Hyperspectral signal unmixing based on constrained non-negative matrix factorization approach
文献类型：期刊论文
| 作者 | Du, Bo1; Wang, Shaodong1; Wang, Nan1; Zhang, Lefei1; Tao, Dacheng1; Zhang, Lifu1 |
| 刊名 | Neurocomputing
 |
| 出版日期 | 2016 |
| 卷号 | 204页码:153-161 |
| 关键词 | GROUNDWATER DEPLETION LAND WATER VARIABILITY VALIDATION SYSTEM MODEL |
| 通讯作者 | Wang, Nan (wangnan@radi.ac.cn) |
| 英文摘要 | Hyperspectral unmixing is a hot topic in signal and image processing. A set of high-dimensional data matrices can be decomposed into two sets of non-negative low-dimensional matrices by Non-negative matrix factorization (NMF). However, the algorithm has many local solutions because of the non-convexity of the objective function. Some algorithms solve this problem by adding auxiliary constraints, such as sparse. The sparse NMF has a good performance but the result is unstable and sensitive to noise. Using the structural information for the unmixing approaches can make the decomposition stable. Someone used a clustering based on Euclidean distance to guide the decomposition and obtain good performance. The Euclidean distance is just used to measure the straight line distance of two points. However, the ground objects usually obey certain statistical distribution. It[U+05F3]s difficult to measure the difference between the statistical distributions comprehensively by Euclidean distance. Kullback-Leibler divergence (KL divergence) is a better metric. In this paper, we propose a new approach named KL divergence constrained NMF which measures the statistical distribution difference using KL divergence instead of the Euclidean distance. It can improve the accuracy of structured information by using the KL divergence in the algorithm. Experimental results based on synthetic and real hyperspectral data show the superiority of the proposed algorithm with respect to other state-of-the-art algorithms. © 2016 Elsevier B.V. |
| 学科主题 | Computer Science |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20161802338997 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39445]  |
| 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. Computer School, Wuhan University, China 2. Institute of remote sensing and digital earth, China academy of sciences, China 3. Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney, Australia |
| 推荐引用方式 GB/T 7714 | Du, Bo,Wang, Shaodong,Wang, Nan,et al. Hyperspectral signal unmixing based on constrained non-negative matrix factorization approach[J]. Neurocomputing,2016,204:153-161. |
| APA | Du, Bo,Wang, Shaodong,Wang, Nan,Zhang, Lefei,Tao, Dacheng,&Zhang, Lifu.(2016).Hyperspectral signal unmixing based on constrained non-negative matrix factorization approach.Neurocomputing,204,153-161. |
| MLA | Du, Bo,et al."Hyperspectral signal unmixing based on constrained non-negative matrix factorization approach".Neurocomputing 204(2016):153-161. |
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。