期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 13, 期 12, 页码 1752-1756出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2016.2602885
关键词
Background contrast; region-of-interest (ROI) detection; structure tensor; superpixel segmentation
类别
资金
- Chang Jiang Scholars Program [T2012122]
- Hundred Leading Talent Project of Beijing Science and Technology [Z141101001514005]
Traditional region-of-interest (ROI) detection methods for remote sensing images are generally formulated at pixel level and are less efficient when applied on large high-resolution images. This letter presents an accurate and efficient approach via superpixel-to-pixel saliency analysis for ROI detection. At first, the image is downsampled and segmented into superpixels by simple linear iterative clustering. Next, structure tensor and background contrast are used to yield superpixel feature maps for texture and color. After fusing the feature maps, the overall superpixel saliency map is obtained and then used to achieve the final pixel-level saliency map by superpixel-to-pixel mapping. Through experimentations, we validate the effectiveness and computational efficiency of the proposed model in comparison with state-of-the-art techniques.
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