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Proceedings Paper

Landsat PNN classifier using PCA of wavelet texture-edge features
Author(s): Harold H. Szu; Jacqueline Le Moigne; Nathan S. Netanyahu; Charles C. Hsu
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Paper Abstract

Macrostructure/Wavelet-texture-label per pixel: In this paper, we concentrate on neural network classifiers on sub- regions of the image and we show how texture information obtained with a wavelet transform can be integrated to improve such a single label classifier. We apply a local spatial frequency analysis, a wavelet transform, to account for statistical texture information in Landsat/TM imagery. Statistical texture is extracted with a continuous edge- texture composite wavelet transform. We show how this approach relates to texture information computed from a co- occurrence matrix. The network is then trained with both the texture information and the additional pixel labels provided by the ground truth data. Theory and regional results are described in this paper.

Paper Details

Date Published: 22 March 1999
PDF: 13 pages
Proc. SPIE 3723, Wavelet Applications VI, (22 March 1999); doi: 10.1117/12.342922
Show Author Affiliations
Harold H. Szu, Naval Surface Warfare Ctr. (United States)
Jacqueline Le Moigne, NASA Goddard Space Flight Ctr. (United States)
Nathan S. Netanyahu, Univ. of Maryland/College Park (United States)
Charles C. Hsu, George Washington Univ. (United States)


Published in SPIE Proceedings Vol. 3723:
Wavelet Applications VI
Harold H. Szu, Editor(s)

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