A Segmentation Method for High Spatial Resolution Remote Sensing Images Based on the Fusion of Multifeatures
A Segmentation Method for High Spatial Resolution Remote Sensing Images Based on the Fusion of Multifeatures
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基于多特征融合的高空间分辨率遥感图像分割方法
DOI：
10.1109/lgrs.2018.2829807
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发表时间：
2018-06
影响因子：
4.8
通讯作者：
Zhu Yongzhong
中科院分区：
文献类型：
--
作者：
Liu Dawei;Han Ling;Ning Xiaohong;Zhu Yongzhong
提出了一种融合光谱、纹理和形状特征的高空间分辨率遥感图像分割方法。该方法在初始分割的基础上，利用区域合并的思想得到最终的分割结果。通过非下采样轮廓波变换获得区域的纹理特征。结合纹理、光谱和形状特征，建立了一个综合的区域合并准则。为了保证算法的效率，在区域合并阶段采用区域邻接图和最近邻图来维护邻接关系。采用全局优化策略逐步实现图像分割。通过两个实验验证了该方法的有效性。通过实验验证了不同特征组合的合并准则对分割结果的影响。另一个实验比较了该方法与嵌入在ENVI和eCognition中的分割方法的效果。实验结果表明，该方法能充分利用图像的特征，实现准确、高效的分割。
A novel method based on the fusion of spectral, texture, and shape features is proposed for the segmentation of high spatial resolution remote sensing images. The method uses the region merging idea to get the final segmentation result on the basis of initial segmentation. Texture features of the regions are obtained by the nonsubsampled contourlet transform. An integrated region merging criterion is built by combining the texture, spectral, and shape features. To ensure the efficiency of the method, the region adjacency graph and the nearest neighbor graph are used to maintain the adjacency relations in the region merging stage. The global optimization strategy is adopted to realize the image segmentation gradually. Two experiments are performed to verify the effectiveness of the proposed method. One experiment validates the influence of the merging criteria with different feature combinations on the segmentation results. Another experiment compares the effect of the method with the segmentation methods embedded in ENVI and eCognition. Experimental results show that the method can make full use of the features of the images to achieve accurate and efficient segmentations.
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DOI：
10.1109/36.73663
发表时间：
1991-03
期刊：
IEEE Trans. Geosci. Remote. Sens.
影响因子：
--
作者：
J. Ton;J. Sticklen;Anil K. Jain
通讯作者：
J. Ton;J. Sticklen;Anil K. Jain
影响因子：
1.3
作者：
Carleer, AP;Debeir, O;Wolff, E
通讯作者：
Wolff, E
影响因子：
1.3
作者：
Hu, XY;Tao, CV;Prenzel, B
通讯作者：
Prenzel, B
DOI：
10.1016/j.isprsjprs.2014.09.011
发表时间：
2014-12
影响因子：
12.7
作者：
Xue-liang Zhang;P. Xiao;Xuezhi Feng;Jiangeng Wang;Zuo Wang
通讯作者：
Xue-liang Zhang;P. Xiao;Xuezhi Feng;Jiangeng Wang;Zuo Wang
影响因子：
8.2
作者：
Nan Li;H. Huo;T. Fang
通讯作者：
Nan Li;H. Huo;T. Fang