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Image Segmentation of Hyperspectral Imagery

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Hyperspectral imagery HSI, a passive technique creating a large collection of images of fine resolution across the infrared spectrum is currently being considered for U.S. Army tactical applications. An important tactical application of infrared 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 um as shown previously by Kwon et al. 3. We are currently investigating the HSI region spanning the wavelengths from 7.5 to 13.7 m. The energy in this range of wavelengths is almost entirely emitted rather than reflected therefore, the gray level of a pixel is a function ofthe temperature and emissivity of the object. This is beneficial because light level and reflection will not need to be considered in the segmentation. We will present results of segmentation analysis on the long-wave infrared LWIR hyperspectrum using a simple distance metric applied to full-band and sub-band HSI data sets, neural network-based classification approaches, and principal component analysis PCA applied to relative temperature profiles derived from the Spatially Enhanced Broadband Array Spectrograph System SEBASS database. A stepwise segmentation will be demonstrated using a back-propagation neural network, which outlines some of the difficulties in the multi-class case. Overall, these results should give an early indication of the added capability hyperspectral imagery and algorithms will bring to bear on the target acquisition problem.

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  • Optics

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