<title>Adaptive lossy compression and classification of hyperspectral images</title>

Abstract

In this paper several methods for image lossy compression are compared in order to find adaptive schemes that may improve compression performance for hyperspectral images under a classification accuracy constraint. Our goal is to achieve high compression ratios without degrading classification accuracy too much for a given classifier. Lossy compression methods such as JPEG, three-dimensional JPEG, a tree structured vector quantizer, a zero- tree wavelet encoder, and a lattice vector quantizer have been used to compress the image before the classification stage. Classification is carried out through classification trees. Two kinds of classification trees are compared: one- stage trees, which classify the input image using only a single classification stage; and multi-stage trees, which use a mixed class that delays the classification of problematic pixels for which the accuracy achieved in the current stage is not enough. Our experiments indicate that is is possible to achieve high compression ratios while maintaining the classification accuracy. It is also shown that compression methods that take advantage of the high band correlation of hyperspectral images provide better results and become more flexible for a real case scenario. As compared to one-stage trees, the employment of multi-stage trees increases the classification accuracy and reduces the classification cost.