A method, apparatus, medium, and device for reprojecting multi-slice remote sensing data

Abstract

This invention discloses a method, apparatus, medium, and device for reprojecting multi-slice remote sensing data, relating to the field of remote sensing image processing. The method includes acquiring multiple slices of a first remote sensing data point for a reference time period; performing a transformation operation on the position information of the multiple slices in a grid to obtain the reprojected positions of the multiple slices in a target coordinate system; wherein the transformation operation includes stitching, reprojection, and resampling operations; constructing a position correspondence matrix based on the position information of the multiple slices before the transformation operation and their reprojected positions in the target coordinate system; and determining the corresponding position information of the reprojected positions of slices from other time periods in the second remote sensing data using a lookup function based on the constructed position correspondence matrix, thereby obtaining the data values ​​of each slice from other time periods; wherein the second remote sensing data is the original remote sensing data acquired from other time periods.

Description

Technical Field

[0001] This invention relates to the field of remote sensing image processing technology, and in particular to a method, apparatus, medium and device for reprojecting multi-slice remote sensing data. Background Technology

[0002] With the development of remote sensing technology and the expansion of its application fields, more and more scientific research or commercial applications in disciplines such as meteorology, hydrology, ecology, and geography require reprojection processing of large amounts of remote sensing data.

[0003] Traditional reprojection techniques typically perform calculations for each scene of remote sensing imagery separately, especially for resampling methods that require altering the original pixel values ​​(such as bilinear interpolation and cubic spline interpolation). This scene-by-scene processing strategy is necessary in these cases. However, traditional reprojection methods fail to effectively utilize the commonalities between data points, and the scene-by-scene processing leads to a large amount of redundant calculations, severely wasting computational resources. This is particularly problematic when dealing with multi-temporal and multi-slice remote sensing data. Furthermore, while the nearest neighbor method, as a resampling method that preserves the original pixel values, has advantages such as computational simplicity and fast processing speed, its scene-by-scene reprojection approach also wastes computation time in large-scale data processing, making it difficult to meet the requirements of real-time remote sensing data processing. This results in the inability to obtain the location information of large batches of reprojected remote sensing data within the original data in real time, and consequently, the difficulty in quickly obtaining the corresponding data values. Summary of the Invention

[0004] This invention provides a method, apparatus, medium, and device for reprojecting multi-slice remote sensing data to solve the aforementioned problems in the prior art, namely, how to quickly obtain the location information of a large batch of reprojected remote sensing data within the original data, thereby obtaining the corresponding data values. This invention provides a method for reprojecting multi-slice remote sensing data, which includes:

[0005] Obtain multiple slices of the first remote sensing data for the baseline time period;

[0006] The position information of multiple slices in the grid is transformed to obtain the reprojected positions of the multiple slices in the target coordinate system; wherein, the transformation operation includes stitching, reprojection and resampling operations;

[0007] Based on the position information of multiple slices before the transformation operation and their reprojection positions in the target coordinate system, a position correspondence matrix is ​​constructed.

[0008] Based on the established location correspondence matrix, the reprojection location of other time period slices in the second remote sensing data is determined by a lookup function, thereby obtaining the data value of each slice in other time periods; wherein, the second remote sensing data is the original remote sensing data acquired in other time periods.

[0009] Optionally, the location information specifically includes:

[0010] Slice index layer, row number layer, and column number layer.

[0011] Optionally, the transformation operation on the position information of multiple slices in the grid to obtain the reprojection positions of the multiple slices in the target coordinate system specifically includes:

[0012] M reproj =ReprojectAndResample(Merge({I i | i ∈ S}), CS orig →CS target , res orig →res target );

[0013] Among them, I i Let S be the index or row / column number of the i-th slice in the layer data, S={1,2,...,n} be the slice set, Merge be the concatenation function, and CS be the concatenation function. orig The coordinate system of the original first remote sensing data, CS target Let res be the target coordinate system. orig For the original resolution, res target For the target resolution, M reproj This is the slice index layer after the conversion operation.

[0014] Optionally, the step of determining the location information in the second remote sensing data corresponding to the reprojection location of other time period slices through a lookup function based on the constructed location correspondence matrix specifically includes:

[0015] The reprojection position of each slice is obtained using the following formula:

[0016] D output,t,i′,j′,k′ =D input,t,i,j,k ;

[0017] in,

[0018] (i,j,k)=Lookup(T, (i′,j′,k′));

[0019] Among them, D output,t,i′,j′,k′ Data D in the target coordinate system output,t The reprojection position of each slice in Dinput,t,i,j,k For the original remote sensing data D input,t The corresponding position information (i,j,k) is given in the matrix, where Lookup is the lookup function and T is the position correspondence matrix.

[0020] The lookup function takes any reprojection location as input and inputs it into the location correspondence matrix T to determine its corresponding grid location information in the original remote sensing data.

[0021] This invention provides a multi-slice remote sensing data reprojection device, comprising:

[0022] The acquisition module is used to acquire multiple slices of the first remote sensing data for the reference time period;

[0023] The coordinate transformation module is used to transform the position information of multiple slices in the grid to obtain the reprojected positions of the multiple slices in the target coordinate system; wherein, the transformation operation includes stitching, reprojection and resampling operations;

[0024] The position correspondence matrix construction module is used to construct a position correspondence matrix based on the position information of multiple slices before the transformation operation and their reprojection positions in the target coordinate system;

[0025] The determination module is used to determine the location information corresponding to the reprojection position of the slices in other time periods in the second remote sensing data by using a lookup function based on the constructed location correspondence matrix, and to obtain the data value of each slice in other time periods; wherein, the second remote sensing data is the original remote sensing data acquired in other time periods.

[0026] The present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-slice remote sensing data reprojection method.

[0027] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described multi-slice remote sensing data reprojection method.

[0028] Compared to existing technologies, the beneficial effects of this invention are as follows: This invention provides a multi-slice remote sensing data reprojection method. This method introduces a position correspondence matrix to establish a mapping relationship between pixel positions in different projection coordinate systems. This allows for rapid reprojection without repeated coordinate transformations during subsequent data reprojection; the mapped position can be obtained simply through matrix lookup. Simultaneously, a precise mapping relationship is established between the original and output data through the position correspondence matrix T. For each output position, the corresponding original data position can be found in matrix T using a lookup function, ensuring that data from different time phases and slices can be accurately reprojected to their corresponding positions in the same target coordinate system. This precise registration ensures complete spatial alignment of the reprojected data, enabling rapid acquisition of corresponding data values ​​and guaranteeing spatial consistency. Furthermore, the position correspondence matrix constructed in this invention is universal and not limited by the type of projection transformation. Attached Figure Description

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0030] Figure 1 A flowchart of a multi-slice remote sensing data reprojection method provided in an embodiment of the present invention;

[0031] Figure 2 A technical roadmap for a multi-slice remote sensing data reprojection method provided in this embodiment of the invention;

[0032] Figure 3 This is a schematic diagram of a computer device for a multi-slice remote sensing data reprojection method provided in an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0034] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0035] like Figure 1and Figure 2 As shown in the figure, this embodiment illustrates a multi-slice remote sensing data reprojection method, including:

[0036] S1: Obtain multiple slices of the first remote sensing data for the baseline time period.

[0037] For example, each slice is gridded (e.g., 1200×1200 grids with a resolution of 1 km constitute a slice file) and has a uniform resolution and coordinate system; this dataset is represented by a three-dimensional array D, where Dt,i,j,k represents the cell value located in the j-th row and k-th column of the t-th time phase (e.g., day number) and the i-th slice. Here, t, i, j, and k are the indices of the time phase, slice, row, and column, respectively.

[0038] S2: Perform a transformation operation on the position information of multiple slices in the grid to obtain the reprojected positions of multiple slices in the target coordinate system; wherein, the transformation operation includes stitching, reprojection and resampling operations.

[0039] Optionally, the location information specifically includes: a slice index layer, a row number layer, and a column number layer.

[0040] For example, when constructing a slice index layer, data from any time period (such as any day) can be selected as a reference (called the input file), denoted as D. base,i,j,k , where i, j, and k are the slice, row, and column indices, respectively. An index layer I is created for each slice. i Each grid cell's value is a triplet (i,j,k), representing the cell's slice number, row number, and column number in the original data, which can be represented as:

[0041] I i (j,k)=(i,j,k);

[0042] For example, when reprojecting and resampling the slice index layer, let the coordinate system of the original data be CS. orig The target coordinate system is CS target Perform reprojection and resampling operations on the slice index layer Is, transferring it from CS. orig Transform to CS target and change the resolution from the original resolution res orig Resampled to new resolution res target The reprojected and resampled slice index layer is denoted as M. reproj This process can be represented as:

[0043] M reproj =ReprojectAndResample(Merge({I i| i ∈ S}), CS orig →CS target , res orig →res target );

[0044] Among them, I i Let S be the index or row / column number of the i-th slice in the layer data, S={1,2,...,n} be the slice set, Merge be the concatenation function, and CS be the concatenation function. orig CS is the coordinate system of the original data. target Let res be the target coordinate system. orig For the original resolution, res target For the target resolution, ReprojectAndResample is the algorithm for implementing reprojection and resampling.

[0045] It should be noted that there is no need to give a specific reprojection calculation method here, and this embodiment is not limited to a specific reprojection type.

[0046] S3: Construct a position correspondence matrix based on the position information of multiple slices before the transformation operation and their reprojection positions in the target coordinate system.

[0047] For example, for any grid (i′,j′,k′) in the output data, since the nearest neighbor resampling method is used, this grid value must correspond to the slice index layer I. s A specific value in the record, which records I s The position information (i,j,k) of a specific grid in the output data can be used to correlate the position of the grid (i′,j′,k′) with its position in the original slice index layer I. s The position information (i,j,k) in the data is mapped to a position correspondence matrix T. Finally, by traversing all grids in the output data, a complete position correspondence matrix T is constructed, i.e., Ti. i′,j′,k′ = (i,j,k).

[0048] S4: Based on the constructed location correspondence matrix, the reprojection location of other time period slices in the second remote sensing data is determined by a lookup function to obtain the data value of each slice in other time periods; wherein, the second remote sensing data is the original remote sensing data obtained in other time periods.

[0049] For example, based on the constructed location correspondence matrix, a lookup function can be used to determine the location information in the second remote sensing data corresponding to the reprojection location of slices from other time periods. Specifically, this includes:

[0050] D output,t,i′,j′,k′ =D input,t,i,j,k ;

[0051] in,

[0052] (i,j,k)=Lookup(T, (i′,j′,k′));

[0053] Among them, D output,t,i′,j′,k′ The data in the target coordinate system (i.e., the output data) D output,t The reprojection position of each slice in D input,t,i,j,k For the raw remote sensing data (i.e., input data) D input,t The corresponding position information (i,j,k) is given in the matrix, where Lookup is the lookup function and T is the position correspondence matrix.

[0054] The lookup function takes the location information of any grid in the output data as input, substitutes it into the location correspondence matrix T, determines the grid position corresponding to it in the original remote sensing data, and then obtains the data value of each slice.

[0055] The following example, using the batch reprojection of MODIS snow cover data from a certain region, illustrates the solution of this invention: The input data is MODIS daily snow cover data product with a resolution of 500 meters. The region is covered by 8 slices daily, and each slice file has 2400 rows and 2400 columns. The 8 slice files of the region are reprojected daily, that is, transformed from the original MODIS Sinusoidal projection coordinate system to the Albers projection coordinate system of the same resolution (central meridian 105 degrees, first standard latitude 25 degrees, second standard latitude 47 degrees), and merged into one file.

[0056] Using traditional methods, such as the MODIS Reprojection Tool, this operation takes 12 minutes; in contrast, after applying the method proposed in this invention for batch reprojection, it only takes 6 seconds. This means that if 10 years of daily snow cover products from MODIS dual satellites need to be reprojected and resampled, the traditional method would take approximately 60 days (365*10*2*12 / 24 / 60), while the method proposed in this invention would take approximately 12 hours (365*10*2*6 / 3600), or half a day. The comparison shows that the method proposed in this invention significantly improves the efficiency of batch remote sensing data reprojection compared to traditional methods.

[0057] The above describes one or more embodiments of a multi-slice remote sensing data reprojection method provided in this specification. Based on the same idea, this specification also provides a corresponding multi-slice remote sensing data reprojection device, including:

[0058] The acquisition module is used to acquire multiple slices of the first remote sensing data for the reference time period;

[0059] The coordinate transformation module is used to transform the position information of multiple slices in the grid to obtain the reprojected positions of the multiple slices in the target coordinate system; wherein, the transformation operation includes stitching, reprojection and resampling operations;

[0060] The position correspondence matrix construction module is used to construct a position correspondence matrix based on the position information of multiple slices before the transformation operation and their reprojection positions in the target coordinate system;

[0061] The determination module is used to determine the location information corresponding to the reprojection position of the slices in other time periods in the second remote sensing data by using a lookup function based on the constructed location correspondence matrix, and to obtain the data value of each slice in other time periods; wherein, the second remote sensing data is the original remote sensing data acquired in other time periods.

[0062] Specific limitations regarding the multi-slice remote sensing data reprojection device can be found in the limitations of the multi-slice remote sensing data reprojection method described above, and will not be repeated here. Each module in the aforementioned multi-slice remote sensing data reprojection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0063] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the multi-slice remote sensing data reprojection method described above.

[0064] The present invention also provides Figure 3 The schematic diagram of the computer device shown is as follows: Figure 3 As shown, at the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the multi-slice remote sensing data reprojection method provided in the above embodiments.

[0065] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this invention.

Claims

1. A method for reprojecting multi-slice remote sensing data, characterized in that, include: Obtain multiple slices of the first remote sensing data for the baseline time period; The position information of multiple slices in the grid is transformed to obtain the reprojected positions of the multiple slices in the target coordinate system; wherein, the transformation operation includes stitching, reprojection and resampling operations; The transformation operation on the position information of multiple slices in the grid to obtain the reprojection positions of multiple slices in the target coordinate system specifically includes: ; Among them, I i Let S be the index or row / column number of the i-th slice in the layer data, S={1,2,...,n} be the slice set, Merge be the concatenation function, and CS be the concatenation function. orig The coordinate system of the original first remote sensing data, CS target Let res be the target coordinate system. orig For the original resolution, res target For the target resolution, M reproj This is the slice index layer after the conversion operation; Based on the position information of multiple slices before the transformation operation and their reprojection positions in the target coordinate system, a position correspondence matrix is ​​constructed. Based on the established location correspondence matrix, the reprojection location of other time period slices in the second remote sensing data is determined by a lookup function, thereby obtaining the data value of each slice in other time periods; wherein, the second remote sensing data is the original remote sensing data acquired in other time periods; The step of determining the location information in the second remote sensing data corresponding to the reprojection location of other time period slices by using a lookup function based on the constructed location correspondence matrix specifically includes: The reprojection position of each slice is obtained using the following formula: ； in, ； Among them, D output,t,i′,j′,k′ Data D in the target coordinate system output,t The reprojection position of each slice in D input,t,i,j,k For the original remote sensing data D input,t The corresponding position information (i,j,k) is given in the matrix, where Lookup is the lookup function and T is the position correspondence matrix. The lookup function takes any reprojection location as input, substitutes it into the location correspondence matrix T, and determines its corresponding grid location information in the original remote sensing data. The location information specifically includes: Slice index layer, row number layer, and column number layer.

2. A multi-slice remote sensing data reprojection device, characterized in that, include: The acquisition module is used to acquire multiple slices of the first remote sensing data for the reference time period; The coordinate transformation module is used to transform the position information of multiple slices in the grid to obtain the reprojected positions of the multiple slices in the target coordinate system. The transformation operation includes stitching, reprojection, and resampling operations. Specifically, the transformation operation to obtain the reprojected positions of the multiple slices in the target coordinate system includes: ; Among them, I i Let S be the index or row / column number of the i-th slice in the layer data, S={1,2,...,n} be the slice set, Merge be the concatenation function, and CS be the concatenation function. orig The coordinate system of the original first remote sensing data, CS target Let res be the target coordinate system. orig For the original resolution, res target For the target resolution, M reproj This is the slice index layer after the conversion operation; The position correspondence matrix construction module is used to construct a position correspondence matrix based on the position information of multiple slices before the transformation operation and their reprojection positions in the target coordinate system; The determination module is used to determine the location information in the second remote sensing data corresponding to the reprojection positions of slices from other time periods in the second remote sensing data by using a lookup function based on a pre-constructed location correspondence matrix, thereby obtaining the data value of each slice in other time periods; wherein, the second remote sensing data is the original remote sensing data acquired in other time periods; the step of determining the location information in the second remote sensing data corresponding to the reprojection positions of slices from other time periods in the second remote sensing data by using a lookup function based on the pre-constructed location correspondence matrix specifically includes: The reprojection position of each slice is obtained using the following formula: ； in, ； Among them, D output,t,i′,j′,k′ Data D in the target coordinate system output,t The reprojection position of each slice in D input,t,i,j,k For the original remote sensing data D input,t The corresponding position information (i,j,k) is given in the matrix, where Lookup is the lookup function and T is the position correspondence matrix. The lookup function takes any reprojection location as input and inputs it into the location correspondence matrix T to determine its corresponding grid location information in the original remote sensing data; the location information specifically includes: a tile index layer, a row number layer, and a column number layer.

3. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the multi-slice remote sensing data reprojection method of claim 1.

4. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-slice remote sensing data reprojection method of claim 1.

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