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DIMENSIONALITY REDUCTION and FEATURE SELECTION IN HYPERSPECTRAL IMAGE CLASSIFICATION

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If you have a question about this talk, please contact Taylan Cemgil .

Hyperspectral images provide a vast amount of information about a scene. However, much of that information is redundant as the bands are highly correlated. For computational and data compression reasons, it is desired to reduce the dimensionality of the data set while maintaining good performance in image analysis tasks. We present a method of dimensionality reduction based on neural networks that uses a novel penalty function to successfully reduce the number of active neurons, which corresponds to the dimensionality of the data for the task of interest. This method can be extended to select the best features from an arbitrary feature set, where “best” is defined in terms of reduction of the number of features while maintaining a desired level of classification performance.

This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.

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