Feature extraction from hyperspectral images compressed using the JPEG-2000 standard

We present results quantifying the exploitability of compressed remote sensing imagery. The performance of various feature extraction and classification tasks is measured on hyperspectral images coded using the JPEG-2000 Standard. Spectral decorrelation is performed using the Karhunen-Loeve transform and the 9-7 wavelet transform as part of the JPEG-2000 process. The quantitative performance of supervised, unsupervised, and hybrid classification tasks is reported as a function of the compressed bit rate for each spectral decorrelation scheme. The tasks examined are shown to perform with 99% accuracy at rates as low as 0.125 bits/pixel/band. This suggests that one need not limit remote sensing systems to lossless compression only, since many common classification tools perform reliably on images compressed to very low bit rates