Electro-Optical (EO) imaging sensors are widely used for a range of tasks, e.g. for Target Acquisition (TA: detection, recognition and identification of (military) relevant objects) or visual search. These tasks can be performed by a human observer, by an algorithm (Automatic Target Recognition) or by both (Aided Target Recognition). In the past decades, the development of night vision devices in the thermal infrared and image intensifying systems has greatly extended the applicability of EO systems. Despite of these rapid developments, the current generation of sensors has important limitations. Until now, operational thermal imagers are sensitive to IR (infrared) radiation from a single spectral band in the Long Wave (8-14 μm, LWIR) or Mid Wave (3-5 μm, MWIR) infrared region. These so-called broad band sensors basically produce a monochrome (i.e. a black-and-white pan-chromatic) image that deviates considerably from a normal daylight view, and is based on temperature contrasts in a scene. With these systems, the distinction between real targets and decoys, r between military and civilian targets is often difficult to make. Also, camouflaged targets or targets hat are hidden deep in the woods are difficult to detect. Recognizing different objects and materials may be difficult. Examples of misinterpretations when using an Image Intensifier system are grass that looks like snow, or trees that look like bushes, when seen from a helicopter. These misinterpretations may lead to disorientation (loss of Situational Awareness) or to a (fatal) wrong distance estimation. Currently, multi-band and hyperspectral imaging sensors in the thermal infrared are under development. Traditionally hyperspectral imagers were developed for satellites with applications ranging from monitoring the environment, climate analysis, detection of pollution and fires. These systems also promise significant improvements in military task performance. With these new systems, targets may be distinguished not only on the basis of differences in radiation magnitude, but also on differences in spectral properties.
CITATION STYLE
A., M., & B.W., P. (2011). Hyperspectral Data Analysis and Visualization. In Knowledge-Oriented Applications in Data Mining. InTech. https://doi.org/10.5772/16218
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