Near-lossless and lossy compression of imaging spectrometer data: Comparison of information extraction performance

We investigate the ability to derive meaningful information from decompressed imaging spectrometer data. Hyperspectral images are compressed with near-lossless and lossy coding methods. Linear prediction between the bands is used in both cases. Each band is predicted by a previously transmitted band. The residual is formed by subtracting the prediction from the original data and then is compressed either with a near-lossless bit-plane coder or with the lossy JPEG2000 algorithm. We study the effects of these two types of compression on hyperspectral image processing such as mineral and vegetation content classification using whole- and mixed pixel analysis techniques. The results presented in this paper indicate that an efficient lossy coder outperforms near-lossless method in terms of its impact on final hyperspectral data applications.