Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 1, Issue 4, Pages 322-326Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2004.837009
Keywords
Hyperspectral imaging; morphology; multiclassification; urban remote sensing
Categories
Funding
- European Union
- Icelandic Research Council
- University of Iceland
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Very high resolution hyperspectral data should be very useful to provide detailed maps of urban land cover. In order to provide such maps, both accurate and precise classification tools need, however, to be developed. In this letter, new methods for classification of hyperspectral remote sensing data are investigated, with the primary focus on multiple classifications and spatial analysis to improve mapping accuracy in urban areas. In particular, we compare spatial reclassification and mathematical morphology approaches. We show results for classification of DAIS data over the town of Pavia, in northern Italy. Classification maps of two test areas are given, and the overall and individual class accuracies are analyzed with respect to the parameters of the proposed classification procedures.
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