Lossy compression of hyperspectral data using vector quantization

Efficient compression techniques are required for the coding of hyperspectral data. Lossless compression is required in the transmission and storage of data within the distribution .system. Lossy techniques have a role in the initial analysis of hyperspectral data where large quantities of data are evaluated to select smaller areas for more detailed evaluation. Central to lossy compression is the developmgent of a suitable distortion measure, and this work discusses the applicability of extant measures in video coding to the compression of hyperspectral imagery. Criteria for a remote sensing distortion measure are developed and suitable distortion measures are discussed. One measure [the percentage maximum absolute distortion (PMAD) measure] is considered to be a suitable candidate for application to remotely sensed images. The effect of lossy compression is then investigated on the maximum likelihood classification of hyperspectral images, both directly on the original reconstructed data and on features extracted by the decision boundary feature extraction (DBFE) technique. The effect of the PMAD measure is determined on the classification of an image reconstructed with varying degrees of distortion. Despite some anomalies caused by challenging discrimination tasks, the classification accuracy of both the total image and its constituent classes remains predictable as the level of distortion increases. Although total classification accuracy is reduced from 96.8% for the original image to 82.8% for the image compressed with 4% PMAD, the loss in accuracy is not significant (less that 8%)for most classes other than those that present a challenging classification problem. Yet the compressed image is 1/17 the size of the original.