An image preprocessing method based on multi-source data

Abstract

This invention belongs to the field of mission planning technology for precision-guided aircraft, specifically relating to an image preprocessing method based on multi-source data. The method includes: expanding the application of image preprocessing to multi-source data through multi-source data fusion; optimizing projection coordinate transformation to support resampling at different resolutions; and employing a discrete pixel value reconstruction method to reassign overly discrete pixel values. Given the high real-time requirements of hypersonic precision-guided aircraft in wartime, this invention provides a complete preprocessing workflow for peacetime image preprocessing compared to existing technologies. The fusion of multi-source data improves the accuracy of image preprocessing results. The discrete pixel value reconstruction method filters out pixel data with obvious discrete characteristics, making the preprocessing results more consistent with actual terrain features. More reasonable image results provide stronger support in wartime and can effectively improve the real-time efficiency of hypersonic precision-guided aircraft operations.

Description

Technical Field

[0001] This invention belongs to the field of mission planning technology for precision-guided aircraft, and specifically relates to an image preprocessing method based on multi-source data. Background Technology

[0002] With the continuous development of technology, modern warfare is characterized by real-time and high-efficiency information-based operations. To meet the demands of war for real-time performance and efficiency, some aspects of hypersonic precision-guided weapon operations can be conducted during peacetime. SAR image preprocessing is a crucial part of this peacetime work. The main purpose of image preprocessing is to eliminate irrelevant information from images, restore useful and accurate information, enhance the detectability of relevant information, and simplify data to the maximum extent possible. This, in turn, accelerates the speed at which hypersonic precision-guided weapon information systems can utilize images and respond more quickly.

[0003] Therefore, SAR image preprocessing is an important means for hypersonic precision-guided aircraft to improve battlefield response speed. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] The technical problem to be solved by this invention is: how to provide an image preprocessing method based on multi-source data, which uses the fusion analysis technology of DEM elevation map, DOMR image map and sea surface mask map to realize the analysis of SAR image preprocessing results, so as to meet the needs of hypersonic aircraft matching error positioning.

[0006] (II) Technical Solution

[0007] To address the aforementioned technical problems, this invention provides an image preprocessing method based on multi-source data, the image preprocessing method comprising the following steps:

[0008] Step 1: Specify the target range to be processed;

[0009] Step 2: Based on the target range specified in Step 1, divide the target range into sub-rectangular tasks;

[0010] Step 3: Distribute the sub-rectangle tasks based on the sub-rectangle tasks divided in Step 2; distribute the divided sub-rectangle tasks to the blade servers using parallel methods;

[0011] Step 4: The blade processor receives the sub-rectangle task, sends the image preprocessing parameters, and begins the image preprocessing calculation process;

[0012] Step 5: Based on the image preprocessing parameters issued in Step 4, including the elevation map, image map, and description image map range and corner coordinate information parameters, perform projection coordinate transformation on the DEM elevation map and DOMR image map;

[0013] Step 6: Based on the image preprocessing parameters issued in Step 4, including the elevation map, mask image, and parameters describing the mask image size, corner coordinates, and sea level information, the sea surface portion of the DEM elevation map is marked using the sea surface mask image. Elevations within the sea surface coverage area are uniformly assigned a value of -777. Figure 5 As shown;

[0014] Step 7: Based on the results of Step 6, and combining the DEM elevation map, DOMR image map, real-time map, and baseline map parameters, perform grayscale analysis, resolution analysis, filtering and denoising, and feature value extraction in sequence, and output the analyzed results. The specific calculation process is as follows: Figure 6 As shown;

[0015] Step 8: Based on the results generated in Step 7, perform projected coordinate transformation;

[0016] Step 9: Based on the data processed in Step 8, perform discrete pixel value reconstruction, and reassign values ​​to overly obvious discrete pixels according to the specified neighborhood value rules;

[0017] Step 10: Based on step 9, the results of the sub-range tasks are fused. The results of all blade machine calculations are sent back to the server for fusion processing through parallel means, and finally fused into the result data of the target range to be processed.

[0018] In step 1, the target range to be processed is specified;

[0019] The specified area is a rectangle defined by the top-left and bottom-right corners, and the rectangle's area falls within the range of the DEM elevation map, DOMR image map, and sea surface mask map. Figure 1 As shown.

[0020] In step 2, the target range is divided according to the target range specified in step 1 to form sub-rectangular tasks.

[0021] When segmenting the target area, sub-rectangles are cut according to a specified length L0 and width W0. To ensure better integration between sub-rectangles, the image is expanded outward by a certain size ex during slicing, resulting in a final sub-rectangle task with length L1 and width W1. DEM elevation maps and DOMR imagery within the L1 and W1 rectangular areas are downloaded from the spatial library as basic preprocessing support data, such as... Figure 2 As shown;

[0022] L1 = L0 + ex

[0023] W1 = W0 + ex.

[0024] Step 5 includes:

[0025] Step 5.1: The projection transformation range is processed according to the length L0, width W0, and specified outer expansion range in Step 2. The outer expansion range is determined by the length L2 and width W2 based on the reference map size Jzt corresponding to the result resolution. Figure 3 As shown;

[0026] The formulas for calculating length L2 and width W2 are:

[0027]

[0028]

[0029] Step 5.2: Convert the data of length l2 and width W2 calculated in Step 5.1 from geodetic coordinates to Gaussian plane coordinates according to the Gaussian coordinate projection principle, and output the projected result according to the specified resolution.

[0030] In step 5.2, the specific formula for performing Gaussian coordinate projection is as follows:

[0031] Geometrically, Gaussian coordinate projection involves imagining a cylinder fitted over the Earth's ellipsoid and tangent to a meridian, with the cylinder's central axis located on the ellipsoid's equator. Figure 4 As shown;

[0032] The latitude and longitude coordinates (B, L) are converted to Gaussian coordinates (x, y) using the forward formula of Gaussian coordinate projection, and the longitude of the central meridian of the projection zone is defined as L. on The formula for calculating Gaussian coordinates (x, y) is as follows;

[0033]

[0034]

[0035] In the above formula,

[0036] X0=a(1-e 2 )(A2L-B2sin(2L)+C2sin(4L-D2sin(6L)+E2sin(8L)-F2sin(10L)+G2sin(12L))

[0037] In the above formula, let the longitude of the central meridian be L. on , and then

[0038] l = LL on

[0039] t = tanB

[0040] m0 = lcosB

[0041]

[0042]

[0043] In the above formula, L is the latitude in radians, a is the semi-major axis of the reference ellipsoid, e is the first eccentricity, and other relevant parameter values ​​are as follows:

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] In step 8, a projection coordinate transformation is performed based on the result generated in step 7.

[0052] Step 8.1: The projection transformation range is processed according to the length L0 and width W0 range in Step 2;

[0053] Step 8.2: Transform from the Gaussian plane coordinate system to the geodetic coordinate system, such as... Figure 7 As shown;

[0054] The inverse formula for Gaussian coordinate projection converts Gaussian coordinates (x, y) to geodetic coordinates (B, L), where the longitude of the central meridian of the projection zone is L. on The formula for calculating the geodetic coordinates (B, L) is as follows;

[0055]

[0056]

[0057] L = l + L on

[0058] In the above formula,

[0059]

[0060]

[0061] ηf 2 =e 2 cos 2 B f

[0062] t f =tanB f

[0063] In the formula, B f This represents the latitude of the base point, that is, the latitude value corresponding to the meridian with x as the starting point of the equator. It is calculated using an iterative solution method.

[0064] Among them, the latitude of the base point B is calculated. f The formula is as follows:

[0065] First, initialize the value to B. f 0

[0066]

[0067] Then iterative calculations are performed. value

[0068]

[0069] Until the iterative calculation satisfies until;

[0070] In the formula, 'a' is the semi-major axis of the reference ellipsoid, 'e' is the first eccentricity, and other relevant parameter values ​​are as follows:

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] In step 9, discrete pixel values ​​are reconstructed based on the data processed in step 8, and overly obvious discrete pixels are reassigned according to the specified neighborhood value rules.

[0079]

[0080]

[0081] In the above formula, i represents the row index value of the center point, j represents the column index value of the center point, d is 250, f is the image resolution, R represents the row index value of the top left corner point of the processing neighborhood, and C represents the column index value of the top left corner point of the processing neighborhood, with R and C rounded to the nearest integer.

[0082] When a black dot exists within a specified region, the center point is assigned a black value, such as... Figure 8 As shown;

[0083] When the specified area contains only white dots, the center point is assigned white, as shown in Figure 9.

[0084] (III) Beneficial Effects

[0085] Given the high real-time requirements of hypersonic precision-guided aircraft in wartime, this invention provides a comprehensive preprocessing workflow for peacetime image preprocessing compared to existing technologies. By fusing multi-source data, the accuracy of the image preprocessing results is improved. Through discrete pixel value reconstruction, pixel data with obvious discrete features is filtered out, making the preprocessing results more consistent with the actual terrain and landforms. More reasonable image results provide stronger support in wartime. Attached Figure Description

[0086] Figure 1 This is a schematic diagram of the target area to be processed provided by the present invention;

[0087] Figure 2 This is a schematic diagram of the target range segmentation provided by the present invention;

[0088] Figure 3 This is a schematic diagram of the image expansion provided by the present invention.

[0089] Figure 4 This is a schematic diagram of the geoid-Gaussian coordinate projection transformation provided by the present invention.

[0090] Figure 5 This is a schematic diagram illustrating the process of processing sea surface data by fusing elevation maps with sea surface mask images, as provided by the present invention.

[0091] Figure 6 The flowchart of the image analysis algorithm provided by this invention.

[0092] Figure 7 This is a schematic diagram of the coordinate projection transformation from Gaussian to geodetic provided by the present invention.

[0093] Figure 8 This is a demonstration diagram provided by the present invention for the reconstruction of black pixel values.

[0094] Figure 9 This is a demonstration diagram provided by the present invention for the case where black pixel values ​​are not reconstructed. Detailed Implementation

[0095] To make the objectives, contents, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples.

[0096] To address the aforementioned technical problems, this invention provides an image preprocessing method based on multi-source data, the image preprocessing method comprising the following steps:

[0097] Step 1: Specify the target range to be processed;

[0098] Step 2: Based on the target range specified in Step 1, divide the target range into sub-rectangular tasks;

[0099] Step 3: Distribute the sub-rectangle tasks based on the sub-rectangle tasks divided in Step 2; distribute the divided sub-rectangle tasks to the blade servers using parallel methods;

[0100] Step 4: The blade processor receives the sub-rectangle task, sends the image preprocessing parameters, and begins the image preprocessing calculation process;

[0101] Step 5: Based on the image preprocessing parameters issued in Step 4, including the elevation map, image map, and description image map range and corner coordinate information parameters, perform projection coordinate transformation on the DEM elevation map and DOMR image map;

[0102] Step 6: Based on the image preprocessing parameters issued in Step 4, including the elevation map, mask image, and parameters describing the mask image size, corner coordinates, and sea level information, the sea surface portion of the DEM elevation map is marked using the sea surface mask image. Elevations within the sea surface coverage area are uniformly assigned a value of -777. Figure 5 As shown;

[0103] Step 7: Based on the results of Step 6, and combining the DEM elevation map, DOMR image map, real-time map, and baseline map parameters, perform grayscale analysis, resolution analysis, filtering and denoising, and feature value extraction in sequence, and output the analyzed results. The specific calculation process is as follows: Figure 6 As shown;

[0104] Step 8: Based on the results generated in Step 7, perform projected coordinate transformation;

[0105] Step 9: Based on the data processed in Step 8, perform discrete pixel value reconstruction, and reassign values ​​to overly obvious discrete pixels according to the specified neighborhood value rules;

[0106] Step 10: Based on step 9, the results of the sub-range tasks are fused. The results of all blade machine calculations are sent back to the server for fusion processing through parallel means, and finally fused into the result data of the target range to be processed.

[0107] In step 1, the target range to be processed is specified;

[0108] The specified area is a rectangle defined by the top-left and bottom-right corners, and the rectangle's area falls within the range of the DEM elevation map, DOMR image map, and sea surface mask map. Figure 1 As shown.

[0109] In step 2, the target range is divided according to the target range specified in step 1 to form sub-rectangular tasks.

[0110] When segmenting the target area, sub-rectangles are cut according to a specified length L0 and width W0. To ensure better integration between sub-rectangles, the image is expanded outward by a certain size ex during slicing, resulting in a final sub-rectangle task with length L1 and width W1. DEM elevation maps and DOMR imagery within the L1 and W1 rectangular areas are downloaded from the spatial library as basic preprocessing support data, such as... Figure 2 As shown;

[0111] L1 = L0 + ex

[0112] W1 = W0 + ex.

[0113] Step 5 includes:

[0114] Step 5.1: The projection transformation range is processed according to the length L0, width W0, and specified outer expansion range in Step 2. The outer expansion range is determined by the length L2 and width W2 based on the reference map size Jzt corresponding to the result resolution. Figure 3 As shown;

[0115] The formulas for calculating length L2 and width W2 are:

[0116]

[0117]

[0118] Step 5.2: Convert the data of length L2 and width W2 calculated in Step 5.1 from geodetic coordinates to Gaussian plane coordinates according to the Gaussian coordinate projection principle, and output the projected result according to the specified resolution.

[0119] In step 5.2, the specific formula for performing Gaussian coordinate projection is as follows:

[0120] Geometrically, Gaussian coordinate projection involves imagining a cylinder fitted over the Earth's ellipsoid and tangent to a meridian, with the cylinder's central axis located on the ellipsoid's equator. Figure 4 As shown;

[0121] The latitude and longitude coordinates (B, L) are converted to Gaussian coordinates (x, y) using the forward formula of Gaussian coordinate projection, and the longitude of the central meridian of the projection zone is defined as L. on The formula for calculating Gaussian coordinates (x, y) is as follows;

[0122]

[0123]

[0124] In the above formula,

[0125] X0=a(1-e 2 )(A2L-B2sin(2L))+C2sin(4L)-D2sin(6L)+E2sin(8L)-F2sin(10L)+G2sin(12L))

[0126] In the above formula, let the longitude of the central meridian be L. on , and then

[0127] l = LL on

[0128] t = tanB

[0129] m0 = lcosB

[0130]

[0131]

[0132] In the above formula, L is the latitude in radians, a is the semi-major axis of the reference ellipsoid, e is the first eccentricity, and other relevant parameter values ​​are as follows:

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140] In step 8, a projection coordinate transformation is performed based on the result generated in step 7.

[0141] Step 8.1: The projection transformation range is processed according to the length L0 and width W0 range in Step 2;

[0142] Step 8.2: Transform from the Gaussian plane coordinate system to the geodetic coordinate system, such as... Figure 7 As shown;

[0143] The inverse formula for Gaussian coordinate projection converts Gaussian coordinates (x, y) to geodetic coordinates (B, L), where the longitude of the central meridian of the projection zone is L. on The formula for calculating the geodetic coordinates (B, L) is as follows;

[0144]

[0145]

[0146] L = l + L on

[0147] In the above formula,

[0148]

[0149]

[0150] η f 2 =e 2 cos 2 B f

[0151] t f =tanB f

[0152] In the formula, B f This represents the latitude of the base point, that is, the latitude value corresponding to the meridian with x as the starting point of the equator. It is calculated using an iterative solution method.

[0153] Among them, the latitude of the base point B is calculated. f The formula is as follows:

[0154] First, initialize the value to B. f 0

[0155]

[0156] Then iterative calculations are performed. value

[0157]

[0158] Until the iterative calculation satisfies until;

[0159] In the formula, 'a' is the semi-major axis of the reference ellipsoid, 'e' is the first eccentricity, and other relevant parameter values ​​are as follows:

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167] In step 9, discrete pixel values ​​are reconstructed based on the data processed in step 8, and overly obvious discrete pixels are reassigned according to the specified neighborhood value rules.

[0168]

[0169]

[0170] In the above formula, i represents the row index value of the center point, j represents the column index value of the center point, d is 250, f is the image resolution, R represents the row index value of the top left corner point of the processing neighborhood, and C represents the column index value of the top left corner point of the processing neighborhood, with R and C rounded to the nearest integer.

[0171] When a black dot exists within a specified region, the center point is assigned a black value, such as... Figure 8 As shown;

[0172] When the specified area contains only white dots, the center point is assigned white, as shown in Figure 9.

[0173] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An image preprocessing method based on multi-source data, characterized in that, The image preprocessing method includes the following steps: Step 1: Specify the target range to be processed; Step 2: Based on the target range specified in Step 1, divide the target range into sub-rectangular tasks; Step 3: Distribute the sub-rectangle tasks based on the sub-rectangle tasks divided in Step 2; distribute the divided sub-rectangle tasks to the blade servers using parallel methods; Step 4: The blade processor receives the sub-rectangle task, sends the image preprocessing parameters, and begins the image preprocessing calculation process; Step 5: Based on the image preprocessing parameters issued in Step 4, including the elevation map, image map, and description image map range and corner coordinate information parameters, perform projection coordinate transformation on the DEM elevation map and DOMR image map; Step 6: Based on the image preprocessing parameters issued in Step 4, including elevation map, mask map and parameters describing the size of the mask map, corner coordinates, and sea surface value, mark the sea surface part of the DEM elevation map in combination with the sea surface mask map, and uniformly assign the elevation within the sea surface coverage area to -777. Step 7: Based on the results of Step 6, and combining the DEM elevation map, DOMR image map and real-time map and baseline map parameters, perform grayscale analysis, resolution analysis, filtering and noise reduction, and feature value extraction in sequence, and output the results after analysis. Step 8: Based on the results generated in Step 7, perform projected coordinate transformation; Step 9: Based on the data processed in Step 8, perform discrete pixel value reconstruction, and reassign overly obvious discrete pixels according to the specified neighborhood value rules; the details are as follows: In the above formula, i represents the row index value of the center point, j represents the column index value of the center point, d is 250, f is the image resolution, R represents the row index value of the top left corner point of the processing neighborhood, and C represents the column index value of the top left corner point of the processing neighborhood, with R and C rounded to the nearest integer. When a black dot exists within a specified region, the center point is assigned a black dot. When the specified domain contains only white dots, the center point is assigned a white value; Step 10: Based on step 9, the results of the sub-range tasks are fused. The results of all blade machine calculations are sent back to the server for fusion processing through parallel means, and finally fused into the result data of the target range to be processed.

2. The image preprocessing method based on multi-source data as described in claim 1, characterized in that, In step 1, the target range to be processed is specified; The specified range is a rectangle defined by the top left and bottom right corners, and the rectangle is within the range of the DEM elevation map, DOMR image map, and sea surface mask map.

3. The image preprocessing method based on multi-source data as described in claim 2, characterized in that, In step 2, the target range is divided according to the target range specified in step 1 to form the divided sub-rectangular tasks. When segmenting the target area, sub-rectangles are cut according to the specified length L0 and width W0. In order to ensure better integration between sub-rectangles, the image is expanded by a certain size ex during the slicing process to obtain the final slicing sub-rectangle task with length L1 and width W1. The DEM elevation map and DOMR image map within the rectangle with length L1 and width W1 are downloaded from the spatial library as basic preprocessing guarantee data. L1 = L0 + ex W1 = W0 + ex.

4. The image preprocessing method based on multi-source data as described in claim 3, characterized in that, Step 5 includes: Step 5.1: The projection transformation range is processed according to the length L0, width W0 and the specified outer expansion range in Step 2. The length L2 and width W2 of the outer expansion range are determined according to the reference map size Jzt corresponding to the result resolution. The formulas for calculating length L2 and width W2 are: Step 5.2: Convert the data of length L2 and width W2 calculated in Step 5.1 from geodetic coordinates to Gaussian plane coordinates according to the Gaussian coordinate projection principle, and output the projected result according to the specified resolution.

5. The image preprocessing method based on multi-source data as described in claim 4, characterized in that, In step 5.2, the specific formula for performing Gaussian coordinate projection is as follows: From a geometric perspective, Gaussian coordinate projection is like imagining a cylinder placed around the Earth's ellipsoid and tangent to a meridian, with the central axis of the cylinder located on the equator of the ellipsoid. The latitude and longitude coordinates (B, L) are converted to Gaussian coordinates (x, y) using the forward formula of Gaussian coordinate projection, and the longitude of the central meridian of the projection zone is defined as L. on The formula for calculating Gaussian coordinates (x, y) is as follows; In the above formula, X0=a(1-e 2 (A2L-B2sin(2L)+C2sin(4L)-D2sin(6L)+E2sin(8L)-F2sin(10L)+G2sin(12L)) In the above formula, let the longitude of the central meridian be L. on , and then l / LL on t = tanB m0 = lcosB In the above formula, L is the latitude in radians, a is the semi-major axis of the reference ellipsoid, e is the first eccentricity, and other relevant parameter values ​​are as follows:

6. The image preprocessing method based on multi-source data as described in claim 5, characterized in that, In step 8, projection coordinate transformation is performed based on the results generated in step 7; Step 8.1: The projection transformation range is processed according to the length L0 and width W0 range in Step 2; Step 8.2: Convert from Gaussian plane coordinate system to geodetic coordinate system; The inverse formula for Gaussian coordinate projection converts Gaussian coordinates (x, y) to geodetic coordinates (B, L), where the longitude of the central meridian of the projection zone is L. on The formula for calculating the geodetic coordinates (B, L) is as follows; L=l+L on In the above formula, or f 2 =e 2 cos 2 B f t f =tanB f In the formula, B f This represents the latitude of the base point, that is, the latitude value corresponding to the meridian with x as the starting point of the equator. It is calculated using an iterative solution method.

7. The image preprocessing method based on multi-source data as described in claim 6, characterized in that, Calculate the latitude of the base point B f The formula is as follows: First, initialize the value to B. f 0 Then iterative calculations are performed. value Until the iterative calculation satisfies until; In the formula, 'a' is the semi-major axis of the reference ellipsoid, 'e' is the first eccentricity, and other relevant parameter values ​​are as follows:

8. The image preprocessing method based on multi-source data as described in claim 7, characterized in that, The method is applied to mission planning for precision-guided aircraft.

9. The image preprocessing method based on multi-source data as described in claim 7, characterized in that, The method integrates multi-source data, which improves the accuracy of image preprocessing results; by using discrete pixel value reconstruction, pixel data with obvious discrete features is filtered out, making the preprocessing results more consistent with the actual terrain and landform.

Images