Remote sensing time series image filtering methods, devices, equipment and storage media

Abstract

This invention discloses a method, apparatus, device, and storage medium for filtering remote sensing time-series images. The method includes acquiring remote sensing time-series image data and digital elevation data of a target area, extracting a topographic location index; reclassifying the topographic location index by calculating the standard deviation and combining it with actual topographic features to obtain the reclassification result; performing spatial location-based data matching to obtain the matching result; dividing the remote sensing image into noisy and non-noise regions; and selecting different convolution kernels to perform spatial convolution operations on the remote sensing time-series image data under different conditions to filter the data. This invention obtains the topographic location index through a series of calculations, distinguishes noise points in the image based on this index, and filters the remote sensing time-series image, solving the technical problems of noise pollution, low automation, and low image accuracy in current high-altitude time-series image processing.

Description

Technical Field

[0001] This invention relates to the field of remote sensing data technology, and in particular to a method, apparatus, device, and storage medium for filtering remote sensing time series images. Background Technology

[0002] High-altitude areas generally refer to regions above 1500 meters in altitude. Due to their complex terrain and harsh environment, high-altitude areas present significant challenges for remote sensing analysis. With the free availability of Landsat satellite data, its long time span, high resolution, and wide coverage have garnered considerable attention and research, making large-scale analysis using optical remote sensing time-series data possible. However, the long time span of Landsat satellite data, the variety of sensor types, and the significant influence of various factors on image formation from different sensors result in substantial noise and uncertainty in the time-series data. This limits its in-depth application, necessitating the use of multiple methods to reconstruct the time-series data to obtain high-quality datasets.

[0003] Currently, filtering methods widely used in remote sensing image denoising can be mainly divided into three categories: First, time-domain processing, including the LandTrendr (LT) algorithm, Savitzky-Golay (SG) algorithm, and BestIndex Slope Extraction (BISE). These methods analyze time-series data to identify and smooth noise in images, thereby improving image quality and the accuracy of information extraction. Second, spatial-domain processing, including Median Filter (MF) and Gaussian Filter (GF). These methods focus on the spatial relationships between adjacent pixels in the image, reducing outliers and noise by statistically adjusting pixel values. Third, frequency-domain processing, including Wavelet Filtering (WF) and Fourier Filtering (FF). These methods transform and filter in the frequency domain to control different frequency components, thereby effectively removing high-frequency noise from the image.

[0004] However, current time-series filtering methods are complex to operate, lack automation, and produce low-precision filtered images, thus having certain limitations. Therefore, how to provide a high-precision, simple, highly automated, and more scientifically sound method for filtering remote sensing time-series images in high-altitude areas is an urgent technical problem to be solved.

[0005] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main objective of this invention is to provide a method, apparatus, device, and storage medium for filtering remote sensing time-series images, aiming to solve the noise pollution problem existing in long-term time-series images in high-altitude areas.

[0007] To achieve the above objectives, the present invention provides a remote sensing time series image filtering method, comprising the following steps:

[0008] Acquire remote sensing time-series image data and digital elevation data of the target area; and extract the topographic location index from the digital elevation data;

[0009] By calculating the overall standard deviation of the topographic location index and combining it with the actual topographic conditions, the optimal threshold is selected to reclassify the topographic location index, thereby obtaining the topographic location index reclassification result that best reflects the actual situation.

[0010] Based on the reclassification results of the terrain location index and the remote sensing time series image data, perform spatial location-based data matching to obtain the data matching results;

[0011] Based on the data matching results, the image is divided into suspected noise areas and non-noise areas. The standard deviation of the image as a whole is calculated, and the presence of noise in the pixels is re-evaluated based on the data matching results.

[0012] Based on the image noise judgment results, the weight of each pixel in the convolution kernel used for pixels in noise regions and pixels in non-noise regions is calculated according to the distance between pixels in the neighborhood window, and the remote sensing time series image data is filtered.

[0013] Optionally, after acquiring remote sensing time-series image data and digital elevation data of the target area, the process may also include:

[0014] Data preprocessing operations are performed on the remote sensing time series image data; wherein, the data preprocessing operations include radiometric calibration, atmospheric correction and topographic correction.

[0015] Spatial interpolation is performed on the preprocessed remote sensing time-series image data and the digital elevation data; wherein, the spatial interpolation operation includes calculating the mean of the neighborhood of the missing data area using a preset range and filling in the missing values.

[0016] The terrain location index is calculated using a neighborhood window of a preset size on the digital elevation data.

[0017] Optionally, the step of reclassifying the terrain location index to obtain the terrain location index reclassification result specifically includes:

[0018] Calculate the standard deviation of the topographic location index of the target area, divide different areas according to the standard deviation and the actual topographic features, and obtain the topographic location index reclassification results.

[0019] Among them, the different regions divided according to the standard deviation and actual topography and landforms in the reclassification results of the topographic location index include ridge regions, valley regions and other regions excluding valleys and ridges.

[0020] Optionally, the step of performing spatial location-based data matching to obtain data matching results based on the topographic location index reclassification results and the remote sensing time series image data specifically includes: matching the number of rows and columns, resolution, and number of pixels of the remote sensing time series image data of the target area with the topographic location index reclassification results, and adjusting the number of rows and columns, the resolution, and the number of pixels to be the same.

[0021] Optionally, based on the data matching results, the image is divided into suspected noise regions and non-noise regions. The standard deviation of the entire image is calculated, and combined with the data matching results, the presence of noise in the pixels is reassessed. Specifically, this includes:

[0022] Based on the data matching results, pixels are divided into suspected noise areas and non-noise areas. When the topographic location index is other areas, pixels are classified as non-noise areas. When the topographic location index is a valley or ridge area, pixels are classified as noise areas, and the standard deviation of the entire image is calculated. Based on the calculated standard deviation of the image, suspected noise areas are further judged to ensure that the image contains only noise and non-noise areas.

[0023] Optionally, when the image is a noisy region, the center point weight is reduced to 0. The weight of each pixel in the convolution kernel is calculated based on the distance between pixels in the neighborhood. Combined with the convolution operation formula, a new pixel value is calculated, where the expression for the new pixel value is:

[0024]

[0025] In the formula: y is the new center point pixel value, and x1, x2, x3, x4, x6, x7, x8, and x9 are the original pixel values ​​at the corresponding positions in the 3×3 convolution kernel.

[0026] When the image is a non-noise region, a higher weight value is retained for the center point to preserve image details. Using the convolution operation formula, a new pixel value is calculated, where the expression for the new pixel value is:

[0027]

[0028] In the formula: y is the new center point pixel value, and x2, x4, x5, x6, and x8 are the original pixel values ​​at the corresponding positions in the 3×3 convolution kernel.

[0029] Optionally, based on the image noise judgment result, after calculating the weight of each pixel in the convolution kernel used for pixels belonging to noise regions and pixels belonging to non-noise regions according to the distance between pixels within the neighborhood window, and after performing a filtering step on the remote sensing time-series image data, the method further includes:

[0030] The accuracy of the spatial convolution operation results is evaluated, wherein the accuracy evaluation indicators include mean absolute error and peak signal-to-noise ratio.

[0031] Furthermore, to achieve the above objectives, the present invention also provides a remote sensing time series image filtering device, the device comprising:

[0032] The acquisition module is used to acquire remote sensing time-series image data and digital elevation data of the target area and extract topographic location index;

[0033] The reclassification module calculates the overall standard deviation of the terrain location index and, in combination with the actual terrain and landform conditions, selects the optimal threshold to reclassify the terrain location index, thereby obtaining the terrain location index reclassification result that best reflects the actual situation.

[0034] The matching module is used to perform spatial location-based data matching based on the terrain location index reclassification results and the remote sensing time series image data to obtain data matching results;

[0035] The judgment module, based on the data matching results, divides the image into suspected noise areas and non-noise areas, calculates the standard deviation of the entire image, and, in conjunction with the data matching results, makes a second judgment on whether there is noise in the pixel points.

[0036] The spatial convolution module, based on the image noise judgment result, calculates the weight of each pixel in the convolution kernel used for pixels in noisy regions and pixels in non-noisy regions according to the distance between pixels in the neighborhood window, and filters the remote sensing time-series image data.

[0037] In addition, to achieve the above objectives, the present invention also provides a remote sensing time series image filtering device, the remote sensing time series image filtering device comprising: a memory, a processor, and a remote sensing time series image filtering program stored in the memory and executable on the processor, wherein when the remote sensing time series image filtering program is executed by the processor, it implements the steps of the remote sensing time series image filtering method as described above.

[0038] In addition, to achieve the above objectives, the present invention also provides a storage medium storing a remote sensing time series image filtering program, which, when executed by a processor, implements the steps of the remote sensing time series image filtering method described above.

[0039] The beneficial effects of this invention are as follows: It proposes a method, apparatus, device, and storage medium for filtering remote sensing time-series images. The method includes acquiring remote sensing time-series image data and digital elevation data of a target area; extracting a topographic location index from the digital elevation data; calculating the standard deviation and combining it with actual topographic features to reclassify the topographic location index, obtaining a topographic location index reclassification result; performing spatial location-based data matching based on the topographic location index reclassification result and the remote sensing time-series image data, obtaining a data matching result; and dividing the image into noisy and non-noise regions based on the data matching result and the image standard deviation result. Different convolution kernels calculated based on neighborhood window distances are selected for different regions of the remote sensing time-series image data to perform spatial convolution operations, thereby filtering the remote sensing time-series image data. This invention, by extracting the topographic location index and combining it with a series of operations, and selecting different convolution kernels calculated based on pixel distances for different regions to filter remote sensing time-series images, solves the technical problems of noise pollution, low automation, and low image accuracy in current high-altitude time-series image processing. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention;

[0041] Figure 2 This is a flowchart illustrating the remote sensing time series image filtering method in an embodiment of the present invention;

[0042] Figure 3 This is a schematic diagram illustrating the principle of the remote sensing time series image filtering method in an embodiment of the present invention;

[0043] Figure 4 This is a result image of the filtered remote sensing time series image in an embodiment of the present invention;

[0044] Figure 5 This is a schematic diagram showing the original pixel value positions involved in the spatial convolution operation in an embodiment of the present invention;

[0045] Figure 6 This is a structural block diagram of a remote sensing time series image filtering device according to an embodiment of the present invention.

[0046] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] like Figure 1 As shown, Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention.

[0049] like Figure 1 As shown, the device may include: a processor 1001, such as a CPU; a communication bus 1002; a user interface 1003; a network interface 1004; and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0050] Those skilled in the art will understand that Figure 1 The structure of the device shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0051] like Figure 1 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a remote sensing time series image filtering program.

[0052] exist Figure 1 In the terminal shown, network interface 1004 is mainly used to connect to the backend server and communicate data with it; user interface 1003 is mainly used to connect to the client (user terminal) and communicate data with it; while processor 1001 can be used to call the remote sensing time series image filtering program stored in memory 1005 and perform the following operations:

[0053] The acquisition module is used to acquire remote sensing time-series image data and digital elevation data of the target area and extract topographic location index;

[0054] The reclassification module calculates the overall standard deviation of the terrain location index and, in combination with the actual terrain and landform conditions, selects the optimal threshold to reclassify the terrain location index, thereby obtaining the terrain location index reclassification result that best reflects the actual situation.

[0055] The matching module is used to perform spatial location-based data matching based on the terrain location index reclassification results and the remote sensing time series image data to obtain data matching results;

[0056] The judgment module, based on the data matching results, divides the image into suspected noise areas and non-noise areas, calculates the standard deviation of the entire image, and, in conjunction with the data matching results, makes a second judgment on whether there is noise in the pixel points.

[0057] The spatial convolution module, based on the image noise judgment result, calculates the weight of each pixel in the convolution kernel used for pixels in noisy regions and pixels in non-noisy regions according to the distance between pixels in the neighborhood window, and filters the remote sensing time-series image data.

[0058] The specific embodiments of the present invention applied to the device are basically the same as the embodiments of the application of remote sensing time series image filtering methods described below, and will not be repeated here.

[0059] This invention provides a method for filtering remote sensing time-series images, referring to... Figure 3 , Figure 3 This is a flowchart illustrating an embodiment of the remote sensing time series image filtering method of the present invention.

[0060] In this embodiment, the remote sensing time series image filtering method includes the following steps:

[0061] Step S100: Obtain remote sensing time series image data and digital elevation data of the target area, and extract the terrain location index from the digital elevation data.

[0062] Specifically, the first step is to acquire time-series optical remote sensing imagery and digital elevation data for the target area. As the name suggests, time-series data refers to long-term imagery with intervals between images. The time-series optical remote sensing imagery then undergoes preprocessing operations, including radiometric calibration, atmospheric correction, and topographic correction.

[0063] Following this, spatial interpolation is performed on the preprocessed optical remote sensing time-series image data and digital elevation model data; wherein, the spatial interpolation operation includes calculating the mean value of a certain range of neighborhoods for missing data regions and filling in the missing values.

[0064] In practical applications, taking the remote sensing time series image filtering of Shangri-La City as an example, when acquiring remote sensing data, the Google Earth engine can be used to filter three high-quality Landsat remote sensing images from each year from 1987 to 2017 using the strip number. The images are concentrated in January to March, with cloud cover less than 20% and a spatial resolution of 30 meters.

[0065] The usage of Landsat remote sensing image data for the target area is shown in the table below:

[0066]

[0067] In the preprocessing of remote sensing images, GEE was first used to coordinate different types of sensors from different years to address the varying spectral, spatial, and radiometric resolutions among the Landsat series sensors. Then, cloud removal was performed on the images. Since the study area involved three images, high-quality image sets selected from each year were combined using a median algorithm to create a comprehensive image covering Shangri-La City. Finally, the images from each year were merged to form a time-series dataset with yearly intervals.

[0068] In this embodiment, after acquiring remote sensing time-series images and digital elevation data, it is also necessary to perform interpolation operations on the remote sensing time-series images and digital elevation data.

[0069] Specifically, because remote sensing time series images span a long period of time, data gaps may occur. To ensure the integrity of the images, spatial interpolation is used to restore the missing data to a certain extent by utilizing information from surrounding neighboring pixels, thereby mitigating the impact of data gaps on image interpretation and analysis.

[0070] In practical applications, by spatially interpolating Landsat time series data from 1987 to 2017 and filling in missing data with the mean value of pixels within a 21×21 neighborhood window, the integrity of remote sensing time series data can be greatly improved.

[0071] Use a 3×3 rectangular neighborhood window to extract the terrain location index from the digital elevation data.

[0072] Step S200: By calculating the overall standard deviation of the terrain location index and combining it with the actual terrain and landform conditions, the optimal threshold is selected to reclassify the terrain location index, thereby obtaining the terrain location index reclassification result that best reflects the actual situation.

[0073] Specifically, the standard deviation of the topographic location index is first calculated, and different regions are divided based on the standard deviation of the topographic location index and the actual topographic features.

[0074]

[0075] The specific classification criteria are shown in the table below:

[0076] Note: SD is the standard deviation of the TPI calculation result, and slope represents the absolute slope.

[0077] In practical applications, the acquired terrain location indices are reclassified in ArcGIS 10.7 and divided into three categories: ridges, valleys, and areas excluding valleys and ridges. Valleys are assigned a value of 1, areas excluding valleys and ridges are assigned a value of 2, and ridges are assigned a value of 3.

[0078] Step S300: Based on the terrain location index reclassification result and the remote sensing time series image data, perform spatial location-based data matching to obtain the data matching result.

[0079] Specifically, in this embodiment, spatial location matching is achieved based on the fact that the size of the remote sensing time series image is the same as the size of the reclassified terrain location index.

[0080] In practical applications, the acquired Landsat remote sensing time-series imagery has a spatial resolution of 30 meters and a row and column count of 7349×3425, with each image containing a total of 25,170,325 pixels. The reclassified topographic location index also has a spatial resolution of 30 meters and a row and column count of 7349×3425, totaling 25,170,325 pixels. Ultimately, spatial matching between the reclassified topographic location index and the remote sensing time-series imagery is achieved.

[0081] Step S400: Based on the data matching results, the image is divided into suspected noise regions and non-noise regions. The standard deviation of the entire image is calculated, and combined with the data matching results, the presence of noise in the pixels is reassessed.

[0082] Specifically, in time-series data acquired at high altitudes during early spring, the primary noise source is likely snow, which is typically prominent in valleys and ridges. Weighted convolution operations, by adjusting the weights, can selectively suppress noise signals, thereby improving image quality and clarity.

[0083] In this embodiment, based on the reclassified topographic location index and the matching results of remote sensing time-series images, the image is divided into suspected noise point regions and non-noise point regions. When the topographic location index belongs to a valley or ridge area, it is more susceptible to snow impact compared to other areas due to its altitude. In this embodiment, it is initially considered a suspected noise point region. Since some valley and ridge areas are not subject to noise pollution, a secondary judgment is made on the suspected noise point regions by calculating the standard deviation of the entire image. When the pixel value is an outlier, it is classified as a noise region; when the pixel value is not an outlier, it is classified as a non-noise region. When the topographic location index belongs to other areas outside of valleys and ridges, this embodiment considers them as non-noise pixels.

[0084] Step S500: Based on the image noise judgment result, calculate the weight of each pixel in the convolution kernel used for pixels in noise regions and pixels in non-noise regions according to the distance between pixels in the neighborhood window, and filter the remote sensing time-series image data.

[0085] Specifically, when the image is a noisy region, the weight of the center point is reduced to 0. The weight of each pixel in the convolution kernel is calculated based on the distance between pixels in the neighborhood. Combining this with the convolution operation formula, a new pixel value is calculated, where the expression for the new pixel value is:

[0086]

[0087] In the formula: y is the new center point pixel value, and x1, x2, x3, x4, x6, x7, x8, and x9 are the original pixel values ​​at the corresponding positions in the 3×3 convolution kernel. The original pixel positions are as follows: Figure 5 As shown.

[0088] When the image is a non-noise region, a higher weight value is retained for the center point to preserve image details. Using the convolution operation formula, a new pixel value is calculated, where the expression for the new pixel value is:

[0089]

[0090] In the formula: y is the new center point pixel value, and x2, x4, x5, x6, and x8 are the original pixel values ​​at the corresponding positions in the 3×3 convolution kernel. The original pixel positions are as follows: Figure 5 As shown.

[0091] After filtering and reconstructing the remote sensing time series images, the mean absolute error and peak signal-to-noise ratio are used to evaluate the image quality. The expression for the image quality evaluation index is as follows:

[0092]

[0093]

[0094]

[0095] MAX f =[f (0,0) :f (W-1,H-1) ]

[0096] In the formula: y i y is the value of the filtered image. j is the value of the image before filtering, n is the number of samples, f is the image after filtering, o is the image before filtering, (x,y) is the pixel position, W is the image width, and H is the image height.

[0097] Specifically, the above filtering method is used in Python to filter the time series imagery of Shangri-La from 1987 to 2017, such as... Figure 3 As shown in the table below, the accuracy of the filtering results is evaluated, and the specific calculation results of the mean absolute error are as follows:

[0098]

[0099] The specific calculation results of the peak signal-to-noise ratio are shown in the table below:

[0100]

[0101] In this embodiment, as Figure 3 As shown, a remote sensing time-series image filtering method is provided. This method acquires remote sensing time-series image data and digital elevation data (DEM) data of the target area, extracts a topographic location index from the DEM data of the target area, calculates the standard deviation of the topographic location index, and reclassifies it based on the actual topographic features. According to the topographic location index classification results, it is matched with the remote sensing time-series image data according to spatial location. Based on the data matching results, the image is divided into suspected noise areas and non-noise areas. The standard deviation of the image as a whole is calculated, and combined with the data matching results, the presence of noise at each pixel is re-evaluated. Based on the image noise judgment results, the weight of each pixel in the convolution kernel used to classify pixels as noise or non-noise areas is calculated according to the distance between pixels within the neighborhood window, and the remote sensing time-series image data is then filtered. This method solves the technical problems of noise pollution, low automation, and low accuracy of filtered images in current high-altitude time-series images.

[0102] Reference Figure 6 , Figure 6 This is a structural block diagram of an embodiment of the remote sensing time series image filtering device of the present invention.

[0103] like Figure 6 As shown, the remote sensing time series image filtering device proposed in this embodiment of the invention includes:

[0104] The acquisition module is used to acquire remote sensing time-series image data and digital elevation data of the target area and extract topographic location index;

[0105] The reclassification module calculates the overall standard deviation of the terrain location index and, in combination with the actual terrain and landform conditions, selects the optimal threshold to reclassify the terrain location index, thereby obtaining the terrain location index reclassification result that best reflects the actual situation.

[0106] The matching module is used to perform spatial location-based data matching based on the terrain location index reclassification results and the remote sensing time series image data to obtain data matching results;

[0107] The judgment module, based on the data matching results, divides the image into suspected noise areas and non-noise areas, calculates the standard deviation of the entire image, and, in conjunction with the data matching results, makes a second judgment on whether there is noise in the pixel points.

[0108] The spatial convolution module, based on the image noise judgment result, calculates the weight of each pixel in the convolution kernel used for pixels in noisy regions and pixels in non-noisy regions according to the distance between pixels in the neighborhood window, and filters the remote sensing time-series image data.

[0109] Other embodiments or specific implementations of the remote sensing time series image filtering device of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0110] Furthermore, the present invention also proposes a remote sensing time series image filtering device, which includes: a memory, a processor, and a remote sensing time series image filtering program stored in the memory and executable on the processor. When the remote sensing time series image filtering program is executed by the processor, it implements the steps of the remote sensing time series image filtering method as described above.

[0111] The specific implementation of the remote sensing time series image filtering device in this application is basically the same as the embodiments of the remote sensing time series image filtering method described above, and will not be repeated here.

[0112] Furthermore, this invention also proposes a readable storage medium, which includes a computer-readable storage medium storing a remote sensing time-series image filtering program thereon. The readable storage medium may be... Figure 2The memory 1005 in the terminal can also be at least one of ROM (Read-Only Memory) / RAM (Random Access Memory), magnetic disk, optical disk, etc. The readable storage medium includes several instructions to cause a remote sensing time series image filtering device with a processor to execute the remote sensing time series image filtering method described in various embodiments of the present invention.

[0113] The specific implementation methods in the readable storage medium of this application are basically the same as those in the embodiments of the remote sensing time series image filtering method described above, and will not be repeated here.

[0114] It is understood that in the description of this specification, references to terms such as "one embodiment," "another embodiment," "other embodiments," or "first embodiment to Nth embodiment," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0115] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0116] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0117] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0118] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A remote sensing time series image filtering method, characterized in that, Includes the following steps: Acquire remote sensing time-series image data and digital elevation data of the target area; and extract the topographic location index from the digital elevation data; By calculating the overall standard deviation of the topographic location index and considering the actual topographic features, the optimal threshold is selected to reclassify the topographic location index, resulting in a reclassification result that best reflects the actual situation; specifically including: Calculate the standard deviation of the topographic location index of the target area, and divide different areas according to the standard deviation and the actual topographic features to obtain the best reclassification result of the topographic location index. Among them, the different regions divided according to the standard deviation and topographic features in the reclassification results of the terrain location index include ridge regions, valley regions and other regions excluding valleys and ridges; Based on the reclassification results of the terrain location index and the remote sensing time series image data, perform spatial location-based data matching to obtain the data matching results; Based on the data matching results, the image is divided into suspected noise regions and non-noise regions. The standard deviation of the entire image is calculated, and combined with the data matching results, the presence of noise in the pixels is reassessed. Specifically, this includes: Based on the data matching results, the pixels are divided into suspected noise areas and non-noise areas; When the topographic location index is for other regions, the pixels are classified as non-noise regions; when the topographic location index is for valleys and ridges, the pixels are classified as noise regions and the standard deviation is calculated for the entire image. Based on the calculation results of the image standard deviation, the suspected noise areas are further judged to ensure that the image contains only noise and non-noise areas; Based on the image noise assessment results, the weight of each pixel in the convolution kernel used to distinguish between noise and non-noise regions is calculated according to the distance between pixels within the neighborhood window, and the remote sensing time-series image data is then filtered; specifically including: When the image is a noisy region, the weight of the center point is reduced to 0. The weight of each pixel in the convolution kernel is calculated based on the distance between pixels in the neighborhood. Combining this with the convolution operation formula, a new pixel value is calculated, where the expression for the new pixel value is: 、 In the formula: For the new center point pixel value, for The original pixel value at the corresponding position in the convolution kernel; When the image is a non-noise region, a higher weight value is retained for the center point to preserve image details. Using the convolution operation formula, a new pixel value is calculated, where the expression for the new pixel value is: 、 In the formula: For the new center point pixel value, for The original pixel value at the corresponding position in the convolution kernel.

2. The remote sensing time series image filtering method as described in claim 1, characterized in that, After acquiring remote sensing time-series image data and digital elevation data of the target area and extracting the topographic location index from the digital elevation data, the process further includes: Data preprocessing operations are performed on the remote sensing time series image data; wherein, the data preprocessing operations include radiometric calibration, atmospheric correction and topographic correction. Spatial interpolation is performed on the preprocessed remote sensing time-series image data and the digital elevation data; wherein, the spatial interpolation operation includes calculating the mean of the neighborhood of the missing data area using a preset range and filling in the missing values; The terrain location index is calculated using a neighborhood window of a preset size on the digital elevation data.

3. The remote sensing time series image filtering method as described in claim 1, characterized in that, The steps of performing spatial location-based data matching to obtain data matching results based on the topographic location index reclassification results and the remote sensing time series image data specifically include: matching the number of rows and columns, resolution, and number of pixels of the remote sensing time series image data of the target area with the topographic location index reclassification results, and adjusting the number of rows and columns, resolution, and number of pixels to be the same.

4. The remote sensing time series image filtering method as described in claim 1, characterized in that, Based on the image noise judgment result, the weight of each pixel in the convolution kernel used to distinguish between noise and non-noise regions is calculated according to the distance between pixels within the neighborhood window. The remote sensing time-series image data is then filtered. Following this filtering step, the method further includes: The accuracy of the spatial convolution operation results is evaluated, wherein the accuracy evaluation indicators include mean absolute error and peak signal-to-noise ratio.

5. A remote sensing time-series image filtering device, characterized in that, The apparatus for the remote sensing time series image filtering method as described in any one of claims 1-4, comprising: The acquisition module is used to acquire remote sensing time-series image data and digital elevation data of the target area and extract topographic location index; The reclassification module calculates the overall standard deviation of the terrain location index and, in combination with the actual terrain and landform conditions, selects the optimal threshold to reclassify the terrain location index, thereby obtaining the terrain location index reclassification result that best reflects the actual situation. The matching module is used to perform spatial location-based data matching based on the terrain location index reclassification results and the remote sensing time series image data to obtain data matching results; The judgment module, based on the data matching results, divides the image into suspected noise areas and non-noise areas, calculates the standard deviation of the entire image, and, in conjunction with the data matching results, makes a second judgment on whether there is noise in the pixel points. The spatial convolution module, based on the image noise judgment result, calculates the weight of each pixel in the convolution kernel used for pixels in noisy regions and pixels in non-noisy regions according to the distance between pixels in the neighborhood window, and filters the remote sensing time-series image data.

6. A remote sensing time series image filtering device, characterized in that, The remote sensing time series image filtering device includes: a memory, a processor, and a remote sensing time series image filtering program stored in the memory and executable on the processor. When the remote sensing time series image filtering program is executed by the processor, it implements the steps of the remote sensing time series image filtering method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium stores a remote sensing time series image filtering program, which, when executed by a processor, implements the steps of the remote sensing time series image filtering method as described in any one of claims 1 to 4.

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