Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 999-1006, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/999/2016/
doi:10.5194/isprs-archives-XLI-B7-999-2016
 
24 Oct 2016
A KERNEL-BASED SIMILARITY MEASURING FOR CHANGE DETECTION IN REMOTE SENSING IMAGES
Xiaodan Shi1, Guorui Ma1, Fenge Chen2, and Yanli Ma1 1State Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, Hubei, 430079, China
2Department of Statistics, Wuhan University of Technology, 129 Luoshi Road, Wuhan, Hubei, 430079, China
Keywords: Change detection, kernel function, similarity measure, probability density Abstract. This paper presents a kernel-based approach for the change detection of remote sensing images. It detects change by comparing the probability density (PD), expressed as kernel functions, of the feature vector extracted from bi- temporal images. PD is compared by defined kernel functions without immediate PD estimation. This algorithm is model-free and it can process multidimensional data, and is fit for the images with rich texture in particular. Experimental results show that overall accuracy of the algorithm is 98.9 %, a little bit better than that of the change vector analysis and classification comparison method, which is 96.7 % and 95.9 % respectively.
Conference paper (PDF, 2151 KB)


Citation: Shi, X., Ma, G., Chen, F., and Ma, Y.: A KERNEL-BASED SIMILARITY MEASURING FOR CHANGE DETECTION IN REMOTE SENSING IMAGES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 999-1006, doi:10.5194/isprs-archives-XLI-B7-999-2016, 2016.

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