Region of interest extraction based on multiscale visual saliency analysis for remote sensing images
Region of interest extraction based on multiscale visual saliency analysis for remote sensing images
复制标题
基于多尺度视觉显着性分析的遥感图像感兴趣区域提取
DOI：
10.1117/1.jrs.9.095050
复制
发表时间：
2015
影响因子：
1.7
通讯作者：
Yu Xianchuan
中科院分区：
文献类型：
--
作者：
Zhang Yinggang;Zhang Libao;Yu Xianchuan
摘要。感兴趣区域（ROI）提取是遥感图像处理的重要组成部分。然而，传统的ROI提取方法通常是基于先验知识的，依赖于分类、分割和全局搜索解决方案，耗时长，计算量大。本文提出了一种基于多尺度视觉显著性分析（MVS）的遥感图像ROI提取模型，该模型在CIE L*a*b*色彩空间中实现，类似于人眼的视觉感知。首先采用不同的方法提取图像的强度、方向和颜色特征：利用视觉注意机制，利用高斯模板的差分去除强度特征；采用整数小波变换提取方向特征；通过颜色信息含量分析，获得图像的颜色特征。然后，提出了一种新的特征竞争方法，该方法针对每个特征图的不同贡献计算每个特征图像的权重，并将它们合并到最终的显著性图中。定性和定量实验结果表明，与其他模型相比，MVS模型在ROI内部孔洞较少的情况下更有效，提取结果更准确。
Abstract. Region of interest (ROI) extraction is an important component of remote sensing image processing. However, traditional ROI extraction methods are usually prior knowledge-based and depend on classification, segmentation, and a global searching solution, which are time-consuming and computationally complex. We propose a more efficient ROI extraction model for remote sensing images based on multiscale visual saliency analysis (MVS), implemented in the CIE L*a*b* color space, which is similar to visual perception of the human eye. We first extract the intensity, orientation, and color feature of the image using different methods: the visual attention mechanism is used to eliminate the intensity feature using a difference of Gaussian template; the integer wavelet transform is used to extract the orientation feature; and color information content analysis is used to obtain the color feature. Then, a new feature-competition method is proposed that addresses the different contributions of each feature map to calculate the weight of each feature image for combining them into the final saliency map. Qualitative and quantitative experimental results of the MVS model as compared with those of other models show that it is more effective and provides more accurate ROI extraction results with fewer holes inside the ROI.
登录
查看更多内容
影响因子：
10.6
作者：
Zhicheng Li;L. Itti
通讯作者：
Zhicheng Li;L. Itti
DOI：
10.1109/cvpr.2009.5206596
发表时间：
2009-06
期刊：
2009 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
R. Achanta;S. Hemami;F. Estrada;S. Süsstrunk
通讯作者：
R. Achanta;S. Hemami;F. Estrada;S. Süsstrunk
DOI：
10.1109/jstars.2014.2319736
发表时间：
2014-05
影响因子：
5.5
作者：
Li-bao Zhang;Kaina Yang;Hao Li
通讯作者：
Li-bao Zhang;Kaina Yang;Hao Li
DOI：
10.1109/tpami.2011.272
发表时间：
2012-10-01
影响因子：
23.6
作者：
Goferman, Stas;Zelnik-Manor, Lihi;Tal, Ayellet
通讯作者：
Tal, Ayellet
影响因子：
2.9
作者：
M. Schmitt
通讯作者：
M. Schmitt