Volume XLI-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 999-1006, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-999-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 999-1006, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-999-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  24 Oct 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 Xiaodan Shi et al.
  • 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.