Salient object detection in hyperspectral imagery using multi-scale spectral-spatial gradient

Spectra in hyperspectral images (HSIs) benefit identifying objects from cluttered background, thus increasing effort has been made for salient object detection in HSIs. However, most existing methods are sensitive to the spectral variation brought by uneven illumination during imaging as well as objects of various scales. To address this problem, we propose a novel multi-scale spectral-spatial gradient based salient object detection method for HSIs. Through constructing a region based hierarchical structure, we obtain various saliency maps by evaluating each region in multiple scales with a spectra-spatial gradient saliency model, which not only depicts the global region contrast with the spectral gradient, but also exploits the spatial gradient to highlight regions with semantic edges. Given these saliency maps, the final result is given as their weighted summation. The proposed method is robust to spectral variation and can adaptively detect objects of various scales. Two prior models are further integrated into the proposed saliency model to enhance the detection accuracy. Experimental results on real HSIs validate the effectiveness of the proposed method.