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14 December 1999 Fuzzy classification of pixels using neural networks
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Imaging spectrometers acquire images in many narrow spectral bands. because of the limited spatial resolution the measured spectrum of a pixel is often a composition of a number of basic spectra. The purpose of fuzzy classification is to determine the presence and abundance of the basic spectra in a measured spectrum. Previous work demonstrated that a neural network could perform fuzzy classification. In this paper we study a more realistic situation of 10 basic spectra using 12 band airborne data and of 16 basic spectra using 6 band LANDSAT data. Available for this study were images and sets of pixels, which have been classified by inspection on the ground. For the LANDSAT case the set was not very pure and not very large. A method to purify and to expand data sets using image processing methods was therefore developed. Mixed pixels training and testing sets were generated from each original and generated set using a linear mixture model, where a mixed pixel could have a contribution from up to three classes. For each of the training sets a 1 hidden layer backpropagation neural network was trained to do the fuzzy classification. Testing the networks showed that they performed up to 20% better than the developed AnaML method, which is a combination of two classical methods.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Theo E. Schouten "Fuzzy classification of pixels using neural networks", Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999);

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