| 空-谱信息与稀疏表示相结合的高光谱遥感影像分类 |
| |
| 引用本文： | 杨钊霞,邹峥嵘,陶超,田彦平,何小飞. 空-谱信息与稀疏表示相结合的高光谱遥感影像分类[J]. 测绘学报, 2015, 44(7): 775-781. DOI: 10.11947/j.AGCS.2015.20140207 |
| |
| 作者姓名： | 杨钊霞 邹峥嵘 陶超 田彦平 何小飞 |
| |
| 作者单位： | 中南大学地球科学与信息物理学院, 湖南 长沙 410083 |
| |
| 基金项目： | The National Basic Research Program of China(973 Program)(No 2．012CB719903)；The National Natural Science Foundation of China (No ．41301453)；The China Postdoctoral Science Foundation(No ．2013M530361)；Research Fund for the Doctoral Program of Higher Education (No ．20130162120027)基金项目国家973计划(2012CB719903)；国家自然科学基金(41301453)；中国博士后科学基金(2013M530361)；教育部博士点基金(20130162120027) |
| |
| 摘 要： | 针对传统的高光谱遥感影像分类中多依赖光谱信息而忽视空间信息以及提取的特征维数高的问题,提出了一种空-谱信息与稀疏表示相结合的分类算法。首先,利用最小噪声分离对原始影像进行降维,在此基础上,对主成分图上局部影像块内的所有像素进行重组,并用排序的方法得到旋转不变的空-谱特征。然后,对空-谱特征进行监督学习得到字典,并将提取的测试样本的空-谱特征编码到字典中以得到测试样本的稀疏表示。最后,使用支持向量机分类器(SVM)对高光谱影像进行分类。3组高光谱数据试验表明,与传统的分类方法比较,本文方法能有效提高分类精度。
|
| 关 键 词： | 高光谱影像 最小噪声分离 空-谱特征 字典学习 稀疏表示 |
| 收稿时间： | 2014-04-23 |
| 修稿时间： | 2014-10-28 |
Hyperspectral Image Cl assification Based on the Combination of Spati al-spectral Feature and Sparse Representation |
| |
| YANG Zhaoxia,ZOU Zhengrong,TAO Chao,TIAN Yanping,HE Xiaofei. Hyperspectral Image Cl assification Based on the Combination of Spati al-spectral Feature and Sparse Representation[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(7): 775-781. DOI: 10.11947/j.AGCS.2015.20140207 |
| |
| Authors: | YANG Zhaoxia ZOU Zhengrong TAO Chao TIAN Yanping HE Xiaofei |
| |
| Affiliation: | School of Geosciences and Info-Physics, Central South University, Changsha 410083, China |
| |
| Abstract: | In order to avoid the problem of being over‐dependent on high‐dimensional spectral feature in the traditional hyperspectral image cl assification ,a novel approach based on the combination of spati al‐spectral feature and sparse representation is proposed in this paper .Firstly ,we extract the spati al‐spectral feature by reorganizing the local image patch with the first d principal components(PCs) into a vector representation ,followed by a sorting scheme to make the vector invari ant to local image rotation . Secondly ,we learn the dictionary through a supervised method ,and use it to code the features from test samples afterwards .Finally ,we embed the resulting sparse feature coding into the support vector machine (SVM) for hyperspectral image cl assification .Experiments using three hyperspectral data show that the proposed method can effectively improve the cl assification accuracy comparing with traditional cl assifica‐ti on methods . |
| |
| Keywords: | hyperspectral image minimum noise fraction spatial-spectral feature dictionary learning sparse representation |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《测绘学报》浏览原始摘要信息 |
|
点击此处可从《测绘学报》下载全文 |
|
