Algorithms for Multispectral and Hyperspectral Image Analysis

Copyright © 2013 Heesung Kwon et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recent advances in multispectral and hyperspectral sensing technologies coupled with rapid growth in computing power have led to new opportunities in remote sensing—higher spatial and/or spectral resolution over larger areas leads to more detailed and comprehensive land cover mapping and more sensitive target detection. However, these massive hyperspectral datasets provide new challenges as well. Accurate and timely processing of hyperspectral data in large volumes must be treated in a nonconventional way in order to drastically enhance data modeling and representation, learning and inference, physics-based analysis, computational complexity, and so forth. Current practical issues in processing multispectral and hyperspectral data include robust characterization of target and background signatures and scene characterization [1–3], joint exploitation of spatial and spectral features [4], background modeling for anomaly detection [5, 6], robust target detection techniques [7], low-dimensional representation, fusion of learning algorithms, the balance of statistical and physical modeling, and real-time computation [8, 9]. e aim of this special issue is to advance the capabilities of algorithms and analysis technologies for mul-tispectral and hyperspectral imagery by addressing some of the above-mentioned critical issues. We have received many submissions and selected six papers aer careful and rigorous peer review. e accepted papers cover a wide range of topics, such as anomaly detection, target detection and classi�cation, dimensionality reduction and reconstruction, fusion of hyperspectral detection algorithms, and non-Gaussian mixture modeling for hyperspectral imagery. e brief summaries of the accepted papers are as follows. e paper " Hyperspectral anomaly detection: comparative evaluation in scenes with diverse complexity, " by D. Borghys et al., provides a comprehensive review of popular hyperspec-tral anomaly detection methods, an important problem in hyperspectral signal processing, including the global Reed-Xiaoli (RX) method, subspace methods, local methods, and segmentation based methods. e extensive performance analysis of these methods is presented in scenes with various backgrounds and different representative targets. e comparative results reveal the superiority of some detectors in certain scenes over other detectors. e paper " Non-Gaussian linear mixing models for hyperspectral images, " by P. Bajorski, addresses the problem of modeling hyperspectral data using non-Gaussian distribution. It is done by assuming a linear mixing model consisting of nonrandom-structured background and random noise terms. e nonvariable part …