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

Article, Other literature type English OPEN
Shi, Xiaodan ; Ma, Guorui ; Chen, Fenge ; Ma, Yanli (2016)
  • Publisher: Copernicus Publications
  • Journal: (issn: 2194-9034, eissn: 2194-9034)
  • Related identifiers: doi: 10.5194/isprs-archives-XLI-B7-999-2016
  • Subject: TA1-2040 | T | TA1501-1820 | Applied optics. Photonics | Engineering (General). Civil engineering (General) | Technology

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.
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