Salient object detection in hyperspectral imagery using spectral gradient contrast

Salient object detection in hyperspectral imagery has drawn people's attention in recent years. Some detection methods which focus on extending Itti's visual saliency model into spectral domain have been proposed. However, these methods are sensitive to high-contrast edges and cannot preserve boundary of salient object well. To address these shortcomings, we propose a region-based spectral gradient contrast method for salient object detection in this paper. First, we calculate gradient along each spectral vector of the input hyperspectral imagery. Then over segmentation and clustering methods are applied on gradient data to get a group of image regions. Finally, center prior and local contrast are employed to compute the saliency score of each region, with which the salient object can be obtained. Experimental results on four datasets demonstrate that the proposed method outperforms several competing methods on detection accuracy.