The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XXXIX-B7
https://doi.org/10.5194/isprsarchives-XXXIX-B7-281-2012
https://doi.org/10.5194/isprsarchives-XXXIX-B7-281-2012
01 Aug 2012
 | 01 Aug 2012

KERNEL-COMPOSITION FOR CHANGE DETECTION IN MEDIUM RESOLUTION REMOTE SENSING DATA

A. C. Braun, U. Weidner, and S. Hinz

Keywords: Change Detection, Classification, Landuse, multispectral, multitemporal, Landsat

Abstract. A framework for multitemporal change detection based on kernel-composition is applied to a multispectral-multitemporal classification scenario, evaluated and compared to traditional change detection approaches. The framework makes use of the fact that images of different points in time can be used as input data sources for kernel-composition – a data fusion approach typically used with kernel based classifiers like support vector machines (SVM). The framework is used to analyze the growth of a limestone pit in the Upper Rhine Graben (West Germany). Results indicate that the highest accuracy rates are produced by the kernel based framework. The approach produces the least number of false positives and gives the most convincing overall impression.