Titlebook: Neurocomputation in Remote Sensing Data Analysis; Proceedings of Conce Ioannis Kanellopoulos,Graeme G. Wilkinson (Head),J Conference procee

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Neural Networks for Classification of Ice Type Concentration from ERS-1 SAR Images,es. It includes a short review of earlier used techniques, implementation of different neural networks and results from various experiments with these networks. The estimation of ice type concentrations from Synthetic Aperture Radar (SAR) images has been investigated for several years, see e.g. [9].

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A Neural Network Approach to Spectral Mixture Analysis,ry is present in a single pixel. In spectral mixture analysis the fractions of the ground cover categories present in a pixel are determined, assuming a linear mixture model. In this paper neural network methods which are able to perform this analysis are considered. Methods for the construction of

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Feature Extraction for Neural Network Classifiers,extraction methods are reviewed, including principal component analysis, discriminant analysis, and the recently proposed decision boundary feature extraction method. The feature extraction methods are then applied in experiments in conjunction with classification by multilayer neural networks. The

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Spectral Pattern Recognition by a Two-Layer Perceptron: Effects of Training Set Size,ification algorithms tend to perform poorly in this context. This is because urban areas comprise a complex spatial assemblage of disparate land cover types - including built structures, numerous vegetation types, bare soil and water bodies. Thus, there is a need for more powerful spectral pattern r

OTTER

Comparison and Combination of Statistical and Neural Network Algorithms for Remote-Sensing Image Cletworks performances with the ones of classical statistical methods. These experimental comparisons pointed out that no single classification algorithm can be regarded as a “panacea”. The superiority of one algorithm over the other strongly depends on the selected data set and on the efforts devoted

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