Recent advances in techniques for hyperspectral image processing


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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. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspectral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the highdimensional nature of the data, and to integrate the spatial and spectral information. Performance of the discussed techniques is evaluated in different analysis scenarios. To satisfy time-critical constraints in specific applications, we also develop efficient parallel implementations of some of the discussed algorithms. Combined, these parts provide an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on future potentials and emerging challenges in the design of robust hyperspectral imaging algorithms.

Imaging spectroscopy (, also known as hyperspectral imaging, is concerned with the measurement, analysis, and interpretation of spectra acquired from a given scene (or specific object) at a short, medium or long distance by an airborne or satellite sensor. The concept of imaging spectroscopy originated in the 1980’s, when A. F. H. Goetz and his colleagues at NASA’s Jet Propulsion Laboratory began a revolution in remote sensing by developing new instruments such as the Airborne Imaging Spectrometer (AIS), then called AVIRIS, for Airborne Visible Infra-Red Imaging Spectrometer (Green, 1998). This system is now able to cover the wavelength region from 0.4 to 2.5 μm using more than two hundred spectral channels, at nominal spectral resolution of 10 nm. The special characteristics of hyperspectral datasets pose different processing problems, which must be necessarily tackled under specific mathematical formalisms, such as classification and segmentation (Jia et al., 1999) or spectral mixture analysis (Adams et al., 1986; Smith et al., 1990a,b). For instance, several machine learning and image processing techniques have been applied to extract relevant information from hyperspectral data during the last decade (Varshney & Arora, 2004). Taxonomies of remote sensing data processing algorithms (including hyperspectral analysis methods) have been developed in the literature (King, 2003; Keshava & Mustard, 2002; Richards, 2005). It should be noted, however, that most available hyperspectral data processing techniques focused on analyzing the data without incorporating information on the spatially adjacent data, i.e., hyperspectral data are usually not treated as images, but as unordered listings of spectral measurements with no particular spatial arrangement

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