Eigen wavelet: hyperspectral image compression algorithm

Summary form only given. The increased information content of hyperspectral imagery over multispectral data has attracted significant interest from the defense and remote sensing communities. We develop a mechanism for compressing hyperspectral imagery with no loss of information. The challenge of hyperspectral image compression lies in the non-isotropy and non-stationarity that is displayed across the spectral channels. Short-range dependence is exhibited over the spatial axes due to the finite extent of objects/texture on the imaged area, while long-range dependence is shown by the spectral axis due to the spectral response of the imaged pixel and transmission medium. A secondary, though critical, challenge is one of speed. In order to be of practical interest, a good solution must be able to scale up to speeds of the order of 20 MByte/s. We use an integerizable eigendecomposition along the spectral channel to optimally extract spectral redundancies. Subsequently, we apply wavelet-based encoding to transmit the residuals of eigendecomposition. We use contextual arithmetic encoding implemented with several innovations that guarantee speed and performance. Our implementation attains operating speeds of 550 kBytes of raw imagery per second, and achieves a compression ratio of around 2.7:1 on typical AVIRIS data. This demonstrates the utility and applicability of our algorithm towards realizing a deployable hyperspectral image compression system.