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Feature Selection for Hyperspectral Data Classification Using Double Parallel Feedforward Neural Networks

  • Mingyi He
  • Rui Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)

Abstract

Double parallel feedforward neural network (DPFNN) based approach is proposed for dimensionality reduction, which is one of very significant problems in multi- and hyperspectral image processing and is of high potential value in lunar and Mars exploration, new earth observation system, and biomedical engineering etc. Instead of using sequential search like most feature selection methods based on neural network (NN), the new approach adopts feature weighting strategy to cut down the computational cost significantly. DPFNN is trained by a mean square error function with regulation terms which can improve the generation performance and classification accuracy. Four experiments are carried out to assesses the performance of DPFNN selector for high-dimensional data classification. The first three experiments with the benchmark data sets are designed to make comparison between DPFNN selector and some NN based selectors. In the fourth experiment, hyperspectral data, that is an airborne visible/infrared imaging spectrometer (AVIRIS) data set, is used to compare DPFNN selector with widely used forward sequential search methods using the Maximum Likelihood classifier (MLC) as criterion. Experiments show the effectiveness of the new feature selection method based on DPFNNs.

Keywords

Feature Selection Hide Neuron Feature Subset Feature Selection Method Hyperspectral Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Mingyi He
    • 1
  • Rui Huang
    • 1
  1. 1.School of Electronics and InformationNorthwestern Polytechnical University, Shaanxi Key Laboratory of Information Acquisition and ProcessingXi’anP.R. China

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