Refined Target Recognition in Hyperspectral Imagery Based on Spectral Reflectance and Derivative Information
نویسندگان
چکیده
A hyperspectral image can be considered as an image cube where the third dimension is the spectral domain represented by hundreds of spectral wavelengths. A hyperspectral image pixel is actually a column vector with dimension equal to the number of spectral bands and contains valuable spectral information that can be used to detect and identify a variety of nature and man-made material. Some spectral similarity measures are advanced such as spectral angle mapper (SAM), spectral correlation mapper (SCM), spectral information divergence (SID) etc [1]. They make use of spectral reflectance only that is limited and is a challenge for refined target. In this paper, a new recognition algorithm is proposed, which includes two key techniques: one is the cooperation of spectral reflectance and derivative information, and the other is the fusion of the preliminary target recognitions from different channels. The algorithm proposed is effective in recognizing refined target in hyperspectral imagery. 2. BACKGROUND
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