In this paper, we report our recent investigation on principal components analysis (PCA) and JPEG2000 in hyperspectral image compression, where the PCA is for spectral coding and JPEG2000 is for spatial coding for principal component (PC) images (referred to as PCA+JP2K). We find out such an integrated scheme significantly outperforms the commonly used 3-dimensional (3D) JPEG2000 (3D-JP2K) in rate-distortion performance, where the discrete wavelet transform (DWT) is used for spectral coding. We also find out that the best rate-distortion performance occurs when a subset of PCs is used instead of all the PCs. In the AVIRIS experiments, PCA+JP2K can bring about 5-10 dB increase in SNR compared to 3D-JP2K, whose SNR in turn is about 0.5dB greater than other popular wavelet based compression approaches, such as 3D-SPIHT and 3D-SPECK. The performance on data analysis using the reconstructed data is also evaluated. We find out that using PCA for spectral decorrelation can provide better performance, in particular, in low bitrates. The schemes for
low-complexity PCA are also presented, which include the spatial
down-sampling in the estimation of covariance matrix and the use of data with non-zero mean. The compression performance on both radiance and reflectance data are also compared. The instructive suggestions on practical applications are provided.