CN106910202A - The image partition method and system of a kind of remote sensing images atural object - Google Patents
The image partition method and system of a kind of remote sensing images atural object Download PDFInfo
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- G06T2207/10—Image acquisition modality
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Abstract
The present invention relates to the image partition method and system of a kind of remote sensing images atural object, method includes：S1：Remote sensing images are put into full convolutional network, full convolutional network includes the multiple convolutional layer groups, multiple warp laminations and the CRF model layers that are arranged in order, wherein, convolutional layer group includes the convolutional layer and lax convolutional layer that are alternately arranged；S2：Coordinate points mark is carried out to remote sensing images by multiple convolutional layer groups and multiple warp laminations, terrain classification probability graph is obtained, wherein, different atural objects have different coordinate points colors and coordinate points depth in terrain classification probability graph；S3：The all coordinate points in atural object class probability figure are classified according to coordinate points color and coordinate points depth, obtains the segmentation figure picture of different atural objects.The beneficial effects of the invention are as follows：The technical program will be during the color of remote sensing images and depth add image recognition and segmentation, and comprehensive analysis colouring information and depth information realize the fine cut of image by CRF model layers.
Description
Technical field
The present invention relates to technical field of remote sensing image processing, more particularly to a kind of image partition method of remote sensing images atural object
And system.
Background technology
It is the key technology of GIS-Geographic Information System to the atural object edge segmentation of remote sensing images, prevents in the reallocation of land, disaster
Control, unmanned plane, satellite, unmanned boat and monitoring resource field have a very important role.Conventional approach only considers 2-D data,
Only consider in cutting procedure image coordinate points color and coordinate points position between relation, for 3 D Remote Sensing image simultaneously
Effectively image segmentation can not be carried out using full detail.
The content of the invention
The technical problems to be solved by the invention are：Conventional approach only considers 2-D data, only considers in cutting procedure
Relation between the coordinate points color of image and coordinate points position, can not effectively using all letters for 3 D Remote Sensing image
Breath carries out image segmentation.
The technical scheme that the present invention solves above-mentioned technical problem is as follows：
A kind of image partition method of remote sensing images atural object, including：
S1：Remote sensing images are put into full convolutional network, multiple convolutional layer groups that the full convolutional network includes being arranged in order,
Multiple warp laminations and CRF model layers, wherein, the convolutional layer group includes the convolutional layer and lax convolutional layer that are alternately arranged；
S2：Coordinate points mark is carried out to the remote sensing images by multiple convolutional layer groups and multiple warp laminations
Note, obtains terrain classification probability graph, wherein, in the terrain classification probability graph different atural objects have different coordinate points colors and
Coordinate points depth；
S3：According to the coordinate points color and the coordinate points depth to all coordinates in the terrain classification probability graph
Point is classified, and obtains the segmentation figure picture of different atural objects.
The beneficial effects of the invention are as follows：The color of remote sensing images and depth are added image recognition and segmentation by the technical program
In, comprehensive analysis colouring information and depth information, using CRF model layers as deep learning neutral net up-sampling layer, in net
On the basis of the coarse segmentation of network output, the fine cut of image is realized.
On the basis of above-mentioned technical proposal, the present invention can also do following improvement.
Preferably, the step S2 includes：
S21：By the remote sensing images by the image after convolutional layer group coordinate points mark described at least one and by institute
There is the image after the convolutional layer group and warp lamination coordinate points mark described at least one repeatedly to be merged, obtain fusion figure
Picture；
S22：By the remote sensing images with the fused images by after warp lamination coordinate points mark described at least one
Image repeatedly merged, obtain terrain classification probability graph.
Beneficial effect using above-mentioned further scheme is：The full convolutional network is substituted for the full connection of legacy network
Convolution, add warp lamination, and the final result of result several layers of before network and network is carried out it is warm, can obtain more
Image information.
Preferably, the step S3 includes：
S31：The energy function that the coordinate points color is input into the CRF model layers is calculated the terrain classification general
First energy value of all coordinate points in rate figure；
S32：The energy function that the coordinate points depth is input into the CRF model layers is calculated the terrain classification general
Second energy value of all coordinate points in rate figure；
S33：The final energy value of all coordinate points is calculated according to first energy value and second energy value；
S34：The all coordinate points in the terrain classification probability graph are classified according to the final energy value, is obtained
The segmentation figure picture of different atural objects.
Beneficial effect using above-mentioned further scheme is：CRF algorithms and Gibbs energy flow function are improved, with coordinate points
Color and depth are put into energy function as basis for estimation, and coordinate points are correctly classified by iteration, reduce energy letter
Several values, realizes that image cuts.
A kind of image segmentation system of remote sensing images atural object, including：
Module is put into, for remote sensing images to be put into full convolutional network, the full convolutional network is more including what is be arranged in order
Individual convolutional layer group, multiple warp lamination and CRF model layers, wherein, the convolutional layer group includes the convolutional layer being alternately arranged and dilute
Loose winding lamination；
Mark module, for being carried out to the remote sensing images by multiple convolutional layer groups and multiple warp laminations
Coordinate points are marked, and obtain terrain classification probability graph, wherein, different atural objects have different coordinates in the terrain classification probability graph
Point color and coordinate points depth；
Sort module, for according to the coordinate points color and the coordinate points depth in the terrain classification probability graph
All coordinate points classified, obtain the segmentation figure picture of different atural objects.
Preferably, the mark module includes：
First fusion submodule, for by the remote sensing images by described at least one convolutional layer group coordinate points mark after
Image carried out repeatedly with the image after warp lamination coordinate points mark by all convolutional layer groups and described at least one
Fusion, obtains fused images；
Second fusion submodule, for by the remote sensing images with the fused images by deconvolution described at least one
Image after layer coordinate points mark is repeatedly merged, and obtains terrain classification probability graph.
Preferably, the sort module includes：
First calculating sub module, the energy function for the coordinate points color to be input into the CRF model layers is calculated
To the first energy value of all coordinate points in the terrain classification probability graph；
Second calculating sub module, the energy function for the coordinate points depth to be input into the CRF model layers is calculated
To the second energy value of all coordinate points in the terrain classification probability graph；
3rd calculating sub module, for being calculated all coordinates according to first energy value and second energy value
The final energy value of point；
Classification submodule, for being clicked through to all coordinates in the terrain classification probability graph according to the final energy value
Row classification, obtains the segmentation figure picture of different atural objects.
Brief description of the drawings
Fig. 1 is a kind of schematic flow sheet of the image partition method of remote sensing images atural object provided in an embodiment of the present invention；
A kind of flow of the image partition method of remote sensing images atural object that Fig. 2 is provided for another embodiment of the present invention is illustrated
Figure；
A kind of flow of the image partition method of remote sensing images atural object that Fig. 3 is provided for another embodiment of the present invention is illustrated
Figure；
Fig. 4 is a kind of structural representation of the image segmentation system of remote sensing images atural object provided in an embodiment of the present invention；
A kind of structural representation of the image segmentation system of remote sensing images atural object that Fig. 5 is provided for another embodiment of the present invention
Figure.
Specific embodiment
Principle of the invention and feature are described below in conjunction with accompanying drawing, example is served only for explaining the present invention, and
It is non-for limiting the scope of the present invention.
As shown in figure 1, in embodiment, there is provided a kind of image partition method of remote sensing images atural object, including：
S1：Remote sensing images are put into full convolutional network, full convolutional network includes the multiple convolutional layer groups, the multiple that are arranged in order
Warp lamination and CRF model layers, wherein, convolutional layer group includes the convolutional layer and lax convolutional layer that are alternately arranged；
S2：Coordinate points mark is carried out to remote sensing images by multiple convolutional layer groups and multiple warp laminations, atural object point is obtained
Class probability graph, wherein, different atural objects have different coordinate points colors and coordinate points depth in terrain classification probability graph；
S3：The all coordinate points in atural object class probability figure are classified according to coordinate points color and coordinate points depth,
Obtain the segmentation figure picture of different atural objects.
It should be understood that in the embodiment, during the color of remote sensing images and depth are added image recognition and are split, comprehensive analysis
Colouring information and depth information, using CRF model layers as deep learning neutral net up-sampling layer, network output rough segmentation
On the basis of cutting, the fine cut of image is realized.CRF (conditional random field algorithm, condition random
) it is a kind of undirected graph model, in recent years in participle, part of speech the characteristics of combine maximum entropy model and hidden Markov model
Good effect is achieved in the sequence labelling task such as mark and name Entity recognition.CRF is a typical discriminative model.
Specifically, in the embodiment, first, traditional full convolutional network is improved, full connection is replaced using convolutional layer
Layer, is up-sampled using warp lamination and CRF model layers after convolutional layer to image；Then, image to be split is put into this
In full convolutional network after improvement, coordinate points mark is carried out to remote sensing images by seven layers of convolutional layer and three layers of warp lamination, given
Coordinate points put on different colours and depth, finally, according to coordinate points color and depth after CRF model layers are marked to coordinate points
Image in all coordinate points be iterated classification, carry out fine segmentation, obtain the segmentation figure picture of different atural objects.
As shown in Fig. 2 in another embodiment, the step S2 in Fig. 1 includes：
S21：By remote sensing images by the image after at least one convolutional layer group coordinate points mark and by all convolutional layers
Image after group and at least one warp lamination coordinate points mark is repeatedly merged, and obtains fused images；
S22：Remote sensing images and fused images are carried out by the image after at least one warp lamination coordinate points mark many
Secondary fusion, obtains terrain classification probability graph.
It should be understood that in the embodiment, the full articulamentum of legacy network has been substituted for convolutional layer by the full convolutional network, addition
Warp lamination, and the final result of result several layers of before network and network is carried out warm, more image informations can be obtained.
As shown in figure 3, in another embodiment, the step S3 in Fig. 1 includes：
S31：The energy function of coordinate points color input CRF model layers is calculated all in terrain classification probability graph
First energy value of coordinate points；
S32：The energy function of coordinate points depth input CRF model layers is calculated all in terrain classification probability graph
Second energy value of coordinate points；
S33：The final energy value of all coordinate points is calculated according to the first energy value and the second energy value；
S34：The all coordinate points in atural object class probability figure are classified according to final energy value, obtains different atural objects
Segmentation figure picture.
It should be understood that in the embodiment, using coordinate points color and depth as basis for estimation, being put into energy function after improvement
In, coordinate points are correctly classified by iteration, the value of energy function is reduced, realize that image cuts.
Specifically, in the embodiment, distinguished by the energy function of CRF model layers according to coordinate points color and depth respectively
The all coordinate points being calculated in terrain classification probability graph corresponding to the first energy value of coordinate points color and corresponding to seat
Second energy value of punctuate depth, the first energy value is added the gross energy for obtaining each coordinate points with the second energy value, according to
The gross energy of each coordinate points carries out Accurate Segmentation to atural object class probability figure, obtains atural object segmentation figure picture.Ji Bu after improvement
This energy function：E (p)=E (z)+E (d), wherein, E (p) is coordinate points gross energy, and E (z) is divided according to coordinate points color
The energy for cutting, E (d) is the energy split according to coordinate points depth, the implementation of E (d) ground similar to E (z), different
Side is to replace coordinate points color with coordinate points depth.The implementation of E (z) is：
ziIt is i-th value of coordinate points, its composition is mainly two large divisions,
It is Part I before plus sige, is single coordinate points primary power function；Part II is phase between coordinate points and surrounding coordinate points
Like performance amount.By iteration, coordinate points are included into correct classification by CRF model layers, constantly reduce the energy value of energy function, from
And realize correct segmentation.
As shown in figure 4, in embodiment, there is provided a kind of image segmentation system of remote sensing images atural object, including：
Module 1 is put into, for remote sensing images to be put into full convolutional network, full convolutional network includes the multiple volumes being arranged in order
Lamination group, multiple warp laminations and CRF model layers, wherein, convolutional layer group includes the convolutional layer and lax convolutional layer that are alternately arranged；
Mark module 2, for carrying out coordinate points mark to remote sensing images by multiple convolutional layer groups and multiple warp laminations,
Terrain classification probability graph is obtained, wherein, different atural objects have different coordinate points color and coordinate points in terrain classification probability graph
Depth；
Sort module 3, for according to coordinate points color and coordinate points depth to all coordinates in atural object class probability figure
Point is classified, and obtains the segmentation figure picture of different atural objects.
As shown in figure 5, in another embodiment, the mark module 2 in Fig. 4 includes：
First fusion submodule 21, for the image by remote sensing images after at least one convolutional layer group coordinate points are marked
Repeatedly merged with by the image after all convolutional layer groups and at least one warp lamination coordinate points mark, obtained fusion figure
Picture；
Second fusion submodule 22, for by remote sensing images and fused images by least one warp lamination coordinate points mark
Image after note is repeatedly merged, and obtains terrain classification probability graph.
As shown in figure 5, in another embodiment, the sort module 3 in Fig. 4 includes：
First calculating sub module 31, for the energy function of coordinate points color input CRF model layers to be calculated into atural object
First energy value of all coordinate points in class probability figure；
Second calculating sub module 32, for the energy function of coordinate points depth input CRF model layers to be calculated into atural object
Second energy value of all coordinate points in class probability figure；
3rd calculating sub module 33, for being calculated all coordinate points most according to the first energy value and the second energy value
Whole energy value；
Classification submodule 34, for being divided all coordinate points in atural object class probability figure according to final energy value
Class, obtains the segmentation figure picture of different atural objects.
The foregoing is only presently preferred embodiments of the present invention, be not intended to limit the invention, it is all it is of the invention spirit and
Within principle, any modification, equivalent substitution and improvements made etc. should be included within the scope of the present invention.
Claims (6)
1. a kind of image partition method of remote sensing images atural object, it is characterised in that including：
S1：Remote sensing images are put into full convolutional network, the full convolutional network includes the multiple convolutional layer groups, the multiple that are arranged in order
Warp lamination and CRF model layers, wherein, the convolutional layer group includes the convolutional layer and lax convolutional layer that are alternately arranged；
S2：Coordinate points mark is carried out to the remote sensing images by multiple convolutional layer groups and multiple warp laminations, is obtained
To terrain classification probability graph, wherein, different atural objects have different coordinate points color and coordinates in the terrain classification probability graph
Point depth；
S3：The all coordinates in the terrain classification probability graph are clicked through according to the coordinate points color and the coordinate points depth
Row classification, obtains the segmentation figure picture of different atural objects.
2. image partition method according to claim 1, it is characterised in that the step S2 includes：
S21：By the remote sensing images by the image after convolutional layer group coordinate points mark described at least one and by all institutes
State the image after warp lamination coordinate points mark described in convolutional layer group and at least one repeatedly to be merged, obtain fused images；
S22：By the remote sensing images with the fused images by the figure after warp lamination coordinate points mark described at least one
As repeatedly being merged, terrain classification probability graph is obtained.
3. image partition method according to claim 2, it is characterised in that the step S3 includes：
S31：The energy function that the coordinate points color is input into the CRF model layers is calculated the terrain classification probability graph
In all coordinate points the first energy value；
S32：The energy function that the coordinate points depth is input into the CRF model layers is calculated the terrain classification probability graph
In all coordinate points the second energy value；
S33：The final energy value of all coordinate points is calculated according to first energy value and second energy value；
S34：The all coordinate points in the terrain classification probability graph are classified according to the final energy value, obtains difference
The segmentation figure picture of atural object.
4. a kind of image segmentation system of remote sensing images atural object, it is characterised in that including：
Module (1) is put into, for remote sensing images to be put into full convolutional network, the full convolutional network includes the multiple being arranged in order
Convolutional layer group, multiple warp lamination and CRF model layers, wherein, the convolutional layer group includes the convolutional layer being alternately arranged and lax
Convolutional layer；
Mark module (2), for being carried out to the remote sensing images by multiple convolutional layer groups and multiple warp laminations
Coordinate points are marked, and obtain terrain classification probability graph, wherein, different atural objects have different coordinates in the terrain classification probability graph
Point color and coordinate points depth；
Sort module (3), for according to the coordinate points color and the coordinate points depth in the terrain classification probability graph
All coordinate points classified, obtain the segmentation figure picture of different atural objects.
5. image segmentation system according to claim 4, it is characterised in that the mark module (2) includes：
First fusion submodule (21), for by the remote sensing images by described at least one convolutional layer group coordinate points mark after
Image carried out repeatedly with the image after warp lamination coordinate points mark by all convolutional layer groups and described at least one
Fusion, obtains fused images；
Second fusion submodule (22), for by the remote sensing images with the fused images by deconvolution described at least one
Image after layer coordinate points mark is repeatedly merged, and obtains terrain classification probability graph.
6. image segmentation system according to claim 5, it is characterised in that the sort module (3) includes：
First calculating sub module (31), the energy function for the coordinate points color to be input into the CRF model layers is calculated
To the first energy value of all coordinate points in the terrain classification probability graph；
Second calculating sub module (32), the energy function for the coordinate points depth to be input into the CRF model layers is calculated
To the second energy value of all coordinate points in the terrain classification probability graph；
3rd calculating sub module (33), for being calculated all coordinates according to first energy value and second energy value
The final energy value of point；
Classification submodule (34), for being clicked through to all coordinates in the terrain classification probability graph according to the final energy value
Row classification, obtains the segmentation figure picture of different atural objects.
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| CN109918449A (en) * | 2019-03-16 | 2019-06-21 | 中国农业科学院农业资源与农业区划研究所 | A kind of agricultural disaster information remote sensing extracting method and system based on Internet of Things |
| 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 |
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