Dimensionality Reduction and Linear Discriminant Analysis for Hyperspectral Image Classification

Authors:
Qian Du;Nicolas H. Younan
Affiliations:
Department of Electrical and Computer Engineering, Mississippi State University, USA;Department of Electrical and Computer Engineering, Mississippi State University, USA
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Year:
2008

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IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

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Abstract

In this paper, we investigate the application of Fisher's linear discriminant analysis (FLDA) to hyperspectral remote sensing image classification. The basic idea of FLDA is to design an optimal transform so that the classes can be well separated in the low-dimensional space. The practical difficulty of applying FLDA to hyperspectral images includes the unavailability of enough training samples and unknown information for all the classes present. So the original FLDA is modified to avoid the requirements of complete class knowledge, such as the number of actual classes present. We also investigate the performance of the class of principal component analysis (PCA) techniques prior to FLDA and find that the interference and noise adjusted PCA (INAPCA) can provide the improvement in the final classification.