Landsat PNN classifier using PCA of wavelet texture-edge features

Harold H. Szu; Jacqueline Le Moigne; Nathan S. Netanyahu; Charles C. Hsu

doi:10.1117/12.342922

22 March 1999 Landsat PNN classifier using PCA of wavelet texture-edge features

Harold H. Szu, Jacqueline Le Moigne, Nathan S. Netanyahu, Charles C. Hsu

Proceedings Volume 3723, Wavelet Applications VI; (1999) https://doi.org/10.1117/12.342922
Event: AeroSense '99, 1999, Orlando, FL, United States

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.

Citation Download Citation

Harold H. Szu, Jacqueline Le Moigne, Nathan S. Netanyahu, and Charles C. Hsu "Landsat PNN classifier using PCA of wavelet texture-edge features", Proc. SPIE 3723, Wavelet Applications VI, (22 March 1999); https://doi.org/10.1117/12.342922

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