Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields
文献类型：期刊论文
| 作者 | Yu, Haoyang1; Gao, Lianru1; Li, Jun1; Li, Shan Shan1; Zhang, Bing1; Benediktsson, Jón Atli1 |
| 刊名 | Remote Sensing
 |
| 出版日期 | 2016 |
| 卷号 | 8期号:4 |
| 关键词 | EAST CHINA SEA ABSORPTION-COEFFICIENTS OPTICAL CLASSIFICATION ATMOSPHERIC CORRECTION WATERS PHYTOPLANKTON REFLECTANCE VARIABILITY ALGORITHMS PRODUCTS |
| 通讯作者 | Gao, Lianru (gaolr@radi.ac.cn) |
| 英文摘要 | This paper introduces a new supervised classification method for hyperspectral images that combines spectral and spatial information. A support vector machine (SVM) classifier, integrated with a subspace projection method to address the problems of mixed pixels and noise, is first used to model the posterior distributions of the classes based on the spectral information. Then, the spatial information of the image pixels is modeled using an adaptive Markov random field (MRF) method. Finally, the maximum posterior probability classification is computed via the simulated annealing (SA) optimization algorithm. The combination of subspace-based SVMs and adaptive MRFs is the main contribution of this paper. The resulting methods, called SVMsub-eMRF and SVMsub-aMRF, were experimentally validated using two typical real hyperspectral data sets. The obtained results indicate that the proposed methods demonstrate superior performance compared with other classical hyperspectral image classification methods. © 2016 by the authors. |
| 学科主题 | Remote Sensing |
| 类目[WOS] | Remote Sensing |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20162302478807 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39241]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 2.100094, China 3. The School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 4.100049, China 5. School of Geography and Planning, Sun Yat-sen University, Guangzhou 6.510275, China 7. Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik IS 8.107, Iceland |
| 推荐引用方式 GB/T 7714 | Yu, Haoyang,Gao, Lianru,Li, Jun,et al. Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields[J]. Remote Sensing,2016,8(4). |
| APA | Yu, Haoyang,Gao, Lianru,Li, Jun,Li, Shan Shan,Zhang, Bing,&Benediktsson, Jón Atli.(2016).Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields.Remote Sensing,8(4). |
| MLA | Yu, Haoyang,et al."Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields".Remote Sensing 8.4(2016). |
入库方式： OAI收割
来源：遥感与数字地球研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。