Hyperspectral data preprocessing improves performance of classification algorithms

Suresh Subramanian; Nahum Gat; Jacob Barhen

doi:10.1117/12.283829

31 October 1997 Hyperspectral data preprocessing improves performance of classification algorithms

Suresh Subramanian, Nahum Gat, Jacob Barhen

Proceedings Volume 3118, Imaging Spectrometry III; (1997) https://doi.org/10.1117/12.283829
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

Neural networks (NN) have been applied to hyperspectral image classification when traditional linear statistical classifiers have proven inadequate. The nonlinear and non- parametric properties of NN have often been cited for their apparent success. It has also been known that data preprocessing techniques such as principal component analysis (PCA) greatly improves classification accuracy. While PCA finds the axes of maximum variance in the data it does not guarantee increased separation between an arbitrary pair of classes. A transformation that is sensitive to class structure is obtained by solving the generalized eigenvalue problem of the amongst and within class covariance matrices of the data. Using this transformation, we demonstrate a case where the performance of linear statistical classifiers is comparable to that of NN classifiers for hyperspectral image classification.

Citation Download Citation

Suresh Subramanian, Nahum Gat, and Jacob Barhen "Hyperspectral data preprocessing improves performance of classification algorithms", Proc. SPIE 3118, Imaging Spectrometry III, (31 October 1997); https://doi.org/10.1117/12.283829

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