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25 July 2002 Hyperspectral imagery and segmentation
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Hyperspectral imagery (HSI), a passive infrared imaging technique which creates images of fine resolution across the spectrum is currently being considered for Army tactical applications. An important tactical application of infra-red (IR) hyperspectral imagery is the detection of low contrast targets, including those targets that may employ camouflage, concealment and deception (CCD) techniques [1,2]. Spectral reflectivity characteristics were used for efficient segmentation between different materials such as painted metal, vegetation and soil for visible to near IR bands in the range of 0.46-1.0 microns as shown previously by Kwon et al [3]. We are currently investigating the HSI where the wavelength spans from 7.5-13.7 microns. The energy in this range of wavelengths is almost entirely emitted rather than reflected, therefore, the gray level of a pixel is a function of the temperature and emissivity of the object. This is beneficial since light level and reflection will not need to be considered in the segmentation. We will present results of a step-wise segmentation analysis on the long-wave infrared (LWIR) hyperspectrum utilizing various classifier architectures applied to both the full-band, broad-band and narrow-band features derived from the Spatially Enhanced Broadband Array Spectrograph System (SEBASS) data base. Stepwise segmentation demonstrates some of the difficulties in the multi-class case. These results give an indication of the added capability the hyperspectral imagery and associated algorithms will bring to bear on the target acquisition problem.
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Mark C. Wellman and Nasser M. Nasrabadi "Hyperspectral imagery and segmentation", Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002);

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