Low-complexity hyperspectral image compression using folded PCA and JPEG2000

Hyperspectral image compression by PCA and JPEG2000 can provide excellent rate distortion performance while preserving essential information for a successive application, e.g., classification tasks. However, for onboard applications, PCA suffers from high computational complexity and large memory requirements due to the eigen-analysis of highdimensional covariance matrix. Therefore, a computationally more efficient analysis, namely Folded Principal Component Analysis (FPCA) is adopted to perform dimension reduction and combined with JPEG2000 for compression. In FPCA, the spectral vector of hyperspectral pixels is folded into a matrix to compute covariance matrix, by which the dimension of covariance matrix is highly reduced. As a result, both computational complexity and memory requirement in subsequent eigen-analysis is reduced. Experimental results demonstrate that the proposed FPCA+JPEG2000 based compression scheme outperforms existing PCA+JPEG2000 in terms of rate distortion and classification after de-compression.