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

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.

• Publication year

  2016

• Venue

  ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

• Publication date

  2016-10-24

• Fields of study

  Mathematics, Computer Science, Environmental Science

• Identifiers
  DOI 10.5194/ISPRS-ARCHIVES-XLI-B7-999-2016
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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