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Information Fusion in the Redundant-Wavelet-Transform Domain for Noise-Robust Hyperspectral Classification

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4 Author(s)
Prasad, S. ; Dept. of Electr. & Comput. Eng., Univ. of Houston, Houston, TX, USA ; Wei Li ; Fowler, J.E. ; Bruce, L.M.

Hyperspectral imagery comprises high-dimensional reflectance vectors representing the spectral response over a wide range of wavelengths per pixel in the image. The resulting high-dimensional feature spaces often result in statistically ill-conditioned class-conditional distributions. Conventional methods for alleviating this problem typically employ dimensionality reduction such as linear discriminant analysis along with single-classifier systems, yet these methods are suboptimal and lack noise robustness. In contrast, a divide-and-conquer approach is proposed to address the high dimensionality of hyperspectral data for effective and noise-robust classification. Central to the proposed framework is a redundant wavelet transform for representing the data in a feature space amenable to noise-robust multiscale analysis as well as a multiclassifier and decision-fusion system for classification and target recognition in high-dimensional spaces under small-sample-size conditions. The proposed partitioning of this feature space assigns a collection of all coefficients across all scales at a particular spectral wavelength to a dedicated classifier. It is demonstrated that such a partitioning of the feature space for a multiclassifier system yields superior noise performance for classification tasks. Additionally, validation studies with experimental hyperspectral data show that the proposed system significantly outperforms conventional denoising and classification approaches.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:50 ,  Issue: 9 )

Date of Publication:

Sept. 2012

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