KERNEL-COMPOSITION FOR CHANGE DETECTION IN MEDIUM RESOLUTION REMOTE SENSING DATA
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2012-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B7/281/2012/isprsarchives-XXXIX-B7-281-2012.pdf |
| Summary: | 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. |
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| ISSN: | 1682-1750 2194-9034 |