Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images
文献类型：期刊论文
| 作者 | Gong, Maoguo1; Zhang, Mingyang1; Yuan, Yuan2
|
| 刊名 | ieee transactions on geoscience and remote sensing
 |
| 出版日期 | 2016 |
| 卷号 | 54期号:1页码:544-557 |
| 关键词 | Band selection evolutionary algorithm (EA) hyperspectral image multiobjective optimization |
| ISSN号 | 01962892 |
| 产权排序 | 2 |
| 英文摘要 | band selection is an important preprocessing step for hyperspectral image processing. many valid criteria have been proposed for band selection, and these criteria model band selection as a single-objective optimization problem. in this paper, a novel multiobjective model is first built for band selection. in this model, two objective functions with a conflicting relationship are designed. one objective function is set as information entropy to represent the information contained in the selected band subsets, and the other one is set as the number of selected bands. then, based on this model, a new unsupervised band selection method called multiobjective optimization band selection (mobs) is proposed. in the mobs method, these two objective functions are optimized simultaneously by a multiobjective evolutionary algorithm to find the best tradeoff solutions. the proposed method shows two unique characters. it can obtain a series of band subsets with different numbers of bands in a single run to offer more options for decision makers. moreover, these band subsets with different numbers of bands can communicate with each other and have a coevolutionary relationship, which means that they can be optimized in a cooperative way. since it is unsupervised, the proposed algorithm is compared with some related and recent unsupervised methods for hyperspectral image band selection to evaluate the quality of the obtained band subsets. experimental results show that the proposed method can generate a set of band subsets with different numbers of bands in a single run and that these band subsets have a stable good performance on classification for different data sets. |
| WOS标题词 | science & technology ; physical sciences ; technology |
| 学科主题 | geochemistry & geophysics ; engineering, electrical & electronic ; remote sensing ; imaging science & photographic technology |
| 类目[WOS] | geochemistry & geophysics ; engineering, electrical & electronic ; remote sensing ; imaging science & photographic technology |
| 研究领域[WOS] | geochemistry & geophysics ; engineering ; remote sensing ; imaging science & photographic technology |
| 关键词[WOS] | extreme learning-machine ; principal components transform ; dimensionality reduction ; mutual information ; clonal selection ; classification ; algorithm ; accuracy ; removal ; quality |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000364833900042 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/27497]  |
| 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.Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Int Res Ctr Intelligent Percept & Computat, Minist Educ, Xian 710071, Peoples R China 2.Chinese Acad Sci, Ctr Opt IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gong, Maoguo,Zhang, Mingyang,Yuan, Yuan. Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images[J]. ieee transactions on geoscience and remote sensing,2016,54(1):544-557. |
| APA | Gong, Maoguo,Zhang, Mingyang,&Yuan, Yuan.(2016).Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images.ieee transactions on geoscience and remote sensing,54(1),544-557. |
| MLA | Gong, Maoguo,et al."Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images".ieee transactions on geoscience and remote sensing 54.1(2016):544-557. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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。