A SAR image adaptive threshold segmentation method and device

Abstract

The present invention discloses a SAR image adaptive threshold segmentation method and device, which relate to the technical field of remote sensing image processing. The present invention first extracts a larger target area in the SAR image through coarse threshold segmentation, automatically selects background points according to the target in the image and extracts a profile line from the target center to the background point, calculates the threshold corresponding to the target based on the profile line, adaptively divides the image into blocks according to the target, and automatically selects a local threshold to achieve adaptive threshold segmentation of the SAR image; compared with the existing adaptive threshold segmentation method and the semi-automatic global threshold segmentation method, the present invention adaptively divides the image into blocks with the large target as the center, automatically draws a profile line across the target and the background for the block image, and automatically extracts the local threshold, thereby overcoming the problems of regular grid division, automatic threshold extraction, manual line drawing, etc. in the existing methods, greatly improving the effect of SAR image threshold segmentation, and can be applied to water body extraction, shadow detection, bright target detection, etc.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image processing, and in particular to a SAR image adaptive threshold segmentation method and device. Background Art

[0002] In reality, compared with optical remote sensing images, SAR images have the advantages of penetrating clouds and fog and collecting information all day and all weather; quickly and accurately obtaining ground information from SAR images is of great significance to surface monitoring, ecological assessment, disaster monitoring and early warning, etc.; threshold segmentation of SAR images is currently the most basic and most commonly used technical method for extracting ground object information; this method uses different thresholds to distinguish different ground objects in the image and extract the target ground objects from the background; for example: water body information extraction, coastline extraction, etc.; however, there is a large amount of coherent speckle noise in SAR images and this noise is multiplicative noise. Compared with the additive noise processed in the optical field, the removal of multiplicative noise is more complicated. Coupled with the unique statistical distribution characteristics of SAR, SAR image threshold segmentation is more difficult than optical image threshold segmentation.

[0003] In practical applications, methods for obtaining thresholds can be divided into two categories: manual extraction and automatic extraction. The manual extraction method requires the operator to check the intensity value between the target object and the background multiple times, and roughly give a threshold based on past experience. The image must also be repeatedly tested and compared. This method relies purely on manual visual interpretation. Although it can sometimes achieve good accuracy, the workload is large and the final threshold is random. When thresholding a large number of SAR images, this method is less efficient. The automatic threshold extraction method is more stable and objective than manual extraction, and can process data in batches. A common method is to improve the OTSU method and apply it to SAR images. However, due to the unique statistical characteristics of SAR images, the SAR intensity obeys a unimodal negative exponential distribution and there is no valley value, so the segmentation effect is not ideal.

[0004] To address these issues, some researchers have proposed a semi-automatic threshold segmentation method. This method requires manual drawing of one or more straight lines across the water and land areas. A greedy algorithm is then used to automatically determine the boundary between bright and dark objects, using the pixel value at the boundary as the threshold. However, this method requires manual line drawing, and the position and number of lines drawn are subjective. Furthermore, the use of a global threshold makes segmentation difficult when the boundaries between bright and dark objects vary significantly across different areas. Consequently, the current threshold segmentation method is ineffective and difficult to apply to water extraction, shadow detection, bright object detection, and other areas. Summary of the Invention

[0005] The embodiments of the present invention provide a SAR image adaptive threshold segmentation method and device, which can solve the problem in the prior art that the threshold segmentation method currently adopted has poor effect.

[0006] The embodiment of the present invention provides a SAR image adaptive threshold segmentation method and device, including the following steps:

[0007] Obtain signal strength or amplitude maps of SAR images;

[0008] Extracting dark targets from the binary image of the signal intensity map or amplitude map of the SAR image, determining the minimum bounding rectangle corresponding to the dark target, and performing a binary operation on the dark target according to the minimum bounding rectangle corresponding to the dark target to obtain the divided dark target;

[0009] For each dark target after segmentation, a background point is selected, and a profile line from the center of the dark target to the background point is extracted, and the local threshold of the dark target of the corresponding segmentation is determined according to the profile line;

[0010] Divide the signal intensity map or amplitude map of the SAR image into blocks to obtain block images;

[0011] Detect the dark targets in each block image. If there is only one dark target, the local threshold corresponding to the dark target is used as the segmentation threshold of the block image. If there are multiple dark targets, the local threshold corresponding to the largest dark target is used as the segmentation threshold of the block image. If there is no dark target, the local threshold corresponding to the dark target closest to the geometric center of the block image is used as the segmentation threshold of the block image.

[0012] Each block image is binarized according to the corresponding local threshold to obtain the threshold segmentation image of the SAR image.

[0013] Preferably, determining the minimum bounding rectangle corresponding to the dark target includes:

[0014] Determine the boundary points of the dark target object and form a point set of the dark target from all the boundary points;

[0015] The minAreaRect function in OpenCV software is used to calculate the point set of the dark target and obtain the minimum bounding rectangle of the dark target.

[0016] Preferably, performing a binary operation on the dark object according to the minimum circumscribed rectangle corresponding to the dark object comprises the following steps:

[0017] Calculate the minimum bounding rectangle of each target in the SAR image;

[0018] If the aspect ratio of the minimum circumscribed rectangle is ≥ 2, the rectangle is divided into two equal parts along the direction perpendicular to the long side;

[0019] Generate a scan line from the minimum bounding rectangle in both length and width directions, and count the number of pixel value changes along the scan line. If the number of changes is greater than two, divide the rectangle into two parts along the scan line.

[0020] The new target obtained after the binary division is judged again. If it meets the binary division conditions, the binary division is continued until all targets do not meet the binary division conditions.

[0021] Preferably, determining the local threshold corresponding to the dark target block includes the following steps:

[0022] Obtain the background points corresponding to each dark target in the segmented SAR image. If the number of background points is 0, skip the dark target and continue to calculate the threshold between the next dark target and the background.

[0023] Connect the geometric center of the minimum circumscribed rectangle corresponding to the dark target with the background point, and estimate the image profile line through which the connecting line passes;

[0024] According to the gradient function, the gradients of adjacent pixels are calculated starting from both ends of the profile line;

[0025] Merge pixels from both ends of the profile line toward the center according to the gradient and update the gradient until the pixel pairs meet;

[0026] The mean of the pixel pairs at the meeting point is used as the threshold, and the average of the thresholds corresponding to all background points is used as the local threshold of the block image target.

[0027] Preferably, obtaining the background point corresponding to each dark target in the segmented SAR image comprises the following steps:

[0028] For each dark target extracted, calculate its minimum bounding rectangle, and further calculate the image coordinates of the long and short side lengths a, b and the geometric center o;

[0029] Take two points along the short side whose distance from the geometric center o is equal to the short side length b, and define them as candidate background points;

[0030] Starting from the geometric center o, the number of pixel changes along the line connecting the candidate background point and the geometric center is determined. If there is no change, the background point is eliminated; if it changes only once, the point is determined as a background point; if the number of changes reaches 2 times or more, the midpoint between the first change and the second change position is taken as the background point;

[0031] If the number of background points is 0, then take the point with a distance from the geometric center o to the long side length a along the long side direction and define it as a candidate background point.

[0032] Preferably, estimating the image profile line through which the connecting line passes comprises the following steps:

[0033] A buffer zone is automatically generated on both sides of the line. The buffer zone takes the drawn line as the center axis, and the distance between the two ends and the center axis is L.

[0034] Extract the pixel values ​​covered by the buffer zone and calculate the mean of all pixels on the vertical line of the central axis to form a mean line, which is the estimated profile line.

[0035] Preferably, the calculating of gradients of adjacent pixels comprises the following steps:

[0036] Let the section line be X, x=(x1,x2,x3,...x n ) is the set of pixels on the profile line X, the total number of pixels is n, and the gradient of adjacent pixels is calculated using the gradient function; where the gradient function used for the head end is:

[0037] f(M k ,x k+1 )=|x k+1 -M k |k=1,2,...,n-1

[0038] Where M1=x1,M k is the weighted mean, and the formula is:

[0039]

[0040] The gradient function used at the tail end is:

[0041] f(x h-1 ,M h )=|x h-1 -M h |h=n,n-1,...,2

[0042] Among them, M n =x n , M h is the weighted mean, and the formula is:

[0043]

[0044] Preferably, the step of combining pixels from both ends of the profile line toward the center and updating the gradient until a pair of pixels meet includes:

[0045] Using the calculated gradient f as the comparison criterion, the left and right ends with the smaller gradient are merged forward by one pixel, and the gradient of the merged pixel set and its adjacent pixels are recalculated. The larger end is not merged, and comparisons are made successively until the left and right ends meet.

[0046] Preferably, obtaining the block images includes:

[0047] The basic block size B is set. For each dark target, the minimum bounding rectangle of the dark target is used for judgment. When any side length of the bounding rectangle of the dark target is greater than B, the SAR image range corresponding to the bounding rectangle is set as a block image. When two or more bounding rectangles overlap, the overlapping bounding rectangles are merged into one rectangle. For the remaining part of the SAR image, the blocks are divided according to the regular grid.

[0048] An embodiment of the present invention further provides a SAR image adaptive threshold segmentation device, comprising:

[0049] SAR imaging module, used to obtain the signal strength map or amplitude map of SAR images;

[0050] An image segmentation module is used to extract dark targets from the binary image of the signal intensity map or amplitude map of the SAR image, determine the minimum bounding rectangle corresponding to the dark target, and perform a binary operation on the dark target according to the minimum bounding rectangle corresponding to the dark target to obtain the dark target after segmentation;

[0051] For each dark target after segmentation, a background point is selected, and a profile line from the center of the dark target to the background point is extracted, and the local threshold of the dark target of the corresponding segmentation is determined according to the profile line;

[0052] Divide the signal intensity map or amplitude map of the SAR image into blocks to obtain block images;

[0053] The threshold acquisition module is used to detect the dark targets in each block image. If there is only one dark target, the local threshold corresponding to the dark target is used as the segmentation threshold of the block image. If there are multiple dark targets, the local threshold corresponding to the largest dark target is used as the segmentation threshold of the block image. If there is no dark target, the local threshold corresponding to the dark target closest to the geometric center of the block image is used as the segmentation threshold of the block image.

[0054] The image segmentation module is used to binarize each block image according to the corresponding local threshold value to obtain the threshold segmentation image of the SAR image.

[0055] The present invention provides a method and device for adaptive threshold segmentation of SAR images. Compared with the prior art, the method and device have the following advantages:

[0056] The present invention first extracts the larger target area in the SAR image through rough threshold segmentation and opening and closing operations, then automatically selects background points and extracts the profile line from the target center to the background point according to the target in the image, and calculates the threshold corresponding to the target based on the profile line. Finally, the image is adaptively divided into blocks according to the target, and the local threshold is automatically selected to achieve adaptive threshold segmentation of the SAR image. Compared with manually drawing lines on the entire image and taking a global threshold, the present invention overcomes the subjectivity and arbitrariness of manual work by automatically drawing lines on the divided images using background points and profile lines after dividing the image into blocks. In addition, the fewer dark targets in the divided image can make the difference between the boundary between bright and dark targets smaller, making it very easy to segment. Finally, the segmentation threshold is used to binarize the divided images and combine them to obtain the segmentation result of the SAR image, which greatly improves the effect of SAR image threshold segmentation and can be applied to water body extraction, shadow detection, bright target detection and other directions.

[0057] Moreover, compared with global single-threshold segmentation, the present invention adopts automatic image segmentation and multi-threshold extraction and segmentation, which can effectively improve the accuracy of light and dark boundary extraction and detection, reduce the false detection rate, have better detection effect, and have stronger stability.

[0058] Moreover, compared with the adaptive threshold segmentation of regular grid blocks, the present invention performs size-adaptive segmentation for larger targets in the image, and through the two steps of coarse threshold segmentation and precise threshold segmentation, it can effectively avoid the problem of large segmentation deviation in the automatic segmentation method, which may occur when the regular grid blocks are full of bright areas or dark areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 The present invention provides a schematic diagram of the overall process of a SAR image adaptive threshold segmentation method and device. DETAILED DESCRIPTION

[0060] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth many specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art can make similar modifications without violating the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] See also Figure 1 The embodiment of the present invention provides a SAR image adaptive threshold segmentation method, comprising the following steps:

[0062] Step 1: Preliminary binarization:

[0063] Calculate the intensity map or amplitude map of the original SAR image and perform preliminary binarization based on the Otsu method.

[0064] Step 2: Dark target extraction and segmentation:

[0065] Perform erosion and dilation processing on the binary image to remove noise and small objects while retaining larger objects in the image; extract dark objects and calculate their minimum bounding rectangles. For dark objects with complex shapes or long and narrow shapes, perform a binary operation based on their minimum bounding rectangles.

[0066] Specifically, the method for performing binary operation on dark targets is:

[0067] Generate scan lines in both the length and width directions of the minimum bounding rectangle, and count the number of pixel value changes along the scan lines. If the number of changes is greater than two, or the aspect ratio of the minimum bounding rectangle is ≥2, the rectangle is divided into two equal parts perpendicular to the long side. At this time, the dark target inside the rectangle is also divided into two parts. The new dark target obtained after the binary division is judged again. If it meets the binary division conditions, the binary division is continued until all dark targets meet the binary division conditions.

[0068] Step 3: Calculation of local threshold and its position:

[0069] The threshold between the dark target and the background after each block is calculated, and the geometric center of the minimum circumscribed rectangle of the dark target is used as the position of the threshold.

[0070] Specifically, the method for calculating the local threshold is:

[0071] ① Background point selection: For each dark target extracted, two background points are calculated and selected.

[0072] ② Automatic connection: Automatically connect the geometric center of the minimum circumscribed rectangle and the background point.

[0073] ③ Profile estimation: Estimate the values ​​of the image profile pixels through which the connecting line passes.

[0074] ④ Gradient calculation: Starting from the beginning and end of the profile line, calculate the gradient f of adjacent pixels according to the gradient function.

[0075] ⑤ Pixel merging: Merge pixels from both ends of the profile line toward the center according to the gradient, and update the gradient until they meet.

[0076] ⑥Threshold calculation: The threshold is calculated based on the value of the pixel pair at the intersection and the gradient. Since there are two background points, there are two corresponding thresholds. Finally, the average of these two thresholds is taken as the local threshold corresponding to the dark target.

[0077] Specifically, the above-mentioned background point selection method is:

[0078] For each dark target extracted, calculate the length of the short side l of its minimum circumscribed rectangle and the coordinates of the geometric center o, and then select points along the short side direction whose distance from the geometric center is the length of the short side. There are two such points, which are defined as candidate background points.

[0079] Starting from the geometric center o, the number of pixel changes is determined along the line connecting the candidate background point and the geometric center. If it changes only once, the point is determined as a background point; if the number of changes reaches 2 times or more, the midpoint between the first change and the second change position is taken as the background point.

[0080] Specifically, the above-mentioned profile estimation method is:

[0081] A buffer zone is automatically generated on both sides of the line. The buffer zone takes the drawn line as the center axis, and the distance between the two ends and the center axis is L.

[0082] Extract the pixel values ​​covered by the buffer zone, calculate the mean of all pixels on the vertical line of the central axis, and form a mean line, which is the estimated profile line.

[0083] Specifically, the above gradient calculation method is:

[0084] Let the section line be X, x=(x1,x2,x3,...x n ) is the set of pixels on the profile line X, the total number of pixels is n, and the gradient of adjacent pixels is calculated using the gradient function; where the gradient function used for the head end is:

[0085] f(M k ,x k+1 )=|x k+1 -M k |k=1,2,...,n-1

[0086] Where: M1 = x1; M k is the weighted mean, and the formula is:

[0087]

[0088] The gradient function used at the tail end is:

[0089] f(x h-1 ,M h )=|x h-1 -M h |h=n,n-1,...,2

[0090] Where: M n =x n ;M h is the weighted mean, and the formula is:

[0091]

[0092] Specifically, the above pixel merging method is:

[0093] Using the calculated gradient f as the comparison criterion, the end with the smaller gradient on the left and right ends is merged forward by one pixel, and the gradient of the merged pixel set and its adjacent pixels is recalculated. The larger end is not merged, and this process is repeated until the left and right ends meet.

[0094] Specifically, the above threshold calculation method is:

[0095] Perform various operations on the values ​​and gradients of pixel pairs at the intersection to obtain several thresholds. Users can select the optimal threshold based on different application scenarios. There are four calculation methods:

[0096] ①When the encounter pixel pair (x i , x i+1 ), the arithmetic mean of the two pixels is calculated as the final threshold T; its formula is as follows:

[0097]

[0098] ②When the encounter pixel pair (x i , x i+1 ), the smaller value of the two pixels is selected as the final threshold T.

[0099] ③When the encounter pixel pair (x i , x i+1 ), the weighted average of the two pixels is calculated as the final threshold T; its formula is as follows:

[0100]

[0101] ④ Sort the gradients calculated on the left and right sides of the profile line from small to large, select j gradients starting from the larger side, and calculate the arithmetic mean of the 2j pixels corresponding to the j gradients as the final threshold T.

[0102] Step 4: Image segmentation:

[0103] Divide the SAR intensity map or amplitude map into blocks.

[0104] Specifically, the image segmentation method is:

[0105] Set the basic block size B. For the minimum bounding rectangle of the dark target detected in the previous step, if any side length is greater than B, extract the original image range corresponding to the rectangle as a block image. If two or more minimum bounding rectangles overlap, merge the overlapping bounding rectangles into one block.

[0106] The remaining image part of the original image is divided according to a regular grid. The specific division method is as follows:

[0107] Assuming that the number of rows of the entire image is H and the number of columns is W, the number of blocks in the column direction is l = round(H / B), where round means rounding off, and the number of blocks in the row direction is r = round(W / B).

[0108] If l=0, let l=1, and if r=0, let r=1.

[0109] Then the number of rows of the actual block image is h=round(H / l), and the number of columns is w=round(W / r).

[0110] Finally, the remaining part of the entire image is divided into blocks of size h*w. Due to rounding, the actual block size of the last row and the last column will vary slightly. The number of rows in the last row of the block image is: Hh*l, and the number of columns in the last column of the block image is: Ww*r.

[0111] Step 5: Image binarization:

[0112] For each SAR image after segmentation, a suitable local threshold is selected as the threshold of the block image, and the threshold is used to binarize the SAR image after segmentation. After each block image is binarized, the entire image is binarized.

[0113] Specifically, the threshold selection method for the block image is:

[0114] Detect the dark target in each block image. If there is only one dark target, the threshold corresponding to the dark target is used as the segmentation threshold of the block image; if there are multiple dark targets, the threshold corresponding to the largest dark target is used as the segmentation threshold of the block image; if there is no dark target, the threshold corresponding to the dark target closest to the geometric center of the block image is used as the segmentation threshold of the block image.

[0115] The purpose of the present invention is to quickly and accurately obtain multiple local thresholds for distinguishing target objects from backgrounds through technical means such as image segmentation, selection of bright and dark target points, and automatic threshold extraction, and automatically perform adaptive image segmentation, effectively solving the problem that the threshold is difficult to automatically determine in the current SAR image threshold segmentation.

[0116] Compared with manual threshold selection, especially when processing large amounts of data, the method of the present invention effectively overcomes the disadvantage of excessive subjectivity in manually obtained results and greatly improves the efficiency and accuracy of threshold extraction.

[0117] Compared with semi-automatic threshold selection, the method of the present invention automates the marking and blocking processes, further reducing the random influence that may be caused by manual participation, obtaining higher-precision extraction results, and can be applied to images of different sensors, different regions, and different resolutions.

[0118] Compared with global single threshold segmentation, the method of the present invention uses automatic image segmentation and multi-threshold extraction and segmentation, which can effectively improve the accuracy of light and dark boundary extraction and detection, reduce the false detection rate, have better detection effect, and have better stability.

[0119] An embodiment of the present invention further provides a SAR image adaptive threshold segmentation device, comprising:

[0120] The SAR image module is used to obtain the signal strength map or amplitude map of the SAR image.

[0121] The image segmentation module is used to extract dark targets from the binary image of the signal intensity map or amplitude map of the SAR image, determine the minimum bounding rectangle corresponding to the dark target, and perform a binary operation on the dark target according to the minimum bounding rectangle corresponding to the dark target to obtain the blocked dark target.

[0122] For each dark target after segmentation, a background point is selected, and a profile line from the center of the dark target to the background point is extracted, and the local threshold of the dark target of the corresponding segmentation is determined according to the profile line.

[0123] The signal intensity map or amplitude map of the SAR image is divided into blocks to obtain a block image.

[0124] The threshold acquisition module is used to detect the block dark targets in each block image. If there is only one block dark target, the local threshold corresponding to the block dark target is used as the segmentation threshold of the block image; if there are multiple block dark targets, the local threshold corresponding to the largest block dark target is used as the segmentation threshold of the block image; if there is no block dark target, the local threshold corresponding to the block dark target closest to the geometric center of the block image is used as the segmentation threshold of the block image.

[0125] The image segmentation module is used to binarize each block image according to the corresponding local threshold value to obtain the threshold segmentation image of the SAR image.

[0126] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A SAR image adaptive threshold segmentation method, characterized in that: The following steps are involved: Obtain signal strength or amplitude maps of SAR images; Extracting dark targets from the binary image of the signal intensity map or amplitude map of the SAR image, determining the minimum bounding rectangle corresponding to the dark target, and performing a binary operation on the dark target according to the minimum bounding rectangle corresponding to the dark target to obtain the divided dark target; For each dark target after segmentation, a background point is selected, and a profile line from the center of the dark target to the background point is extracted, and the local threshold of the dark target of the corresponding segmentation is determined according to the profile line; Divide the signal intensity map or amplitude map of the SAR image into blocks to obtain block images; Detect the dark objects in each block image. If there is only one dark object, use the local threshold corresponding to the dark object as the segmentation threshold of the block image. If there are multiple dark objects in the block, the local threshold corresponding to the largest dark object in the block is used as the segmentation threshold of the block image; If there is no dark target in the block, the local threshold corresponding to the dark target in the block closest to the geometric center of the block image is used as the segmentation threshold of the block image; Each block image is binarized according to the corresponding local threshold to obtain the threshold segmentation image of the SAR image.

2. The SAR image adaptive threshold segmentation method according to claim 1, characterized in that: Determining the minimum bounding rectangle corresponding to the dark target includes: Determine the boundary points of the dark target object and form a point set of the dark target from all the boundary points; The minAreaRect function in OpenCV software is used to calculate the point set of the dark target and obtain the minimum bounding rectangle of the dark target.

3. The SAR image adaptive threshold segmentation method according to claim 1, characterized in that: The binary operation of the dark object according to the minimum circumscribed rectangle corresponding to the dark object includes the following steps: Calculate the minimum bounding rectangle of each target in the SAR image; If the aspect ratio of the minimum circumscribed rectangle is ≥ 2, the rectangle is divided into two equal parts along the direction perpendicular to the long side; Generate a scan line from the minimum bounding rectangle in both length and width directions, and count the number of pixel value changes along the scan line. If the number of changes is greater than two, divide the rectangle into two parts along the scan line. The new target obtained after the binary division is judged again. If it meets the binary division conditions, the binary division is continued until all targets do not meet the binary division conditions.

4. The SAR image adaptive threshold segmentation method according to claim 1, characterized in that: Determining the local threshold corresponding to the dark target block includes the following steps: Obtain the background points corresponding to each dark target in the segmented SAR image. If the number of background points is 0, skip the dark target and continue to calculate the threshold between the next dark target and the background. Connect the geometric center of the minimum circumscribed rectangle corresponding to the dark target with the background point, and estimate the image profile line through which the connecting line passes; According to the gradient function, the gradients of adjacent pixels are calculated starting from both ends of the profile line; Merge pixels from both ends of the profile line toward the center according to the gradient and update the gradient until the pixel pairs meet; The mean of the pixel pairs at the meeting point is used as the threshold, and the average of the thresholds corresponding to all background points is used as the local threshold of the block image target.

5. The SAR image adaptive threshold segmentation method according to claim 4, characterized in that: The method of obtaining the background point corresponding to each dark target in the segmented SAR image comprises the following steps: For each dark target extracted, calculate its minimum bounding rectangle, and further calculate the image coordinates of the long and short side lengths a, b and the geometric center o; Take two points along the short side whose distance from the geometric center o is equal to the short side length b, and define them as candidate background points; Starting from the geometric center o, the number of pixel changes along the line connecting the candidate background point and the geometric center is determined. If there is no change, the background point is eliminated; if it changes only once, the point is determined as a background point; if the number of changes reaches 2 times or more, the midpoint between the first change and the second change position is taken as the background point; If the number of background points is 0, then take the point with a distance from the geometric center o to the long side length a along the long side direction and define it as a candidate background point.

6. The SAR image adaptive threshold segmentation method according to claim 4, characterized in that: The step of estimating the image profile line through which the connecting line passes comprises the following steps: A buffer zone is automatically generated on both sides of the line. The buffer zone takes the drawn line as the center axis, and the distance between the two ends and the center axis is L. Extract the pixel values ​​covered by the buffer zone and calculate the mean of all pixels on the vertical line of the central axis to form a mean line, which is the estimated profile line.

7. The SAR image adaptive threshold segmentation method according to claim 4, characterized in that: Calculating the gradients of adjacent pixels comprises the following steps: Let the section line be X, x=(x1,x2,x3,...x n ) is the set of pixels on the profile line X, the total number of pixels is n, and the gradient of adjacent pixels is calculated using the gradient function; where the gradient function used for the head end is: f(M k ,x k+1 )=|x k+1 -M k | k=1,2,...,n-1 Where M1=x1,M k is the weighted mean, and the formula is: The gradient function used at the tail end is: f(x h-1 ,M h )=|x h-1 -M h |h=n,n-1,...,2 Among them, M n =x n , M h is the weighted mean, and the formula is: 。 8. The SAR image adaptive threshold segmentation method according to claim 4, characterized in that: The pixel merging and gradient updating from both ends of the profile line toward the center according to the gradient until the pixel pairs meet include: Using the calculated gradient f as the comparison criterion, the left and right ends with the smaller gradient are merged forward by one pixel, and the gradient of the merged pixel set and its adjacent pixels are recalculated. The larger end is not merged, and comparisons are made successively until the left and right ends meet.

9. The SAR image adaptive threshold segmentation method according to claim 1, characterized in that: The step of obtaining the block images comprises: The basic block size B is set. For each dark target, the judgment is made based on the minimum bounding rectangle of the dark target. When any side length of the minimum bounding rectangle of the dark target is greater than B, the SAR image range corresponding to the minimum bounding rectangle is set as a block image. When two or more minimum bounding rectangles overlap, the overlapping minimum bounding rectangles are merged into one rectangle. For the remaining part of the SAR image, the blocks are divided according to the regular grid.

10. A SAR image adaptive threshold segmentation device, characterized in that: include: SAR imaging module, used to obtain the signal strength map or amplitude map of SAR images; An image segmentation module is used to extract dark targets from the binary image of the signal intensity map or amplitude map of the SAR image, determine the minimum bounding rectangle corresponding to the dark target, and perform a binary operation on the dark target according to the minimum bounding rectangle corresponding to the dark target to obtain the dark target after segmentation; For each dark target after segmentation, a background point is selected, and a profile line from the center of the dark target to the background point is extracted, and the local threshold of the dark target of the corresponding segmentation is determined according to the profile line; Divide the signal intensity map or amplitude map of the SAR image into blocks to obtain block images; The threshold acquisition module is used to detect the dark target in each block image. If there is only one dark target, the local threshold corresponding to the dark target is used as the segmentation threshold of the block image. If there are multiple dark objects in the block, the local threshold corresponding to the largest dark object in the block is used as the segmentation threshold of the block image; If there is no dark target in the block, the local threshold corresponding to the dark target in the block closest to the geometric center of the block image is used as the segmentation threshold of the block image; The image segmentation module is used to binarize each block image according to the corresponding local threshold value to obtain the threshold segmentation image of the SAR image.

Images