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Research on object-oriented remote sensing change detection method based on KL divergence |
| ZHU Hongchun1, 2, HUANG Wei1, LIU Haiying3, ZHANG Zhongfang1, WANG Bin1 |
1. Geomatics College, Shandong University of Science and Technology, Qingdao 266590, China;
2. Key Laboratory of Geomatics and Digital Technology, Qingdao 266590, China;
3. College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China |
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Abstract The change detection of remote sensing image has many research results from face-to-face to object-oriented operation and from the threshold to the similarity measurement; nevertheless, there are many problems such as the selection of the segmentation parameters, the determination of the change of the object and the degree of the change of the object. In view of such a situation, this paper proposes a new method based on similarity measurement to detect the change. This method has broken the performance form which has been used to detect the change of the results. Firstly, the optimal parameters of image object segmentation are calculated, and then the image patches are obtained. After that, the similarity coefficients are calculated by KL similarity calculation method, and the natural clustering features of the coefficients are calculated. The results show that the changes of the national economic development, disaster prevention and land use management decision-making are obvious, which shows the scientific nature and effectiveness of this method.
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| Keywords
Hapke model
mixed spectra
reflectance spectra
simulation spectra
mineral
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Issue Date: 03 May 2017
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2017,
Vol. 29
