Spatial neighboring clustering method for hyperspectral imagery based on kernel spectral angel cosine

The paper presented the concept of KSAC (Kernel Spectral Angel Cosine) to address the restrictions that the classification precision of hyperspectral imagery is very sensitive to segmentation threshold based on SAC (Spectral Angel Cosine). First, we defined the representation of KSAC, and then analyzed the effect of polynomial kernel-function parameter on KSAC, at last, we presented the method of spatial neighboring clustering based on KSAC. The experiments on the hyperspectral imagery of Shenzhen Red-forest field indicate that the threshold area coverage of the spatial neighboring clustering based on KSAC is extended up to nine times of that based on SAC.