Improving the performance of PCA and JPEG2000 for hyperspectral image compression

Abstract

In our previous paper, it has been demonstrated that principal component analysis (PCA) can outperform discrete wavelet transform (DWT) in spectral coding for hyperspectral image compression and a superior rate distortion performance can be provided in conjunction with 2-dimensional (2D) spatial coding using JPEG2000. The resulting compression algorithm is denoted as PCA+JPEG2000. In this paper, we further investigate how the data size (i.e., spatial and spectral size) influences the performance of PCA+JPEG2000 and provide a rule of thumb for PCA+JPEG2000 to perform appropriately. We will also show that using a subset of principal components (PCs) (the resulting algorithm is denoted as SubPCA+JPEG2000) can always yield a better rate distortion performance than PCA+JPEG2000 with all the PCs being preserved for compression.