Signal and Image Processing in Hyperspectral Remote Sensing [From the Guest Editors]

In recent years, it has become clear that hyperspectral imaging has formed a core area within the geoscience and remote sensing community. Armed with advanced optical sensing technology, hyperspectral imaging offers high spectral resolution-a hyperspectral image can contain more than 200 spectral channels (rather than a few channels as in multispectral images), covering visible and near-infrared wavelengths at a resolution of about 10 nm. The result, on one hand, is significant expansion in data sizes. A captured scene can easily take 100 MB, or more. On the other hand, the vastly increased spectral information content available in hyperspectral images (or large spectral degrees of freedom in signal processing languages) creates a unique opportunity that may have previously been seen as impossible in multispectral remote sensing. We can detect difficult targets, for example, those appearing at a subpixel level. We can perform image classification with greatly improved accuracy. We can also identify underlying materials in a captured scene without prior information of the materials to be encountered, by carrying out blind unmixing.