Signal Processing for Hyperspectral Data

Hyperspectral data form a data-cube consisting of images of an object collected at several hundred, closely spaced wavelengths. They have been found to be of significant potential benefit in areas such as remote sensing of the Earth, medicine, and non-destructive evaluation. Effective extraction of information from the hyperspectral data cube presents several signal processing challenges, some of them unique to hyperspectral data. The problems involved range from registration and enhancement to development of statistical signal processing algorithms and models for object detection and classification. The focus of this paper is to provide an overview of select processing and modeling techniques for hyperspectral data