期刊
NEUROCOMPUTING
卷 291, 期 -, 页码 215-225出版社
ELSEVIER
DOI: 10.1016/j.neucom.2018.02.070
关键词
Spatial-spectral gradient; Multi-scale analysis; Salient object detection; Hyperspectral images
资金
- National Natural Science Foundation of China [61671385, 61231016, 61571354]
- Natural Science Basis Research Plan in Shaanxi Province of China [2017JM6021]
- China Postdoctoral Science Foundation [158201]
- Innovation Foundation for Doctoral Dissertation of Northwestern Polytechnical University [CX201521]
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. (c) 2018 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据