CN106910202B - Image segmentation method and system for ground object of remote sensing image - Google Patents
Image segmentation method and system for ground object of remote sensing image Download PDFInfo
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Abstract
The invention relates to an image segmentation method and system for a ground object of a remote sensing image, wherein the method comprises the following steps: s1: putting the remote sensing image into a full convolution network, wherein the full convolution network comprises a plurality of convolution layer groups, a plurality of deconvolution layers and a CRF model layer which are sequentially arranged, and the convolution layer groups comprise convolution layers and loose convolution layers which are alternately arranged; s2: carrying out coordinate point marking on the remote sensing image through a plurality of convolution layer groups and a plurality of deconvolution layers to obtain a ground feature classification probability map, wherein different ground features in the ground feature classification probability map have different coordinate point colors and coordinate point depths; s3: and classifying all coordinate points in the ground feature classification probability map according to the coordinate point colors and the coordinate point depths to obtain segmentation images of different ground features. The invention has the beneficial effects that: according to the technical scheme, the color and the depth of the remote sensing image are added into image recognition and segmentation, color information and depth information are comprehensively analyzed, and fine cutting of the image is achieved through a CRF model layer.
Description
Technical Field
The invention relates to the technical field of remote sensing image processing, in particular to an image segmentation method and system for ground objects of a remote sensing image.
Background
The method is a key technology of a geographic information system for dividing the edges of the ground objects of the remote sensing images, and has very important functions in the fields of land planning, disaster prevention and control, unmanned aerial vehicles, satellites, unmanned ships and resource monitoring. In the traditional method, only two-dimensional data is considered, only the relation between the coordinate point color and the coordinate point position of an image is considered in the segmentation process, and the three-dimensional remote sensing image cannot be segmented by effectively utilizing all information.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in the traditional method, only two-dimensional data is considered, only the relation between the coordinate point color and the coordinate point position of an image is considered in the segmentation process, and the three-dimensional remote sensing image cannot be segmented by effectively utilizing all information.
The technical scheme for solving the technical problems is as follows:
an image segmentation method for ground objects of remote sensing images comprises the following steps:
s1: putting the remote sensing image into a full convolution network, wherein the full convolution network comprises a plurality of convolution layer groups, a plurality of deconvolution layers and a CRF model layer which are sequentially arranged, and the convolution layer groups comprise convolution layers and loose convolution layers which are alternately arranged;
s2: carrying out coordinate point marking on the remote sensing image through the plurality of convolution layer groups and the plurality of deconvolution layers to obtain a ground feature classification probability map, wherein different ground features in the ground feature classification probability map have different coordinate point colors and coordinate point depths;
s3: and classifying all coordinate points in the ground feature classification probability map according to the coordinate point colors and the coordinate point depths to obtain segmentation images of different ground features.
The invention has the beneficial effects that: according to the technical scheme, the color and the depth of the remote sensing image are added into image recognition and segmentation, color information and depth information are comprehensively analyzed, a CRF model layer is used as an upper sampling layer of a deep learning neural network, and fine cutting of the image is achieved on the basis of rough segmentation output by the network.
On the basis of the technical scheme, the invention can be further improved as follows.
Preferably, the step S2 includes:
s21: fusing the image of the remote sensing image marked by at least one convolution layer group coordinate point with the image marked by all the convolution layer groups and at least one deconvolution layer coordinate point for multiple times to obtain a fused image;
s22: and fusing the remote sensing image and the fused image for multiple times after the remote sensing image and the fused image are marked by at least one deconvolution layer coordinate point to obtain a ground feature classification probability map.
The beneficial effect of adopting the further scheme is that: the full convolution network replaces the full connection of the traditional network with convolution, adds an anti-convolution layer, and blends the results of the first layers of the network with the final result of the network, thereby obtaining more image information.
Preferably, the step S3 includes:
s31: inputting the coordinate point color into an energy function of the CRF model layer to calculate to obtain first energy values of all coordinate points in the ground feature classification probability map;
s32: inputting the depth of the coordinate points into an energy function of the CRF model layer to calculate to obtain second energy values of all the coordinate points in the terrain classification probability map;
s33: calculating to obtain final energy values of all coordinate points according to the first energy value and the second energy value;
s34: and classifying all coordinate points in the ground feature classification probability map according to the final energy value to obtain segmentation images of different ground features.
The beneficial effect of adopting the further scheme is that: the CRF algorithm and the Gibbs energy function are improved, the color and the depth of the coordinate point are used as judgment bases, the coordinate point is placed in the energy function, the coordinate point is correctly classified through iteration, the value of the energy function is reduced, and image cutting is achieved.
An image segmentation system for remote sensing image ground objects, comprising:
the remote sensing image acquisition module is used for acquiring a remote sensing image, and comprises an input module, an output module and a processing module, wherein the input module is used for inputting the remote sensing image into a full convolution network, the full convolution network comprises a plurality of convolution layer groups, a plurality of deconvolution layers and a CRF (cyclic redundancy check) model layer which are sequentially arranged, and the convolution layer groups comprise convolution layers and loose convolution layers which are alternately arranged;
the marking module is used for marking coordinate points on the remote sensing image through the plurality of convolution layer groups and the plurality of deconvolution layers to obtain a ground feature classification probability map, wherein different ground features in the ground feature classification probability map have different coordinate point colors and coordinate point depths;
and the classification module is used for classifying all coordinate points in the ground feature classification probability map according to the coordinate point colors and the coordinate point depths to obtain segmentation images of different ground features.
Preferably, the marking module comprises:
the first fusion submodule is used for fusing the image of the remote sensing image marked by at least one convolution layer group coordinate point with the image marked by all the convolution layer groups and at least one deconvolution layer coordinate point for multiple times to obtain a fused image;
and the second fusion submodule is used for fusing the remote sensing image and the image of the fused image after the at least one deconvolution layer coordinate point mark for multiple times to obtain a ground feature classification probability map.
Preferably, the classification module comprises:
the first calculation submodule is used for inputting the coordinate point color into an energy function of the CRF model layer to calculate and obtain first energy values of all coordinate points in the ground feature classification probability map;
the second calculation submodule is used for inputting the depth of the coordinate point into an energy function of the CRF model layer to calculate and obtain second energy values of all coordinate points in the ground feature classification probability map;
the third calculation submodule is used for calculating to obtain the final energy values of all the coordinate points according to the first energy value and the second energy value;
and the classification submodule is used for classifying all coordinate points in the ground feature classification probability map according to the final energy value to obtain the segmentation images of different ground features.
Drawings
Fig. 1 is a schematic flow chart of an image segmentation method for a ground object of a remote sensing image according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of an image segmentation method for a ground object in a remote sensing image according to another embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for segmenting an image of a ground object in a remote sensing image according to another embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an image segmentation system for a ground object in a remote sensing image according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an image segmentation system for a ground object in a remote sensing image according to another embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, in an embodiment, an image segmentation method for a ground object in a remote sensing image is provided, which includes:
s1: putting the remote sensing image into a full convolution network, wherein the full convolution network comprises a plurality of convolution layer groups, a plurality of deconvolution layers and a CRF model layer which are sequentially arranged, and the convolution layer groups comprise convolution layers and loose convolution layers which are alternately arranged;
s2: carrying out coordinate point marking on the remote sensing image through a plurality of convolution layer groups and a plurality of deconvolution layers to obtain a ground feature classification probability map, wherein different ground features in the ground feature classification probability map have different coordinate point colors and coordinate point depths;
s3: and classifying all coordinate points in the ground feature classification probability map according to the coordinate point colors and the coordinate point depths to obtain segmentation images of different ground features.
It should be understood that, in this embodiment, the color and the depth of the remote sensing image are added into the image recognition and segmentation, the color information and the depth information are comprehensively analyzed, the CRF model layer is used as an upsampling layer of the deep learning neural network, and the fine cutting of the image is realized on the basis of the coarse segmentation of the network output. The CRF (conditional random field) combines the characteristics of a maximum entropy model and a hidden Markov model, is an undirected graph model, and has good effect in sequence labeling tasks such as word segmentation, part of speech labeling, named entity recognition and the like in recent years. CRF is a typical discriminant model.
Specifically, in this embodiment, first, a conventional full convolutional network is improved, a convolutional layer is used instead of a full link layer, and an image is up-sampled by using an inverse convolutional layer and a CRF model layer after the convolutional layer; then, the image to be segmented is placed in the improved full convolution network, coordinate point marking is carried out on the remote sensing image through the seven layers of convolution layers and the three layers of deconvolution layers, different colors and depths are marked on the coordinate points, finally, all coordinate points in the image after the coordinate point marking are subjected to iterative classification through a CRF model layer according to the colors and the depths of the coordinate points, and fine segmentation is carried out to obtain segmented images of different ground objects.
As shown in fig. 2, in another embodiment, step S2 in fig. 1 includes:
s21: fusing the image of the remote sensing image marked by at least one convolution layer group coordinate point with the image marked by all convolution layer groups and at least one deconvolution layer coordinate point for multiple times to obtain a fused image;
s22: and fusing the remote sensing image and the fused image for multiple times after the remote sensing image and the fused image are marked by at least one deconvolution layer coordinate point to obtain a ground feature classification probability map.
It should be understood that in this embodiment, the full convolutional network replaces the fully connected layer of the conventional network with the convolutional layer, adds the anti-convolutional layer, and blends the results of the first layers of the network with the final result of the network, so as to obtain more image information.
As shown in fig. 3, in another embodiment, step S3 in fig. 1 includes:
s31: inputting the color of the coordinate point into an energy function of a CRF model layer to calculate to obtain first energy values of all the coordinate points in the ground feature classification probability map;
s32: inputting the depth of the coordinate points into an energy function of a CRF model layer to calculate to obtain second energy values of all the coordinate points in the ground feature classification probability map;
s33: calculating to obtain final energy values of all coordinate points according to the first energy value and the second energy value;
s34: and classifying all coordinate points in the ground feature classification probability map according to the final energy value to obtain segmentation images of different ground features.
It should be understood that, in this embodiment, the color and the depth of the coordinate point are used as the judgment basis, the coordinate point is put into the improved energy function, the coordinate point is correctly classified through iteration, the value of the energy function is reduced, and the image cutting is realized.
Specifically, in this embodiment, a first energy value corresponding to a color of a coordinate point and a second energy value corresponding to a depth of the coordinate point of all coordinate points in the feature classification probability map are respectively calculated according to the color and the depth of the coordinate point and through an energy function of a CRF model layer, the first energy value and the second energy value are added to obtain a total energy of each coordinate point, and the feature classification probability map is accurately segmented according to the total energy of each coordinate point to obtain a feature segmentation image. Modified gibbs energy function: e (p) ═ e (z) + e (d), where e (p) is the total energy of the coordinate points, e (z) is the energy of segmentation according to the color of the coordinate points, e (d) is the energy of segmentation according to the depth of the coordinate points, e (d) is implemented in a similar way to e (z), except that the depth of the coordinate points replaces the color of the coordinate points. The implementation mode of E (z) is as follows:
As shown in fig. 4, in an embodiment, there is provided an image segmentation system for a ground feature of a remote sensing image, including:
the remote sensing image acquisition module comprises an input module 1, a full convolution network and a remote sensing image acquisition module, wherein the input module 1 is used for inputting a remote sensing image into the full convolution network, the full convolution network comprises a plurality of convolution layer groups, a plurality of deconvolution layers and CRF (cross domain gradient) model layers which are sequentially arranged, and the convolution layer groups comprise convolution layers and sparse convolution layers which are alternately arranged;
the marking module 2 is used for marking coordinate points on the remote sensing image through the plurality of convolution layer groups and the plurality of deconvolution layers to obtain a ground feature classification probability map, wherein different ground features in the ground feature classification probability map have different coordinate point colors and coordinate point depths;
and the classification module 3 is used for classifying all coordinate points in the ground feature classification probability map according to the coordinate point colors and the coordinate point depths to obtain segmentation images of different ground features.
As shown in fig. 5, in another embodiment, the marking module 2 in fig. 4 includes:
the first fusion submodule 21 is configured to perform multiple fusion on an image of the remote sensing image marked by at least one convolution layer group coordinate point and an image marked by all convolution layer groups and at least one deconvolution layer coordinate point to obtain a fusion image;
and the second fusion submodule 22 is used for fusing the remote sensing image and the image of the fused image after the remote sensing image and the fused image are marked by at least one deconvolution layer coordinate point for multiple times to obtain a ground feature classification probability map.
As shown in fig. 5, in another embodiment, the classification module 3 in fig. 4 includes:
the first calculating submodule 31 is configured to input the coordinate point color into an energy function of the CRF model layer to calculate first energy values of all coordinate points in the ground feature classification probability map;
the second calculating submodule 32 is used for inputting the depth of the coordinate point into an energy function of the CRF model layer to calculate and obtain second energy values of all coordinate points in the ground feature classification probability map;
a third calculating submodule 33, configured to calculate final energy values of all coordinate points according to the first energy value and the second energy value;
and the classification submodule 34 is configured to classify all coordinate points in the ground feature classification probability map according to the final energy value, so as to obtain segmented images of different ground features.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. An image segmentation method for ground features of remote sensing images is characterized by comprising the following steps:
s1: putting the remote sensing image into a full convolution network, wherein the full convolution network comprises a plurality of convolution layer groups, a plurality of deconvolution layers and a CRF model layer which are sequentially arranged, and the convolution layer groups comprise convolution layers and loose convolution layers which are alternately arranged;
s2: carrying out coordinate point marking on the remote sensing image through the plurality of convolution layer groups and the plurality of deconvolution layers to obtain a ground feature classification probability map, wherein different ground features in the ground feature classification probability map have different coordinate point colors and coordinate point depths;
s3: classifying all coordinate points in the ground feature classification probability map according to the coordinate point colors and the coordinate point depths to obtain segmentation images of different ground features;
the step S3 includes:
s31: inputting the coordinate point color into an energy function of the CRF model layer to calculate to obtain first energy values of all coordinate points in the ground feature classification probability map;
s32: inputting the depth of the coordinate points into an energy function of the CRF model layer to calculate to obtain second energy values of all the coordinate points in the terrain classification probability map;
s33: calculating to obtain final energy values of all coordinate points according to the first energy value and the second energy value;
s34: and classifying all coordinate points in the ground feature classification probability map according to the final energy value to obtain segmentation images of different ground features.
2. The image segmentation method according to claim 1, wherein the step S2 includes:
s21: fusing the image of the remote sensing image marked by at least one convolution layer group coordinate point with the image marked by all the convolution layer groups and at least one deconvolution layer coordinate point for multiple times to obtain a fused image;
s22: and fusing the remote sensing image and the fused image for multiple times after the remote sensing image and the fused image are marked by at least one deconvolution layer coordinate point to obtain a ground feature classification probability map.
3. An image segmentation system for remote sensing image ground objects, comprising:
the remote sensing image acquisition system comprises an input module (1) and a remote sensing image acquisition module, wherein the input module is used for inputting a remote sensing image into a full convolution network, the full convolution network comprises a plurality of convolution layer groups, a plurality of deconvolution layers and CRF (cyclic redundancy check) model layers which are sequentially arranged, and the convolution layer groups comprise convolution layers and sparse convolution layers which are alternately arranged;
the marking module (2) is used for marking coordinate points on the remote sensing image through the convolution layer groups and the deconvolution layers to obtain a ground feature classification probability map, wherein different ground features in the ground feature classification probability map have different coordinate point colors and coordinate point depths;
the classification module (3) is used for classifying all coordinate points in the ground feature classification probability map according to the coordinate point colors and the coordinate point depths to obtain segmentation images of different ground features;
the classification module (3) comprises:
the first calculating submodule (31) is used for inputting the coordinate point color into an energy function of the CRF model layer to calculate to obtain first energy values of all coordinate points in the terrain classification probability map;
the second calculation submodule (32) is used for inputting the coordinate point depth into an energy function of the CRF model layer to calculate to obtain second energy values of all coordinate points in the terrain classification probability map;
a third calculation submodule (33) for calculating a final energy value of all coordinate points according to the first energy value and the second energy value;
and the classification submodule (34) is used for classifying all coordinate points in the ground feature classification probability map according to the final energy value to obtain segmentation images of different ground features.
4. The image segmentation system according to claim 3, characterized in that the labeling module (2) comprises:
the first fusion submodule (21) is used for fusing the image of the remote sensing image marked by at least one convolution layer group coordinate point with the image marked by all the convolution layer groups and at least one deconvolution layer coordinate point for multiple times to obtain a fused image;
and the second fusion submodule (22) is used for fusing the remote sensing image and the image of the fused image after being marked by at least one deconvolution layer coordinate point for multiple times to obtain a ground feature classification probability map.
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| CN107527352B (en) * | 2017-08-09 | 2020-07-07 | 中国电子科技集团公司第五十四研究所 | Remote sensing ship target contour segmentation and detection method based on deep learning FCN network |
| CN108537824B (en) * | 2018-03-15 | 2021-07-16 | 上海交通大学 | Feature map enhanced network structure optimization method based on alternating deconvolution and convolution |
| CN108710863A (en) * | 2018-05-24 | 2018-10-26 | 东北大学 | Unmanned plane Scene Semantics dividing method based on deep learning and system |
| CN111582004A (en) * | 2019-02-15 | 2020-08-25 | 阿里巴巴集团控股有限公司 | Target area segmentation method and device in ground image |
| CN109918449B (en) * | 2019-03-16 | 2021-04-06 | 中国农业科学院农业资源与农业区划研究所 | Internet of things-based agricultural disaster information remote sensing extraction method and system |
| CN111242132A (en) * | 2020-01-07 | 2020-06-05 | 广州赛特智能科技有限公司 | Outdoor road scene semantic segmentation method and device, electronic equipment and storage medium |
| CN112419266B (en) * | 2020-11-23 | 2022-09-30 | 山东建筑大学 | Remote sensing image change detection method based on ground surface coverage category constraint |
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