Author(s)

J. Minguillon, J. Pujol, J. Serra & I. Ortimo

Abstract

In this paper the relationship between compression rate and classification accuracy for hyperspectral images is studied. We construct several clas- sification trees using CART and then, for various rates, we measure their classification accuracy after the input image has been compressed. We compare two kinds of classification trees: first, we construct one- stage trees, which classify the input image using only a single classification stage. Second, we construct multi-stage trees; these use a mixed class that delays classification of problematic pixels for which the accuracy achieved in the current stage is not enough. Then, for several compression rates, we study the classification accuracy evolution of such trees when a lossy compression method is applied to the input image

Keywords