Classification performance of hyperspectral images using the discrete wavelet transform with color and quality scalability under JPEG2000
Abstract
The classification performance of hyperspectral images using two different forward transforms is investigated. The tradeoff between the Discrete Wavelet Transform (DWT) and the Principal Component Analysis (PCA) is measured using different compression algorithms found in the JPEG2000 standard. These cross-component transforms can be evaluated in order to obtain a comparison of how well each transform can decorrelate data, reorder data optimally, speed of implementation, and effect on mineral classification performance. In addition, two scalability options defined by JPEG2000 were used: color and quality. Compression algorithms were carried out for the DWT and the PCA using the same set of parameters; the same hyperspectral image, the same classification algorithm—Spectral Angle Mapper, and the same type of compression: lossless compression. The purpose of this comparison is to be able to determine how much partial decompression is needed in order to achieve a 90% hit rate when classifying several minerals. This evaluation is focused primarily on two types of Mica called Aluminum-rich and Aluminum-poor Mica. The DWT yielded a compression performance almost as good as that of the PCA; for example, the 90% hit rate mark was achieved when using less than double the number of component bands with color scalability and less than triple with quality scalability for the DWT. On the other hand, this transform was about seven times faster than the PCA.
Subject Area
Electrical engineering|Computer science
Recommended Citation
Negrete, David F, "Classification performance of hyperspectral images using the discrete wavelet transform with color and quality scalability under JPEG2000" (2005). ETD Collection for University of Texas, El Paso. AAI1425895.
https://scholarworks.utep.edu/dissertations/AAI1425895