An automatic nonlinear correlation approach for processing of hyperspectral images
Hyperspectral imaging technology demands sophisticated processing techniques that offer precise characterizations of complex spectral signatures. A nonlinear correlator structure is implemented for interference mitigation and object recognition. A key asset is the correlator's applicability to both the spatial (two-dimensional) and spectral (one-dimensional) domains, thus ideal for hyperspectral processing. The process consists of a standard convolution summed with a nonlinear adaptive term. The premise is the same in each case but the mathematical implementation is different. By performing the correlation calculations in the frequency domain, the processing algorithm is efficient, robust, and well suited for implementation on a parallel processing computational architecture. The nonlinear correlator depends on two parameters and an algorithm to determine these parameters based only on the input image (two-dimensional) or spectral signature (one-dimensional) is presented. Based on the results with the selected spatial and spectral templates, a target is identified and the spatial coordinates as well as the spectral signature are input to the final fusion stage, which analyses both spectral and spatial signatures for a correct target identification. Several examples are given and insights to template (mask) selection are provided.