0100] (2) The minimum (maximum) value of the peach tree pixel in this image is greater than (less than) the maximum (minimum) value of the non-peach tree pixel.

[0101] Step 6: Set the image pixel values ​​selected in step 5 between 2100 and 3500 as peach trees, remove the pixels outside this threshold, and obtain the raster result of the peach planting area. Use the "raster to surface" tool to convert the raster result of the peach planting area into a vector to obtain the preliminary extraction vector of the peach planting area ( Figure 6 ).

[0102] Step 7: The DEM data of the study area uses the ASTER GDEM digital elevation model, which is downloaded and resampled according to the boundary of the study area ( Figure 7 ). Use the "Extract by Attribute Tool" to extract pixels with elevation values ​​less than or equal to 130, and obtain the DEM data of the study area with elevation values ​​not exceeding 130.

[0103] Step 8: Use the "Raster to Polygon" tool to convert the DEM data with elevation values ​​not exceeding 130 obtained in step 7 into vector data to obtain the plain range vector of the study area.

[0104] Step 9: Use the plain range vector obtained in step 8 as the clipping element and the preliminary extraction vector obtained in step 6 as the input element to clip and obtain the peach tree distribution vector result within the plain range.

[0105] Step 10: Delete small spots and smooth the vector results within the plain area. Delete spots with an area less than 100 square meters to achieve small spot deletion; use the PAEK algorithm and set the smoothing tolerance to 5 meters to smooth the vectors.

[0106] Step 11: Manually modify the smoothed vector, delete the wrong spots, outline the missed spots, and obtain the final peach planting area distribution vector ( Figure 8 ).

[0107] Steps 6 to 11 can be implemented in ArcGIS software.

[0108] On the other hand, an embodiment of the present invention further provides an electronic device, including:

[0109] A memory for storing program instructions;

[0110] The processor is used to call the program instructions stored in the memory and execute the method for extracting ground object information based on hyperspectral and high-resolution remote sensing images described in the above-mentioned embodiment according to the obtained program instructions.

[0111] On the other hand, an embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned method for extracting ground object information based on hyperspectral and high-resolution remote sensing images is implemented.

[0112] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are implemented to be located in one place, or may also be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0113] Although specific embodiments are described herein, those of ordinary skill in the art will recognize that many other modifications or alternative embodiments are also within the scope of the present disclosure. For example, any of the functions and / or processing capabilities described in conjunction with a particular device or component may be performed by any other device or component. In addition, although various exemplary implementations and architectures have been described according to embodiments of the present disclosure, those of ordinary skill in the art will recognize that many other modifications to the exemplary implementations and architectures described herein are also within the scope of the present disclosure.

[0114] Some aspects of the present disclosure are described above with reference to the block diagram and flow chart of the system, method, system and / or computer program product according to the exemplary embodiments.It should be understood that the combination of one or more blocks in the block diagram and the flow chart and the blocks in the block diagram and the flow chart can be realized by executing computer executable program instructions respectively.Equally, according to some embodiments, some blocks in the block diagram and the flow chart may not need to be executed in the order shown, or may not need to be executed in full.In addition, additional components and / or operations beyond those components and / or operations shown in the blocks in the block diagram and the flow chart may be present in certain embodiments.

[0115] Therefore, the blocks in the block diagrams and flow charts support the combination of devices for performing the specified functions, the combination of elements or steps for performing the specified functions, and the program instruction devices for performing the specified functions. It should also be understood that each block in the block diagrams and flow charts and the combination of blocks in the block diagrams and flow charts can be implemented by a special-purpose hardware computer system or a combination of special-purpose hardware and computer instructions that performs a specific function, element or step.

[0116] The program modules, applications, etc. described herein may include one or more software components, including, for example, software objects, methods, data structures, etc. Each such software component may include computer-executable instructions that, in response to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the illustrative methods described herein) to be performed.

[0117] Software components can be encoded with any of various programming languages. An exemplary programming language can be a low-level programming language, such as an assembly language associated with a specific hardware architecture and / or an operating system platform. The software component comprising assembly language instructions may need to be converted to executable machine code by an assembler before being executed by hardware architecture and / or platform. Another exemplary programming language can be a higher-level programming language, which can be transplanted across multiple architectures. The software component comprising a higher-level programming language may need to be converted to an intermediate representation by an interpreter or a compiler before execution. Other examples of programming languages ​​include but are not limited to macro languages, shells or command languages, job control languages, scripting languages, database queries or search languages ​​or report writing languages. In one or more exemplary embodiments, the software component comprising the instruction of one of the above-mentioned programming language examples can be directly executed by an operating system or other software components, without first being converted into another form.

[0118] Software components may be stored as files or other data storage structures. Software components of similar type or related functions may be stored together, such as in a specific directory, folder, or library. Software components may be static (e.g., preset or fixed) or dynamic (e.g., created or modified at execution time).

[0119] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A method for extracting ground object information based on hyperspectral and high-resolution remote sensing images, characterized in that: The following steps are involved: Acquire a hyperspectral remote sensing image and a high-resolution remote sensing image of the study area, and preprocess the hyperspectral remote sensing image and the high-resolution remote sensing image; Cropping and fusing the preprocessed hyperspectral remote sensing image and the high-resolution remote sensing image to obtain a fused image; Performing spectral analysis on the fused image, and performing band screening on the fused image according to the analysis result to obtain single-band first image data; Performing multiple image enhancement processes on the first image data, and obtaining a corresponding grayscale image after each image enhancement process; Performing band synthesis on the plurality of grayscale images to obtain a multi-band image; Performing raster analysis and band screening on the multi-band image to obtain single-band second image data; Segmenting the second image data into a grid result of the target object, and performing grid-to-surface conversion on the grid result to obtain a first vector; Obtaining DEM data within a preset elevation range of the study area, and performing raster-to-surface conversion on the DEM data to obtain a second vector; The first vector is clipped according to the second vector to obtain a distribution vector result of the target object in the study area.

2. The method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to claim 1, characterized in that: The preprocessing of the hyperspectral remote sensing image and the high-resolution remote sensing image comprises: Performing at least one of band combination, radiation calibration, atmospheric correction and geometric correction on the hyperspectral remote sensing image; At least one of radiation calibration, atmospheric correction, image fusion and geometric correction is performed on the high-resolution remote sensing image.

3. The method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to claim 1, characterized in that: The step of clipping and fusing the preprocessed hyperspectral remote sensing image and the high-resolution remote sensing image to obtain a fused image comprises: According to the boundary vector of the research area, the preprocessed hyperspectral remote sensing image and the high-resolution remote sensing image are cropped; All the bands of the cropped hyperspectral remote sensing image and the preset bands of the high-resolution remote sensing image are fused to obtain a fused image.

4. The method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to claim 1, characterized in that: The step of performing spectral analysis on the fused image and performing band screening on the fused image according to the analysis result to obtain single-band first image data includes: Acquire spectral information of target objects and spectral information of non-target objects on the fused image, and draw spectral curves respectively; According to the spectral curve, a single band having the largest difference in spectral reflectance between the target object and the non-target object is selected as the first image data.

5. The method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to claim 1, characterized in that: The step of performing multiple image enhancement processes on the first image data and obtaining a corresponding grayscale image after each image enhancement process comprises: The first image data is subjected to multiple image enhancement processing by using at least two of convolution, mean filtering, focus analysis, dilation, erosion, opening operation and closing operation, and a corresponding grayscale image is obtained after each image enhancement processing.

6. The method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to claim 1, characterized in that: The step of performing raster analysis and band screening on the multi-band image to obtain single-band second image data includes: Analyzing pixel value distribution patterns of target objects and non-target objects on the multi-band image; The grayscale image with the highest separation degree between the target object and the non-target object is selected according to a preset rule as the second image data.

7. The method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to claim 6, characterized in that: The preset rules include: The pixel value distribution intervals of the target object and the non-target object do not intersect; The minimum pixel value of the target object is smaller than the minimum pixel value of the non-target object, or the maximum pixel value of the target object is larger than the maximum pixel value of the non-target object.

8. The method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to claim 1, characterized in that: The method further comprises: Performing spot deletion and smoothing processing on the distribution vector result; The distribution vector result after smoothing is corrected.

9. An electronic device, characterized in that: include: A memory for storing program instructions; A processor is used to call the program instructions stored in the memory, and execute the method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to any one of claims 1-8 according to the obtained program instructions.

10. A storage medium, characterized in that: The storage medium stores computer executable instructions, and the computer executable instructions are used to enable a computer to execute the method for extracting ground object information based on hyperspectral and high-resolution remote sensing images according to any one of claims 1 to 8.

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