KERNEL-COMPOSITION FOR CHANGE DETECTION IN MEDIUM RESOLUTION REMOTE SENSING DATA
| Autor: | A. C. Braun, U. Weidner, S. Hinz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: | |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXIX-B7, Pp 281-286 (2012) |
| Druh dokumentu: | article |
| ISSN: | 1682-1750 2194-9034 |
| DOI: | 10.5194/isprsarchives-XXXIX-B7-281-2012 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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