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A Neural Relevance Model for Feature Extraction from Hyperspectral Images, and Its Application in the Wavelet Domain
RICE UNIV HOUSTON TX DEPT OF ELECTRICAL AND COMPUTER ENGINEERING
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Our research is motivated by military applications related to aspects of contingency planning. Of recent interest is the identification of landmasses which can support the landing and takeoff of fixed wing and rotary aircraft where accurate classification of the surface cover is of utmost importance. In a supervised classification scenario, a natural question is whether a subset of the input features spectral bands could be used without degrading classification accuracy. Our interest in feature extraction is twofold. First, we desire a significantly reduced set of features by which we can compress the signal. Second, we desire to enhance classification performance by alleviating superfluous signal content. Feature extraction models based on PCA or wavelets judge feature importance by the magnitude of the transform coefficients rarely leading to an appropriate set of features for classification. We analyze a recent neural paradigm, Generalized Relevance Learning Vector Quantization GRLVQ, to discover input dimensions relevant for classification. GRLVQ is based on, and substantially extends, Learning Vector Quantization LVQ by learning relevant input dimensions while incorporating classification accuracy in the cost function. LVQ is the supervised version of Kohonens unsupervised Self-Organizing Map. LVQs iteratively adjust prototype vectors to define class boundaries while minimizing the Bayes risk. Our analysis reveals two major algorithmic deficiencies of GRLVQ. Fixing these deficiencies leads to improved convergence performance and classification accuracy. We call our improved version GRLVQ-Improved GRLVQI.
APPROVED FOR PUBLIC RELEASE