Changed Image Objects Extraction Algorithms Considering Texture Feature Contribution

doi:10.11947/j.AGCS.2017.20160581

Abstract

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

CLC Number:

P237

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

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