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Hyper-spectral Image Compression

Hyper-spectral images consist of huge amount of data. This paper presents a compression technique developed specifically for these images. This technique explores unique characteristics of the hyper-spectral images and incorporates them into the compression. It also exploits the wavelet transformation and bit plane entropy coding used in general image compression. In this technique, predicting one image from another plays an important role. One of the problems in applying the prediction is to find the ordering of which image predicts which one. This paper presents the solution to this problem using the minimum weight spanning tree algorithm found several years ago. Using this algorithm, a high compression result is achieved.