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22 March 1999 Landsat PNN classifier using PCA of wavelet texture-edge features
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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