A nested spatial window-based approach to target detection for hyperspectral imagery

A great challenge of hyperspectral target detection is to detect subtle targets without prior knowledge, particularly, when the targets of interest are insignificant and occur with low probabilities. This work provides a promising alternative to adaptive hyperspectral target detection. It considers a nested spatial window-based target detection (NSWTD) approach for hyperspectral imagery where a set of different spatial windows are nested and implemented to extract targets whose signatures are spectrally and spatially distinct. The use of nested spatial windows is determined by the image pixel resolution and applications. In order to demonstrate the performance of the proposed NSWTD approach, dual nested windows and three nested windows are implemented for computer simulations and real hyperspectral image experiments. The experimental results demonstrate that our proposed NSWTD approach performs effectively and improves a recent adaptive anomaly detector developed by Kwon et al. and the commonly used anomaly detector developed by Reed and Yu, referred to as RX algorithm. Its computation complexity is also very simple.