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Overview and Introduction
The past few years have witnessed tremendous advances in hyperspectral imaging where statistical signal processing has played a pivotal role in driving algorithm design and development for hyperspectral data exploitation. It has attracted attention of those who come from different disciplinary areas by exploring new applications and making connections between remote sensing and other engineering fields. In recent years, there has been a significant increase in participation in various conferences and venues related to this area, which in turn has provided evidence that hyperspectral signal and image processing has broken away from traditional spatial domain analysis–based remote sensing and successfully branched out to stand alone as a single research topic, similar to signal processing that evolved as a separate area from communications in the late 1970s. On the contrary issues related to high spectral resolution provided by hyperspectral imaging sensors have also changed the ways in which algorithms are designed and developed. As a consequence, many problems such as subpixels and mixed pixels that are generally encountered in hyperspectral image processing, but do not occur in classical two-dimensional (2D) image processing, have become major issues for traditional spatial domain-based techniques. This is because the concept of “seeing-is-believing” by visual inspection, which has been widely used in image processing, cannot resolve issues of targets ...
