Acta Geodaetica et Cartographica Sinica >

2017 , Vol. 46 >Issue 5: 605 - 613

DOI: https://doi.org/10.11947/j.AGCS.2017.20160581

Changed Image Objects Extraction Algorithms Considering Texture Feature Contribution

  • WEI Dongsheng ,
  • ZHOU Xiaoguang
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  • 1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
    2. College of Civil Engineering, Central South University of Forestry and Technology, Changsha 410004, China;
    3. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University), Ministry of Education, Changsha 410083, China;
    4. Key Laboratory of Non-ferrous Resources and Geological Hazard Detection, Changsha 410083, China

Received date: 2016-11-21

  Revised date: 2017-02-28

  Online published: 2017-06-05

Supported by

The National Key Research and Development Program of China (NO.2016YFB0501403);The National Natural Science Foundation of China (No. 41371366)

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Abstract

Remote sensing image change detection is an important part of global change research.The change detection methods based on two-temporal remote sensing images consist of drawbacks which affect the accuracy of change detection results, such as rigorous data requirements, inadequate adoption of multi-source remote sensing image data. At present, there are some existing classification vector dataset available for change detection in many regions, and some prior knowledge are included in the existing classification vector dataset, e.g., the position, shape, size and class. Making full use of the prior information is beneficial to improve the accuracy of change detection result. Extracting changed image objects is the key step in the change detection using the existing vector data and the latest remote sensing image,Therefore,a new change detection method based on texture feature contribution is proposed. The vector data is used to segment remote sensing image, the image objects can be extracted, and the texture feature value of image objects can be calculated. According to the principle of information gain, the feature contribution of texture feature parameters is defined, and it is used to select texture feature parameters for texture feature analysis. A similar coefficient of texture feature is defined and is used to extract changed image objects. The experimental results show that selecting texture feature parameters based on feature contribution can effectively improve the accuracy of extracting changed image object result.

Key words： texture feature; image object; information gain ratio; texture feature contribution

Cite this article

WEI Dongsheng , ZHOU Xiaoguang . Changed Image Objects Extraction Algorithms Considering Texture Feature Contribution[J]. Acta Geodaetica et Cartographica Sinica, 2017 , 46(5) : 605 -613 . DOI: 10.11947/j.AGCS.2017.20160581

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