Abstract
We propose an approach, based on KLT—Karhunen-Loève Transform, in order to automatically classify hyperspectral images. We used an AVIRIS image with 224 bands taken from the surroundings of Alto Paraiso City, on Goias State, in the middle of Brazil. Our approach performs dimensionality reduction, using only the eigenvectors with the highest eigenvalues, generating an eigenspace of low dimension. We also consider the spectral signatures of each class we work with. The classification is done finding the shortest Euclidean distance among the primitives of the new images and the primitives of the classes. We built a thematic map with 4 different classes.
This is a preview of subscription content, log in via an institution to check access.
References
V. Brennan and J. Príncipe, Multiresolution using Principal Component Analysis (University of Florida, Gainesville, FL, USA, 1999).
G. Fernandez and C. M. Wittembrink, Region Based KLT for Multispectral Image Compression (University of Califórnia, Santa Cruz, Santa Cruz, CA, USA, 1999).
M. Kirby and L. Sirovich, “Application of the Karhunen-Loéve Procedure for the Characterisation of Human Faces,” IEEE Transactions on Pattern Analysis and Machine Intelligence (1990).
M. Turk and A. Pentland, “Eigenfaces for Recognition,” Vision and Modeling Group, The Media Laboratory, MIT, In: Journal of Cognitive Neuroscience 3(1), 71–86 (1991).
Author information
Authors and Affiliations
Additional information
The text was submitted by the authors in English.
Rights and permissions
About this article
Cite this article
Quintiliano, P., Santa-Rosa, A. & Guadagnin, R. Hyperspectral images classification based on KLT. Pattern Recognit. Image Anal. 16, 39–42 (2006). https://doi.org/10.1134/S1054661806010123
Received:
Issue Date:
DOI: https://doi.org/10.1134/S1054661806010123