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.
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© 2008 Springer-Verlag Berlin Heidelberg
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Du, Q., Younan, N.H. (2008). Dimensionality Reduction and Linear Discriminant Analysis for Hyperspectral Image Classification. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85567-5_49
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DOI: https://doi.org/10.1007/978-3-540-85567-5_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85566-8
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