Adaptive Compressed Classification for hyperspectral imagery

Hyperspectral imaging (HSI) is a useful tool for the classification of vast areas. High accuracy is achieved by means of spectral information for each pixel, which inherently leads to a huge amount of data and, thus, requires costly processing. We present an Adaptive Compressed Classification (ACC) framework for HSI that allows a compressive acquisition of the scene of interest. Since classification is performed in the compressive domain, expensive reconstruction is avoided, significantly reducing computational requirements. For ACC, we propose an adaptive probabilistic approach to optimize the measurement and basis matrices. Based on real data sets, we show that Compressed Classification yields high classification accuracy close to results obtained for the complete data. Using the proposed adaptive approach, even higher accuracies are achieved in all tested cases.