Three-Dimensional Wavelet-Based Compression of Hyperspectral Images

Hyperspectral images may be treated as a three-dimensional data set for the purposes of compression. Here we present some compression techniques based on a three-dimensional wavelet transform that produce compressed bit streams with many useful properties. These properties are progressive quality encoding and decoding, progressive lossyto-lossless encoding, and progressive resolution decoding. We feature an embedded, block-based, image coding algorithm of low complexity, called SPECK (Set Partitioning Embedded bloCK), that has been proposed originally for single images and is modified and extended to three dimensions. The resultant algorithm, Three-Dimensional Set Partitioning Embedded bloCK (3D-SPECK), efficiently encodes 3D volumetric image data by exploiting the dependencies in all dimensions. We describe the use of this coding algorithm in two implementations, first in a purely quality or rate scalable mode and secondly in a resolution scalable mode. We utilize both integer and floating point wavelet transforms, whereby the former one enables lossy and lossless decompression from the same bit stream, and the latter one achieves better performance in lossy compression. The structure of hyperspectral images reveals spectral responses that would seem ideal candidates for compression by 3D-SPECK. We demonstrate that 3D-SPECK, a wavelet domain algorithm, like other time domain algorithms, can preserve spectral profiles This work was performed at Rensselaer Polytechnic Institute and was supported in part by National Science Foundation Grant No. EEC-981276. The government has certain rights in this material.