Superpixel-Based Extended Random Walker for Hyperspectral Image Classification | |
Cui, Binge1; Xie, Xiaoyun1; Ma, Xiudan1; Ren, Guangbo2; Ma, Yi2 | |
刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
2018-06 | |
卷号 | 56期号:6页码:3233-3243 |
关键词 | Extended random walkers (ERWs) hyperspectral image spectral-spatial image classification superpixel support vector machine (SVM) weighted graph |
ISSN号 | 0196-2892 |
DOI | 10.1109/TGRS.2018.2796069 |
英文摘要 | In this paper, a novel SuperPixel-based Extended Random Walker (SPERW) classification method for hyperspectral images is proposed that consists of three main steps. First, a multiscale segmentation algorithm is adopted to generate many superpixels, each of which represents a homogeneous region of adaptive shape and size. Then, a new weighted graph is constructed based on the superpixels in which the nodes correspond to the superpixels and the edges correspond to the links connecting two adjacent superpixels. Each edge has a weight that defines the similarity between the two superpixels. Second, a widely used pixelwise classifier, i.e., the support vector machine, is adopted to obtain classification probability maps for a hyperspectral image, which are then used to approximate the prior probabilities of the superpixels. Finally, the obtained prior probability maps of the superpixels are optimized by using the Extended Random Walker (ERW) algorithm, which encodes the spatial information both among and within the superpixels of the hyperspectral image in a weighted graph. Compared with the spectrum of a single pixel, the spectrum of a superpixel is more stable and less affected by noise; therefore, superpixels are more appropriate for adoption as the basic elements in the hyperspectral image classification. Because the spectral correlation between pixels within the same superpixel and the spatial correlation among adjacent superpixels are both well considered in the ERW-based global optimization framework, the proposed method shows high classification accuracy on four widely used real hyperspectral data sets even when the number of training samples is relatively small. |
资助项目 | Shandong Province Natural Science Foundation of China[ZR2014DQ030] |
WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000433328400018 |
内容类型 | 期刊论文 |
源URL | [http://ir.fio.com.cn/handle/2SI8HI0U/2884] |
专题 | 业务部门_海洋物理与遥感研究室 |
作者单位 | 1.Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao 266590, Peoples R China; 2.State Ocean Adm, Inst Oceanog 1, Qingdao 266061, Peoples R China |
推荐引用方式 GB/T 7714 | Cui, Binge,Xie, Xiaoyun,Ma, Xiudan,et al. Superpixel-Based Extended Random Walker for Hyperspectral Image Classification[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2018,56(6):3233-3243. |
APA | Cui, Binge,Xie, Xiaoyun,Ma, Xiudan,Ren, Guangbo,&Ma, Yi.(2018).Superpixel-Based Extended Random Walker for Hyperspectral Image Classification.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,56(6),3233-3243. |
MLA | Cui, Binge,et al."Superpixel-Based Extended Random Walker for Hyperspectral Image Classification".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.6(2018):3233-3243. |
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