Recent developments and future directions in hyperspectral data classification

Antonio J. Plaza

doi:10.1117/12.753100

24 October 2007 Recent developments and future directions in hyperspectral data classification

Antonio J. Plaza

Proceedings Volume 6748, Image and Signal Processing for Remote Sensing XIII; 67480A (2007) https://doi.org/10.1117/12.753100
Event: SPIE Remote Sensing, 2007, Florence, Italy

Abstract

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.

Citation Download Citation

Antonio J. Plaza "Recent developments and future directions in hyperspectral data classification", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480A (24 October 2007); https://doi.org/10.1117/12.753100

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