CN112017158B - Spectral characteristic-based adaptive target segmentation method in remote sensing scene - Google Patents
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Abstract
The invention provides a self-adaptive target segmentation method in a remote sensing scene based on spectral characteristics, which solves the problem of low target segmentation precision of the existing image segmentation method, improves the precision of obtaining high-resolution remote sensing target information, and provides more characteristics for the post-processing of a remote sensing target. The method comprises the following steps: 1) Inputting remote sensing scene data; 2) Carrying out Gaussian filtering on the remote sensing scene data; 3) Performing HSV space conversion on the filtered data; 4) Performing Lab space conversion on the filtered data; 5) Calculating the number of the super-pixel segmentation areas; 6) Performing superpixel segmentation on the data of the Lab spatial domain by using an SLIC (narrow slice correlation coefficient); 7) Calculating HSV mean value mapping of each region after superpixel segmentation; 8) Performing secondary clustering by using a distance measurement function defined by combining k-means + +; 9) Calculating the maximum number of classes; 10 Merge target shadow regions; 11 Hole filling; 12 Output a remote sensing target segmentation mask.
Description
Technical Field
The invention belongs to the field of high-resolution visible light remote sensing image data processing, and particularly relates to a self-adaptive target segmentation method in a remote sensing scene based on spectral characteristics.
Background
The remote sensing image has abundant spectral information and spatial information, so that the automatic interpretation of the remote sensing data is quickly and effectively realized, and the extraction of important target information in the remote sensing image is the development direction of the high-resolution remote sensing field. Image segmentation is a core content of the development direction, and plays an important role in the field of remote sensing image processing. In the remote sensing target segmentation method, under the conditions of small data volume and unsupervised segmentation, related algorithm researches such as a k-means clustering algorithm and slic superpixel segmentation are carried out, but the existing target segmentation methods cannot effectively segment remote sensing images, often divide targets and background areas into one class, and reduce the precision of target segmentation.
Disclosure of Invention
The invention aims to solve the problem that the target segmentation precision is low in the existing image segmentation method, and provides a self-adaptive target segmentation method in a remote sensing scene based on spectral characteristics.
In order to solve the problems, the technical scheme of the invention is as follows:
a self-adaptive target segmentation method in a remote sensing scene based on spectral characteristics comprises the following steps:
step one, inputting a remote sensing target scene F to be segmented;
step two, carrying out Gaussian filtering on the remote sensing target scene F to obtain filtered data F _ gaus;
thirdly, performing HSV space conversion on the data filtered in the second step by using the following formula to obtain data F _ HSV, wherein the F _ HSV comprises values of three channels of H, S and V;
V＝max(R，G，B)
wherein, H, S and V are the values of three channels of the final HSV color space; r, G and B are three channel values of RGB space in F _ gauge;
performing Lab space conversion on the data filtered in the step two by using the following formula to obtain F _ Lab, wherein the F _ Lab comprises values of three channels of L, a and b;
wherein L, a, b are the values of the three channels of the final LAB color space; x, Y, Z are calculated values after RGB conversion; x n ，Y n ，Z n Conversion coefficients of X, Y and Z;
step five, calculating an initial segmentation number K1 of slic super pixels in the Lab space;
K1＝(w×h)/c
wherein, w and h are the length and width of the input data respectively, and c is the hyper-parameter of the input data;
step six, processing the F _ Lab data by using a slic super pixel segmentation algorithm according to the initialized segmentation number K1, segmenting n super pixel areas marked as L i ，i∈1，2，3…n；
Step seven, mapping the super-pixel regions obtained in the step six to an HSV space, and calculating the mean values of the super-pixel regions of the respective channels of the F _ HSV to obtain an F _ HSV _ mean;
F_HSV_mean＝(f_h_mean，f_s_mean，f_v_mean)
wherein f _ h _ mean represents the pixel mean of the h channel; f _ s _ mean represents the pixel mean of the s channel; f _ v _ mean represents the pixel mean of the v channel; length (L) i ) Indicating the number of pixels in the ith super-pixel region; f _ h i (k) A k-th pixel value representing an h-channel in an i-th super-pixel region; f _ s i (k) A k-th pixel value representing the s-channel in the i-th super-pixel region; f _ v i (k) A k-th pixel value representing a v-channel in the i-th super-pixel region;
step eight, performing secondary clustering on the F _ HSV _ mean data by using a distance measurement function and k _ means + +, wherein the default clustering number is 3, and the default clustering number represents a target, a shadow and a background respectively;
the distance metric function is as follows:
f_hsv＝(w1×f_h_mean，w2×f_s_mean，w3×f_v_mean)
wherein, w1, w2 and w3 are weight coefficients of three channels; l is i,j Represents a distance between the ith pixel and the jth pixel;
denotes f _ hsv i Rank of (d);
denotes f _ hsv j Rank of (d); f _ hsv j A column vector representing that pixel values of the jth pixel constitute three dimensions; f _ hsv i Pixel values representing the ith pixel constitute a three-dimensional column vector; f _ HSV represents data subjected to weight transformation on F _ HSV _ mean data;
step nine, searching a corresponding maximum class K2_ max in the clustering space by counting the number of pixels of each region in the clustering space;
step ten, combining areas of non-maximum class, namely a target area and a shadow area;
step eleven, filling holes in the target area and the background area;
and step twelve, outputting the final segmentation mask and the target extraction result.
Further, in step four, X n 、Y n 、Z n Respectively 95.047, 100.0 and 108.883.
Further, in step five, the hyper-parameter c of the input data is 40.
Further, in step eight, w1, w2, and w3 are 0.8,0.9, and 1.3, respectively.
Meanwhile, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method.
Furthermore, the present invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.
Compared with the prior art, the method has the beneficial effects that:
1. in the self-adaptive target segmentation method based on the spectral characteristics in the remote sensing scene, a target area and a target shadow area are extracted through the spectral characteristics in remote sensing data, so that the remote sensing image is effectively segmented; meanwhile, the method can efficiently distinguish the target background area through the distance measurement function, so that the segmentation precision is improved.
2. The self-adaptive target segmentation method based on the spectral characteristics in the remote sensing scene effectively relieves the sharp edge of the target area and smoothes the segmentation edge of the target area by performing Gaussian filtering pretreatment on input data; meanwhile, the super-pixel region segmented by using the slic algorithm is mapped to the hsv color space and subjected to mean processing, so that the subsequent clustering precision based on the distance measurement function is improved.
3. The self-adaptive target segmentation method based on the spectral characteristics in the remote sensing scene can perform significance segmentation on the remote sensing target with the shadow area, is wide in application range, and performs unsupervised self-adaptive segmentation under the condition of small data volume.
Drawings
FIG. 1 is a flow chart of a method for segmenting a self-adaptive target in a remote sensing scene based on spectral characteristics according to the present invention;
FIG. 2 is a schematic illustration of a remote sensing image of a large-scale propeller military Aircraft in an OPT-Aircraft data set selected in accordance with an exemplary embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of the method of the present invention after segmentation of slic superpixels;
FIG. 4 is a diagram illustrating superpixels after mapping to HSV spectral space means using superpixel partition regions in an embodiment of the method of the present invention;
fig. 5 is a schematic diagram of object shadow background segmentation after quadratic clustering by using k _ means + + in the embodiment of the method of the present invention.
FIG. 6 is a schematic diagram of target shadow extraction using masks after hole filling in an exemplary embodiment of the present invention;
FIG. 7 is a comparative example of masks obtained using the existing k _ means clustering method;
fig. 8 is a schematic diagram of target shadow extraction using the mask of fig. 7 after using the conventional k _ means clustering method.
Detailed Description
The technical solution of the present invention will be clearly and completely described below with reference to the embodiment of the present invention and the accompanying drawings.
The invention provides a self-adaptive target segmentation method in a remote sensing scene based on spectral characteristics, which considers that a remote sensing image to be segmented is a three-channel image and has abundant color information, so that the classification precision is improved by combining color characteristics. And finally, accurately extracting a shadow area of the target while ensuring that the target is separated from the background, wherein the method specifically comprises the following steps: 1) Inputting remote sensing scene data; 2) Carrying out Gaussian filtering on the remote sensing scene data; 3) Performing HSV space conversion on the filtered data; 4) Performing Lab space conversion on the filtered data; 5) Calculating the number of the super-pixel segmentation areas; 6) Performing superpixel segmentation on the data of the Lab spatial domain by using an SLIC (narrow slice correlation coefficient); 7) Calculating HSV mean value mapping of each region after superpixel segmentation; 8) Performing quadratic clustering by using a distance measurement function defined by combining k-means + +; 9) Calculating the maximum number of classes; 10 Merge target shadow regions; 11 Hole filling; 12 Output a remotely sensed target segmentation mask. The method of the invention makes full use of the spectral information of the remote sensing image, and provides a distance measurement function aiming at the remote sensing target, and can extract the target area in a self-adaptive manner on the premise of no supervision, thereby improving the precision of target segmentation.
The self-adaptive target segmentation method based on the spectral characteristics in the remote sensing scene mainly segments the remote sensing target with the shadow, and finally extracts the target area with the shadow. As shown in fig. 2, a large-sized propeller military Aircraft in the OPT-Aircraft data set is taken as an example of remote sensing image object segmentation, and the final object is to extract an object region with object shadows as completely as possible. As shown in fig. 1, the method for adaptively segmenting the target in the remote sensing scene based on the spectral characteristics specifically includes the following steps:
step one, inputting a remote sensing target scene F to be segmented;
step two, carrying out Gaussian filtering on the remote sensing target scene F to obtain filtered data F _ gauge;
thirdly, performing HSV space conversion on the filtered data by using the following formula to obtain data F _ HSV, wherein the F _ HSV comprises values of three channels of H, S and V;
V＝max(R，G，B)
wherein, H, S and V are the values of three channels of the final HSV color space; r, G and B are three channel values of RGB space in the F _ gauge;
step four, respectively carrying out Lab space conversion on the data filtered in the step two by using the following two groups of formulas to obtain F _ Lab, wherein the F _ Lab comprises values of three channels L, a and b;
wherein L, a, b are values of three channels of the final LAB color space, X, Y, Z are calculated values after RGB conversion, X n ，Y n ，Z n Conversion coefficients for X, Y, Z, typically default to 95.047, 100.0, 108.883;
step five, calculating the super-pixel segmentation number K1 in the Lab space;
K1＝(w×h)/c
wherein, w and h are the length and width of the input data respectively, c is a hyper-parameter of the input data, and the default value is 40;
step six, processing the F _ Lab by using slic superpixel segmentation algorithm on the F _ Lab data according to the initialized segmentation number K1, segmenting n superpixel areas, and marking the n superpixel areas as L i I belongs to 1,2,3 \8230n; the specific results are shown in FIG. 3;
step seven, mapping the obtained super-pixel area to an HSV space, calculating the mean value of the super-pixel areas of the respective channels of the F _ HSV to obtain an F _ HSV _ mean which is formed by the following formula, wherein the specific result is shown in figure 4;
F_HSV_mean＝(f_h_mean，f_s_mean，f_v_mean)
wherein f _ h _ mean represents the pixel mean of the h channel; f _ s _ mean represents the pixel mean of the s channel; f _ v _ mean represents the pixel mean of the v channel; length (L) i ) Indicating the number of pixels in the ith super-pixel region; f _ h i (k) A k-th pixel value representing an h-channel in an i-th super-pixel region; f _ s i (k) A k-th pixel value representing the s-channel in the i-th super-pixel region; f _ v i (k) A k-th pixel value representing a v-channel in the i-th super-pixel region;
step eight, performing secondary clustering on the F _ HSV _ mean data by using k _ means + + in combination with a distance measurement function, wherein the default clustering number is 3, and the target, the shadow and the background are respectively represented; the distance metric function is as follows:
f_hsv＝(w1×f_h_mean，w2×f_s_mean，w3×f_v_mean)
wherein w1, w2 and w3 are weight coefficients of the three channels, and the default is 0.8,0.9 and 1.3; l is i,j Represents a distance between the ith pixel and the jth pixel;
denotes f _ hsv i Rank of (d);
denotes f _ hsv j Rank of (d); f _ hsv j A column vector representing that pixel values of the jth pixel constitute three dimensions; f _ hsv i A column vector representing three dimensions of pixel values of the ith pixel; f _ HSV represents data subjected to weight transformation on F _ HSV _ mean data;
step nine, searching a corresponding maximum class K2_ max in the clustering space by counting the number of pixels in each region in the clustering space;
step ten, merging the areas of non-maximum class, namely the target area and the shadow area, and the specific result is shown in fig. 5.
Step eleven, filling holes in the target area and the background area, wherein the specific result is shown in fig. 6;
and step twelve, outputting the final segmentation mask and the target extraction result.
Fig. 7 is a comparison example of masks obtained by using the conventional k _ means clustering method, fig. 8 is a schematic diagram of extracting a target shadow by using the masks of fig. 7 after using the k _ means clustering method, and fig. 6 is a schematic diagram of a target shadow region extracted by the method of the present invention.
Meanwhile, the embodiment of the invention also provides a computer readable storage medium for storing a program, and the program realizes the steps of the adaptive target segmentation method in the remote sensing scene based on the spectral characteristics when being executed. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the methods presented above in this description, when said program product is run on said terminal device.
A program product for implementing the above method, which may employ a portable compact disc read only memory (CD-ROM) and include program code, may be run on a terminal device, a computer device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Claims (6)
1. A self-adaptive target segmentation method in a remote sensing scene based on spectral characteristics is characterized by comprising the following steps:
step one, inputting a remote sensing target scene F to be segmented;
step two, carrying out Gaussian filtering on the remote sensing target scene F to obtain filtered data F _ gauge;
thirdly, performing HSV space conversion on the data filtered in the second step by using the following formula to obtain data F _ HSV, wherein the F _ HSV comprises values of three channels of H, S and V;
V＝max(R，G，B)
wherein, H, S and V are the values of three channels of the final HSV color space; r, G and B are three channel values of RGB space in F _ gauge;
performing Lab space conversion on the data filtered in the step two by using the following formula to obtain F _ Lab, wherein the F _ Lab comprises values of three channels of L, a and b;
wherein L, a, b are the values of the three channels of the final LAB color space; x, Y, Z are calculated values after RGB conversion; x n ，Y n ，Z n Conversion coefficients of X, Y and Z;
step five, calculating the initial segmentation number K1 of the slic super pixel in the Lab space;
K1＝(w×h)/c
wherein, w and h are the length and width of the input data respectively, and c is the hyper-parameter of the input data;
step six, processing the F _ Lab data by using a slic superpixel segmentation algorithm according to the initialized segmentation number K1, segmenting n superpixel areas, and marking the n superpixel areas as L i ，i∈1，2，3…n；
Step seven, mapping the super-pixel regions obtained in the step six to an HSV space, and calculating the mean values of the super-pixel regions of the respective channels of the F _ HSV to obtain an F _ HSV _ mean;
F_HSV_mean＝(f_h_mean，f_s_mean，f_v_mean)
wherein f _ h _ mean represents the pixel mean of the h channel; f _ s _ mean represents the pixel mean of the s channel; f _ v _ mean represents the pixel mean of the v channel; length (L) i ) Indicating the number of pixels in the ith super-pixel region; f _ h i (k) A k-th pixel value representing an h-channel in an i-th super-pixel region; f _ s i (k) A k-th pixel value representing the s-channel in the i-th super-pixel region; f _ v i (k) A k-th pixel value representing a v-channel in the i-th super-pixel region;
step eight, performing secondary clustering on the F _ HSV _ mean data by using a distance measurement function and k _ means + +, wherein the default clustering number is 3, and the default clustering number represents a target, a shadow and a background respectively;
the distance metric function is as follows:
f_hsv＝(w1×f_h_mean，w2×f_s_mean，w3×f_v_mean)
wherein, w1, w2 and w3 are weight coefficients of three channels; l is i,j Representing a distance between an ith pixel and a jth pixel;
denotes f _ hsv i Rank of (d);
denotes f _ hsv j Rank of (d); f _ hsv j A column vector representing that pixel values of the jth pixel constitute three dimensions; f _ hsv i A column vector representing three dimensions of pixel values of the ith pixel; f _ HSV represents data subjected to weight transformation on F _ HSV _ mean data;
step nine, searching a corresponding maximum class K2_ max in the clustering space by counting the number of pixels in each region in the clustering space;
step ten, combining areas of non-maximum class, namely a target area and a shadow area;
step eleven, filling holes in the target area and the background area;
and step twelve, outputting the final segmentation mask and the target extraction result.
2. The method for segmenting the adaptive target in the remote sensing scene based on the spectral characteristics as claimed in claim 1, characterized in that: in step four, X n 、Y n 、Z n Respectively 95.047, 100.0 and 108.883.
3. The adaptive target segmentation method in the remote sensing scene based on the spectral characteristics as claimed in claim 2, wherein: in step five, the hyper-parameter c of the input data is 40.
4. The method for adaptively segmenting the target in the remote sensing scene based on the spectral characteristics as claimed in claim 1,2 or 3, wherein: in the step eight, w1, w2 and w3 are respectively 0.8,0.9 and 1.3.
5. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 4.
6. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein: the processor, when executing the computer program, performs the steps of the method of any of claims 1 to 4.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127791A (en) * | 2016-07-06 | 2016-11-16 | 武汉大学 | A kind of contour of building line drawing method of aviation remote sensing image |
| CN107886507A (en) * | 2017-11-14 | 2018-04-06 | 长春工业大学 | A kind of salient region detecting method based on image background and locus |
| CN109087330A (en) * | 2018-06-08 | 2018-12-25 | 中国人民解放军军事科学院国防科技创新研究院 | It is a kind of based on by slightly to the moving target detecting method of smart image segmentation |
| CN110120042A (en) * | 2019-05-13 | 2019-08-13 | 哈尔滨工业大学 | A kind of crop map based on SLIC super-pixel and automatic threshold segmentation is as pest and disease damage method for extracting region |
| CN110163239A (en) * | 2019-01-25 | 2019-08-23 | 太原理工大学 | A kind of Weakly supervised image, semantic dividing method based on super-pixel and condition random field |
| CN110599517A (en) * | 2019-08-30 | 2019-12-20 | 广东工业大学 | Target feature description method based on local feature and global HSV feature combination |
-
2020
- 2020-07-28 CN CN202010737865.1A patent/CN112017158B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127791A (en) * | 2016-07-06 | 2016-11-16 | 武汉大学 | A kind of contour of building line drawing method of aviation remote sensing image |
| CN107886507A (en) * | 2017-11-14 | 2018-04-06 | 长春工业大学 | A kind of salient region detecting method based on image background and locus |
| CN109087330A (en) * | 2018-06-08 | 2018-12-25 | 中国人民解放军军事科学院国防科技创新研究院 | It is a kind of based on by slightly to the moving target detecting method of smart image segmentation |
| CN110163239A (en) * | 2019-01-25 | 2019-08-23 | 太原理工大学 | A kind of Weakly supervised image, semantic dividing method based on super-pixel and condition random field |
| CN110120042A (en) * | 2019-05-13 | 2019-08-13 | 哈尔滨工业大学 | A kind of crop map based on SLIC super-pixel and automatic threshold segmentation is as pest and disease damage method for extracting region |
| CN110599517A (en) * | 2019-08-30 | 2019-12-20 | 广东工业大学 | Target feature description method based on local feature and global HSV feature combination |
Non-Patent Citations (2)
| Title |
|---|
| 《Colour based semantic image segmentation and classification for unmanned ground operations》;Mattehew C et al;《IEEE》;20160704;全文 * |
| 《融合深度信息的图像分割算法研究》;李应彬;《中国优秀硕士学位论文全文数据库 信息科技辑》;20140915;全文 * |
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