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There is increasing interest in using wide-area standoff airborne hyperspectral sensors to detect potential targets at large oblique viewing angles. Under such conditions, the intervening atmosphere between the targets and the imager can attenuate and alter the detected signal. To help compensate for the reduced signal for long range viewing, recent efforts have focused on using hyperspectral sensors to collect imagery derived from short wave radiation SWIR (1-2.5 μm) rather than the more standard visible-near infrared radiation Vis-NIR (0.4-1.0 μm). However, unlike imagery collected using Vis-NIR, there is currently a relative dearth of analytical and classification algorithms that only use SWIR. To enhance the ability to detect spectral features confined to the SWIR regime, this study has examined extracting vegetation features in the SWIR. Visible-near infrared hyperspectral imagery has successfully extracted vegetation within a scene through computation of the normalized difference vegetation index (NDVI). The Visible NDVI computes a normalized difference of two bands corresponding to the chlorophyll absorption (0.67 μm) and IR edge (0.80 μm). This work extended and examined schemes for extracting vegetation within SWIR imagery. Specifically, this study examined the HYDICE data collect (0.4-2.5 μm) and the visible NDVI was used as the standard for determining the vegetation within the scene. A SWIR derived NDVI was generated using pairs of SWIR bands (1.08, 1.46 μm), (1.08, 1.57 μm), (1.08, 1.66 μm), and (1,08, 2.18 μm). All SWIR paired bands exhibited large (> 0.92) correlation coefficients with the Vis-NIR NDVI. Vis-NIR NDVI preferentially detects the greenest vegetation but the SWIR NDVI tends to favor vegetation residing in shadows. Water has large SWIR NDVI but has low reflectance throughout the SWIR. By setting a threshold, water can be eliminated from consideration and only vegetation is detected. In addition, minimizing the mean squared error between the visible and SWIR imagery can generate a suitable linear combination of all suitable bands involving SWIR wavelengths that exclude the atmospheric absorption. Using the high number of SWIR bands approach yields even larger correlation (>0.99) with visible NDVI. However, the specific coefficients used in the linear combination approach, varies from scene to scene. Therefore, using a fixed set of coefficients yields small correlation coefficient with visible NDVI.
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