Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method
文献类型：期刊论文
| 作者 | Zhao, Yifei1,2,3; Su, Fenzhen1,2; Yan, Fengqin1,2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020-05-01 |
| 卷号 | 12期号:9页码:20 |
| 关键词 | hyperspectral image superpixel weighted connectivity graph discrete potential semi-supervised classification |
| DOI | 10.3390/rs12091528 |
| 通讯作者 | Su, Fenzhen(sufz@lreis.ac.cn) |
| 英文摘要 | Hyperspectral image (HSI) classification plays an important role in the automatic interpretation of the remotely sensed data. However, it is a non-trivial task to classify HSI accurately and rapidly due to its characteristics of having a large amount of data and massive noise points. To address this problem, in this work, a novel, semi-supervised, superpixel-level classification method for an HSI was proposed based on a graph and discrete potential (SSC-GDP). The key idea of the proposed scheme is the construction of the weighted connectivity graph and the division of the weighted graph. Based on the superpixel segmentation, a weighted connectivity graph is constructed usingthe weighted connection between a superpixel and its spatial neighbors. The generated graph is then divided into different communities/sub-graphs by using a discrete potential and the improved semi-supervised Wu-Huberman (ISWH) algorithm. Each community in the weighted connectivity graph represents a class in the HSI. The local connection strategy, together with the linear complexity of the ISWH algorithm, ensures the fast implementation of the suggested SSC-GDP method. To prove the effectiveness of the proposed spectral-spatial method, two public benchmarks, Indian Pines and Salinas, were utilized to test the performance of our proposal. The comparative test results confirmed that the proposed method was superior to several other state-of-the-art methods. |
| WOS关键词 | FEATURE-EXTRACTION ; SEGMENTATION ; ALGORITHM ; SUPPORT ; OCEAN |
| 资助项目 | National Natural Science Foundation of China[41890854] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000543394000175 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/162362]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Su, Fenzhen |
| 作者单位 | 1.Nanjing Univ, Collaborat Innovat Ctr South China Sea Studies, Nanjing 210093, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Yifei,Su, Fenzhen,Yan, Fengqin. Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method[J]. REMOTE SENSING,2020,12(9):20. |
| APA | Zhao, Yifei,Su, Fenzhen,&Yan, Fengqin.(2020).Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method.REMOTE SENSING,12(9),20. |
| MLA | Zhao, Yifei,et al."Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method".REMOTE SENSING 12.9(2020):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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