Unsupervised feature learning for scene classification of high resolution remote sensing image
文献类型：会议论文
| 作者 | Fu, Min1,2 ; Yuan, Yuan1; Lu, Xiaoqiang1
|
| 出版日期 | 2015 |
| 会议名称 | ieee china summit and international conference on signal and information processing, chinasip 2015 |
| 会议日期 | 2015-07 |
| 会议地点 | chengdu, china |
| 页码 | 206-210 |
| 通讯作者 | lu, xiaoqiang |
| 英文摘要 | due to the rapid development of various satellite sensors, a large amount of high resolution remote sensing images can be obtained. in order to efficiently represent the scenes from these high resolution images, an unsupervised feature learning method is proposed for high resolution image scene classification. in the proposed method, a set of filter banks are learned in an unsupervised manner from the unlabeled image patches, which are robust, efficient and do not need elaborately designed descriptors such as sift. and then, each image is encoded by these filter banks using a soft distance assignment scheme, generating a final feature vector to excellently represent the image scene. finally, by virtue of the traditional svm classifier, the sematic concepts of different scenes can be categorized. experimental evaluation on the the high resolution remote sensing images demonstrates the effectiveness and good performance of the proposed method. © 2015 ieee. |
| 收录类别 | EI |
| 产权排序 | 1 |
| 会议录 | 2015 ieee china summit and international conference on signal and information processing, chinasip 2015 - proceedings
 |
| 会议录出版者 | institute of electrical and electronics engineers inc. |
| 语种 | 英语 |
| ISBN号 | 9781479919482 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/27822]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'An Institute of Optics and Precision Mechanics, Xi'an Shaanxi, China 2.University of the Chinese Academy of Sciences, 19A Yuquanlu, Beijing, China |
| 推荐引用方式 GB/T 7714 | Fu, Min,Yuan, Yuan,Lu, Xiaoqiang. Unsupervised feature learning for scene classification of high resolution remote sensing image[C]. 见:ieee china summit and international conference on signal and information processing, chinasip 2015. chengdu, china. 2015-07. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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