Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than 30 years from being a sparse research tool into a commodity product available to a broad user community. As a result, there is an emerging need for standardized data processing techniques, able to take into account the special properties of hyperspectral data and to take advantage of latest-generation sensor instruments and computing environments. The goal of this paper is to provide a seminal view on recent advances in techniques for hyperspectral data classification. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spatial and spectral information. The performance of the proposed techniques is evaluated in different analysis scenarios, including land-cover classification, urban mapping and spectral unmixing. To satisfy time-critical constraints in many remote sensing applications, parallel implementations for some of the discussed algorithms are also developed. Combined, these parts provide a snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges in the design of robust hyperspectral data classification algorithms.

Date Published: 24 October 2007
PDF: 15 pages
Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480A (24 October 2007); doi: 10.1117/12.753100

Published in SPIE Proceedings Vol. 6748:
Image and Signal Processing for Remote Sensing XIII
Lorenzo Bruzzone, Editor(s)