Noise constrained hyperspectral data compression

Abstract

Hyperspectral data present significant challenges to downlinking, processing, and exploitation. Adaptive linear unmixing compression algorithms exploit spectral correlation to produce high compression ratios with little to no loss of significant information content. This paper presents an iterative adaptive linear unmixing compression method constrained by the estimated noise statistics of the hypercube. By dynamically optimizing the end-members for each pixel this method minimizes the number of components required to represent the spectrum of any given pixel, yielding a higher compression ratio with less information loss than conventional linear unmixing model approaches. The adaptive approach utilizes spatial connectivity to optimize the end-member selection process and noise statistics to limit data loss. We will demonstrate the effectiveness of this method with AVIRIS and HyMap/sup TM/ hyperspectral datasets.