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Proceedings Paper

Enhancement of tropical land cover mapping with wavelet-based fusion and unsupervised clustering of SAR and Landsat image data
Author(s): Jacqueline Le Moigne; Nadine Laporte; Nathan S. Netanyahu
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Paper Abstract

The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument. In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi- sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet- based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

Paper Details

Date Published: 28 January 2002
PDF: 9 pages
Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); doi: 10.1117/12.454153
Show Author Affiliations
Jacqueline Le Moigne, NASA Goddard Space Flight Ctr. (United States)
Nadine Laporte, Univ. of Maryland/College Park (United States)
Nathan S. Netanyahu, Bar-Ilan Univ. and Univ. of Maryland/College Park (United States)


Published in SPIE Proceedings Vol. 4541:
Image and Signal Processing for Remote Sensing VII
Sebastiano Bruno Serpico, Editor(s)

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