CN106803106A - SAR image automatic classification system and method - Google Patents
SAR image automatic classification system and method Download PDFInfo
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- CN106803106A CN106803106A CN201710108835.2A CN201710108835A CN106803106A CN 106803106 A CN106803106 A CN 106803106A CN 201710108835 A CN201710108835 A CN 201710108835A CN 106803106 A CN106803106 A CN 106803106A
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Abstract
SAR image automatic classification system of the invention, including：Original image is put in storage processing unit, original SAR data parse obtains essential information, and generates SAR image according to the polarization mode included in essential information, essential information and SAR image is associated and is saved in original image storehouse；Original image storehouse, preserves SAR image and its essential information；Sample Storehouse, preserves sample image and sample configuration file；Sample Storehouse construction unit, selects SAR image as sample image from original image storehouse, and the sample image is set the parameters to generate sample configuration file, and sample image and corresponding sample configuration file are associated is saved in the Sample Storehouse；And taxon, according to the essential information of image to be classified, the sample image and its sample configuration file of matching are obtained from Sample Storehouse, automatic classification configurations file is generated according to the sample configuration file, image to be classified is classified based on the automatic classification configurations file.
Description
Technical field
The invention belongs to technical field of remote sensing image processing, more particularly to based on sample library management and various sorting techniques
SAR image automatic classification method.
Background technology
Synthetic aperture radar (Synthetic Aperture Radar, SAR) is a kind of Active Imaging Lidar system, is had
It is round-the-clock, the characteristics of be imaged round the clock, wherein, polarization SAR system can be operated under different POLARIZATION CHANNEL integrated modes, can be obtained more
Many ground object target information.In recent years, there are various SAR systems, the interpretation and application of SAR information show more extensive
Application prospect, more and more important effect has been played at aspects such as Land_use change, surface cover, target detections.
In the application process of SAR image, image classification is an important content of Polarimetric SAR Image interpretation, at present, is not gone out
Now for the special taxonomic hierarchies of SAR scenes, it is impossible to magnanimity SAR data is effectively managed, SAR treatment is relied primarily on specifically
Data type and algorithm, mainly by artificial visual interpretation, it is impossible to realize real automatic interpretation.These problems are greatly hindered
The application potential of SAR image, reduces the interpretation efficiency of SAR image.
The content of the invention
In view of the shortcomings of the prior art, SAR image can be divided automatically it is an object of the invention to provide one kind
The SAR image automatic classification system based on Sample Storehouse and sorting technique of class.
SAR image automatic classification system for realizing the purpose of the invention described above, it is characterised in that including：Original graph
As storage processing unit, the original SAR data to being input into carries out parsing the essential information for obtaining the SAR data, and according to institute
The polarization mode generation SAR image included in the essential information for obtaining, the essential information and the SAR image are associated
It is saved in original image storehouse；Original image storehouse, for preserving the SAR image and its corresponding essential information；Sample Storehouse, uses
In preservation sample image and sample configuration file；Sample Storehouse construction unit, selects SAR image conduct from the original image storehouse
Sample image, sets the parameters to generate sample configuration file to the sample image, and by the sample image and corresponding sample
Configuration file is associated and is saved in the Sample Storehouse；And taxon, according to the base of the SAR image to be sorted being input into
This information, obtains the sample image and its sample configuration file of matching from the Sample Storehouse, is given birth to according to the sample configuration file
Into automatic classification configurations file, the SAR image to be sorted is classified based on the automatic classification configurations file.
The essential information also ground object target type, data source, resolution ratio, imaging time including image.
Further, the automatic classification configurations file includes the feature recommended in automatic classification,
The feature recommended in the automatic classification that the taxon includes according to the automatic classification configurations file, leads to
The mode for crossing cross validation obtains optimum classifier, afterwards using the optimum classifier that sample image training is acquired, last profit
SAR image to be sorted is classified with the optimum classifier for training.
Further, also, for preserving the various grader templates for having trained, grader is worked as including grader ATL
When there is grader template corresponding with acquired optimum classifier in ATL, the optimal classification acquired in training is not required to
Device, is directly classified using the corresponding grader for training in grader ATL to SAR image to be sorted.
SAR image automatic classification method of the invention, it is characterised in that including：Original image is put in storage process step, to institute
The original SAR data of input carries out parsing the essential information for obtaining the SAR data, and is included according in resulting essential information
Polarization mode generation SAR image, the essential information and the SAR image are associated and are saved in original image storehouse；Sample
This storehouse construction step, selects SAR image as sample image from the original image storehouse, and the sample image is set the parameters to
Generation sample configuration file, and the sample image and corresponding sample configuration file are associated and be saved in Sample Storehouse；
And classifying step, according to the essential information of the SAR image to be sorted being input into, the sample of matching is obtained from the Sample Storehouse
Image and its sample configuration file, automatic classification configurations file is generated according to the sample configuration file, is matched somebody with somebody based on the automatic classification
File is put to classify the SAR image to be sorted.
The essential information also ground object target type, data source, resolution ratio, imaging time including image.
Further, the automatic classification configurations file includes the feature recommended in automatic classification, in the classification step
Suddenly, the feature recommended in the automatic classification for being included according to the automatic classification configurations file, by way of cross validation
Optimum classifier is obtained, afterwards using the optimum classifier that sample image training is acquired, finally using the most optimal sorting for training
Class device is classified to SAR image to be sorted.
Further, it is corresponding with acquired optimum classifier when existing in grader ATL in the classifying step
During grader template, the optimum classifier acquired in training is not required to, is directly trained accordingly in utilization grader ATL
Grader is classified to SAR image to be sorted.
Technique effect of the invention is as follows.
According to the present invention, in face of the various SAR data of magnanimity, Sample Storehouse, Jin Erke are built using Sample Storehouse construction unit
Various SAR images and sample are managed collectively by Sample Storehouse, and combine specific grader, SAR image is carried out automatically
Classification, is favorably improved the interpretation efficiency of SAR image, and enhancing people obtain surface cover letter from large scene magnanimity SAR image
The ability of breath.
Brief description of the drawings
Fig. 1 is the schematic diagram of the SAR image automatic classification system for showing the first embodiment of the present invention.
Fig. 2 is the schematic diagram of the SAR image automatic classification method for showing the first embodiment of the present invention.
Fig. 3 shows that SAR image automatic classification system and its method based on the first embodiment of the present invention have been carried out automatically
Result images after the original SAR image of one example of classification and classification.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with the accompanying drawings to preferred reality of the invention
The mode of applying is described in detail.
Fig. 1 is the schematic diagram for showing SAR image automatic classification system of the invention.As shown in figure 1, the SAR of the present embodiment
Classification of images system 100 includes original image storehouse 110, Sample Storehouse 120, Sample Storehouse construction unit 130, taxon 140
Processing unit 150 is put in storage with original image.
Wherein, original image storehouse 110 preserves the SAR image and its corresponding essential information.The essential information can be with
The information such as polarization mode, ground object target type, data source (satellite source), resolution ratio, imaging time including image, so that can
The SAR image in original image storehouse 110 is indexed, browsed and called using these information as parameter.
The original SAR data that original image storage processing unit 150 pairs is input into carries out parsing the base for obtaining the SAR data
This information, and SAR image is generated according to the polarization mode included in resulting essential information.For example, for single polarization SAR numbers
According to gray-scale map can be generated, for dual polarization or full-polarization SAR data, pcolor can be generated.Afterwards, by the essential information and institute
State SAR image and associate and be saved in original image storehouse 110.
Sample Storehouse 120 is used to preserve sample image and sample configuration file.The sample configuration file may include the class of image
The structural information of other information, color channel values and image.Can uniquely be navigated to accordingly by the information in Sample Storehouse 120
Sample image, be SAR image classify automatically offer sample support.The structure of original image storehouse 110 and Sample Storehouse 120, storage and
Management, can use data base tool, and the data and feature that are stored therein are visited by fast and effectively database index
The operation such as ask, add.
Sample Storehouse construction unit 130 selects SAR image as sample image from original image storehouse 110, to the sample graph
As setting the parameters to generate sample configuration file, and the sample image and corresponding sample configuration file are associated into preservation
To in the Sample Storehouse 110.The arrange parameter can mainly include two parts, and a part is the structural information of sample, i.e. sample
The information such as this border；Another part is the classification information of sample, the classification including sample, information etc. the color channel values of sample.
Taxon 140 obtains matching according to the essential information of the SAR image to be sorted being input into from the Sample Storehouse
Sample image and its sample configuration file, automatic classification configurations file is generated according to the sample configuration file, it is automatic based on this
Classification configurations file is classified to the SAR image to be sorted.
Fig. 2 is the schematic diagram of the SAR image automatic classification method for showing the first embodiment of the present invention.
As shown in Fig. 2 the SAR image automatic classification method of first embodiment includes that original image is put in storage process step
S110, Sample Storehouse construction step S120 and classifying step S130.
Process step S110, the original SAR numbers that original image storage processing unit 150 pairs is input into are put in storage in original image
According to carrying out parsing the essential information for obtaining the SAR data, essential information can specifically include polarization mode, the ground object target of image
The information such as type, data source, resolution ratio, imaging time.Also, according to the polarization mode life included in resulting essential information
Into SAR image, for example, gray-scale map can be generated for single polarization SAR data, for dual polarization or full-polarization SAR data, can generate
Pcolor.Afterwards, the essential information and SAR image for parsing being obtained are associated and are saved in original image storehouse 110.
In Sample Storehouse construction step S120, Sample Storehouse construction unit 130 selects SAR image to make from original image storehouse 110
It is sample image, afterwards, selected SAR image is set the parameters to generate sample configuration file, and by the sample image
Associated with corresponding sample configuration file and be saved in Sample Storehouse 120.Specifically, Sample Storehouse construction unit 130 can be
The SAR image and its essential information in original image storehouse 110 are shown on system interface, operating personnel can be selected by manual type
After SAR image is as sample image, set the parameters to be formed the sample configuration file of the sample image to sample image.Its
In, arrange parameter may include structural information of classification information, color channel values and image of SAR image etc., and structural information can be wrapped
Include the information such as the border of image.
It is to be sorted with what is be input into according to the essential information of the SAR image to be sorted being input into classifying step S130
The essential information of SAR image obtains the sample image of matching as querying condition from Sample Storehouse 120.Afterwards, using with obtained
The corresponding sample configuration file of sample image for taking generates automatic classification configurations file, and then based on the automatic classification configurations file
The SAR image to be sorted is classified.
The Polarimetric SAR Image of 7 days December in the 2011 Wuhan Area RadarSAT-2 8m resolution ratio that (a) is represented in Fig. 3
As a example by, it is input into after the SAR data to be sorted to SAR image automatic classification system 100, original image storage processing unit
150 pairs of SAR datas to be sorted parse obtaining essential information：RadarSAT-2,8m resolution ratio, complete polarization, 2011 12
Imaging of the moon 7 etc..Taxon 140 to parse the essential information of SAR data for obtaining as querying condition, from the Sample Storehouse
The sample image of matching is obtained in 120, and using automatic point of sample configuration file corresponding with acquired sample image generation
Class configuration file.
Afterwards, according to the feature recommended in the automatic classification configurations file, analyzed by way of cross validation
To the best grader of classifying quality as optimum classifier, and determined according to the optimum classifier type and calculate survey to be sorted
Feature needed for examination data, in this example, grader used is nearest neighbor classifier, used to be characterized as 3 that Freeman is decomposed
Component characterization.Acquisition on optimum classifier, if necessary, can select automatic classification method with manual intervention.
Afterwards, according to sample image, the selected grader of training estimates classifier parameters according to sample.In this example, recently
The arest neighbors number of adjacent grader is K=30.
Assuming that number of samples is N, and each sample one category label c of correspondence, classification sum is C, calculates data z to be sorted
To this N number of sample point apart from d1≤i≤NAnd sort, the preceding K distance for taking minimum constitutes a data set SK。
Statistics SKIn middle sample of all categories number, the maximum target type c for being data to be sortedz。
Here a certain specific classification device is not directed to, its method for parameter estimation is especially emphasized.It should be noted that different classifications
The training method of device is different, and the present invention can be accordingly adapted to according to specific grader.
Afterwards, using the grader for training, original SAR data to be sorted is classified, classification results according to target divide
Legend distribution color in class system, shown in (b) of classification results such as Fig. 3.
The present invention is described above in association with specific example.Embodiment shown in above-mentioned, is not intended to limit this hair
It is bright, a kind of Polarimetric SAR Images of Radarsat-2 are also not limited to, ALOS-2/PALSAR, TerraSAR-X are applied also for,
Other spaceborne or carried SAR data such as EVNISAT/ASAR, SIR-C, AIRSAR, EMISAR, PISAR.
In a second embodiment, the SAR image automatic classification system of the present embodiment is on the basis of the structure of first embodiment
On also include grader ATL, for preserving the various grader templates that have trained.Described in grader template point
The information of class device, including the classifier parameters that grader name, feature used and training are obtained.When in grader ATL exist with
During the corresponding grader template of acquired optimum classifier, the optimum classifier acquired in training is not required to, directly using classifying
The corresponding grader for training is classified to SAR image to be sorted in device ATL.Such that it is able to more quickly apply
To in other scenes, the step of eliminate classifier training, simplify classification process.
The preferred embodiment of the present invention is described in detail above in association with accompanying drawing, but, the present invention is not limited to above-mentioned reality
The detail in mode is applied, in range of the technology design of the invention, various letters can be carried out to technical scheme
Monotropic type, these simple variants belong to protection scope of the present invention.
Any the technical staff in the technical field of the invention, without departing from the spirit and scope of the present invention, should
Various changes can be made and modification.Therefore the scope that protection scope of the present invention should be defined with appended claims is
It is accurate.
Claims (8)
1. a kind of SAR image automatic classification system, it is characterised in that
Including：
Original image is put in storage processing unit, and the original SAR data to being input into carries out parsing the basic letter for obtaining the SAR data
Breath, and SAR image is generated according to the polarization mode included in resulting essential information, by the essential information and the SAR
Image is associated and is saved in original image storehouse；
Original image storehouse, for preserving the SAR image and its corresponding essential information；
Sample Storehouse, for preserving sample image and sample configuration file；
Sample Storehouse construction unit, selects SAR image as sample image from the original image storehouse, and the sample image is set
Parameter generates sample configuration file, and the sample image and corresponding sample configuration file associated be saved in it is described
In Sample Storehouse；And
Taxon, according to the essential information of the SAR image to be sorted being input into, obtains the sample of matching from the Sample Storehouse
Image and its sample configuration file, automatic classification configurations file is generated according to the sample configuration file, is matched somebody with somebody based on the automatic classification
File is put to classify the SAR image to be sorted.
2. SAR image automatic classification system according to claim 1, it is characterised in that
The essential information also ground object target type, data source, resolution ratio, imaging time including image.
3. SAR image automatic classification system according to claim 1 and 2, it is characterised in that
The automatic classification configurations file includes the feature recommended in automatic classification,
The feature recommended in the automatic classification that the taxon includes according to the automatic classification configurations file, by handing over
The mode for pitching checking obtains optimum classifier, afterwards using the optimum classifier that sample image training is acquired, finally using instruction
The optimum classifier perfected is classified to SAR image to be sorted.
4. SAR image automatic classification system according to claim 3, it is characterised in that
Also include grader ATL, for preserving the various grader templates for having trained, deposited when in grader ATL
In grader template corresponding with acquired optimum classifier, the optimum classifier acquired in training is not required to, directly utilized
The corresponding grader template for training is classified to SAR image to be sorted in grader ATL.
5. a kind of SAR image automatic classification method, it is characterised in that including：
Original image is put in storage process step, and the original SAR data to being input into carries out parsing the basic letter for obtaining the SAR data
Breath, and SAR image is generated according to the polarization mode included in resulting essential information, the essential information and the SAR scheme
It is saved in original image storehouse as associating；
Sample Storehouse construction step, selects SAR image as sample image from the original image storehouse, and the sample image is set
Parameter generates sample configuration file, and associates and be saved in sample the sample image and corresponding sample configuration file
In storehouse；And
Classifying step, according to the essential information of the SAR image to be sorted being input into, obtains the sample of matching from the Sample Storehouse
Image and its sample configuration file, automatic classification configurations file is generated according to the sample configuration file, is matched somebody with somebody based on the automatic classification
File is put to classify the SAR image to be sorted.
6. SAR image automatic classification method according to claim 5, it is characterised in that
The essential information also ground object target type, data source, resolution ratio, imaging time including image.
7. the SAR image automatic classification method according to claim 5 or 6, it is characterised in that
The automatic classification configurations file includes the feature recommended in automatic classification,
In the classifying step, according to the feature recommended in the automatic classification that the automatic classification configurations file includes, lead to
The mode for crossing cross validation obtains optimum classifier, afterwards using the optimum classifier that sample image training is acquired, last profit
SAR image to be sorted is classified with the optimum classifier for training.
8. SAR image automatic classification method according to claim 7, it is characterised in that
In the classifying step, when there is grader template corresponding with acquired optimum classifier in grader ATL
When, the optimum classifier acquired in training is not required to, directly treated using the corresponding grader for training in grader ATL
Classification SAR image is classified.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107895179A (en) * | 2017-11-29 | 2018-04-10 | 合肥赑歌数据科技有限公司 | It is a kind of based on close on value analysis workpiece categorizing system and method |
| CN108805046A (en) * | 2018-05-25 | 2018-11-13 | 京东方科技集团股份有限公司 | For the method for facial match, unit and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8125370B1 (en) * | 2007-04-16 | 2012-02-28 | The United States Of America As Represented By The Secretary Of The Navy | Polarimetric synthetic aperture radar signature detector |
| CN103927387A (en) * | 2014-04-30 | 2014-07-16 | 成都理想境界科技有限公司 | Image retrieval system, method and device |
| CN104134080A (en) * | 2014-08-01 | 2014-11-05 | 重庆大学 | Method and system for automatically detecting roadbed collapse and side slope collapse of road |
| CN106134458B (en) * | 2013-07-31 | 2015-05-13 | 中国人民解放军63956部队 | Dirigible for target acquisition/camouflage protection carries complete polarization SAR system |
| CN105701481A (en) * | 2016-02-26 | 2016-06-22 | 民政部国家减灾中心 | Collapsed building extraction method |
-
2017
- 2017-02-27 CN CN201710108835.2A patent/CN106803106B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8125370B1 (en) * | 2007-04-16 | 2012-02-28 | The United States Of America As Represented By The Secretary Of The Navy | Polarimetric synthetic aperture radar signature detector |
| CN106134458B (en) * | 2013-07-31 | 2015-05-13 | 中国人民解放军63956部队 | Dirigible for target acquisition/camouflage protection carries complete polarization SAR system |
| CN103927387A (en) * | 2014-04-30 | 2014-07-16 | 成都理想境界科技有限公司 | Image retrieval system, method and device |
| CN104134080A (en) * | 2014-08-01 | 2014-11-05 | 重庆大学 | Method and system for automatically detecting roadbed collapse and side slope collapse of road |
| CN105701481A (en) * | 2016-02-26 | 2016-06-22 | 民政部国家减灾中心 | Collapsed building extraction method |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107895179A (en) * | 2017-11-29 | 2018-04-10 | 合肥赑歌数据科技有限公司 | It is a kind of based on close on value analysis workpiece categorizing system and method |
| CN108805046A (en) * | 2018-05-25 | 2018-11-13 | 京东方科技集团股份有限公司 | For the method for facial match, unit and storage medium |
| CN108805046B (en) * | 2018-05-25 | 2022-11-04 | 京东方科技集团股份有限公司 | Method, apparatus, device and storage medium for face matching |
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