The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B7
https://doi.org/10.5194/isprs-archives-XLI-B7-999-2016
https://doi.org/10.5194/isprs-archives-XLI-B7-999-2016
24 Oct 2016
 | 24 Oct 2016

A KERNEL-BASED SIMILARITY MEASURING FOR CHANGE DETECTION IN REMOTE SENSING IMAGES

Xiaodan Shi, Guorui Ma, Fenge Chen, and Yanli Ma

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.