| 主动学习与图的半监督相结合的高光谱影像分类 |
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| 引用本文： | 田彦平,陶超,邹峥嵘,杨钊霞,何小飞.主动学习与图的半监督相结合的高光谱影像分类[J].测绘学报,2015,44(8):919-926. |
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| 作者姓名： | 田彦平 陶超 邹峥嵘 杨钊霞 何小飞 |
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| 作者单位： | 中南大学 地球科学与信息物理学院, 湖南 长沙 410083 |
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| 基金项目： | 国家973计划(2012CB719903)，国家自然科学基金(41301453)，中国博士后面上基金(2013M530361)，教育部博士点基金(20130162120027)Foundation support:The National Basic Research Program of China (973 Program )(2012CB719903)，The National Natural Science Foundation of China(41301453)，China Postdoc-toral Science Foundation(2013M530361)，Specialized Research Foud for the Doctoral Program of Higher Education(20130162120027) |
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| 摘 要： | 针对当前高光谱影像分类时,人工标注样本费时费力以及大量未标记样本未有效利用等问题,提出了一种主动学习与图的半监督相结合的高光谱影像分类方法。首先,将像素的光谱信息与其邻域内的空间信息相结合,利用重排序机制得到一种旋转不变的空谱特征表达。在此基础上,利用主动学习算法选择最不确定性样本(即分类模糊度最大的样本),提交操作者标注得到标记样本集。最后将该标记样本与未标记样本组合,用于图的半监督分类。该算法可保证类别边界样本的选择,利于分类器的边界构造,同时,在较少标记样本情况下,通过引入大量的未标记样本,可以达到较好的分类效果。在3幅真实高光谱影像上的试验表明,该方法可以取得精度较高的分类结果。
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| 关 键 词： | 高光谱影像分类 图的半监督学习 主动学习 空-谱特征 |
| 收稿时间： | 2014-04-21 |
| 修稿时间： | 2014-10-21 |
Semi-supervised Graph-based Hyperspectral Image Classification with Active Learning |
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| TIAN Yanping,TAO Chao,ZOU Zhengrong,YANG Zhaoxia,HE Xiaofei.Semi-supervised Graph-based Hyperspectral Image Classification with Active Learning[J].Acta Geodaetica et Cartographica Sinica,2015,44(8):919-926. |
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| Authors: | TIAN Yanping TAO Chao ZOU Zhengrong YANG Zhaoxia HE Xiaofei |
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| Institution: | School of Geosciences and Info-Physics, Central South University, Changsha 410083, China |
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| Abstract: | Currently,it is difficult and time-consuming to obtain enough labeled samples for hyperspectral image(HSI)classification,while numerous unlabeled samples can be easily identified but unused.Here,in order to overcome these shortcomings ,we proposed a semi-supervised graph-based combined with active learning mechanism approach in this paper.Firstly, we extracted the spatio-spectral feature by reorganizing the spectrum of a pixel with its neighbors,followed by a sorting scheme to make the feature representation to be rotation invariant.Then,the most uncertain samples (namely largest ambiguity samples for classifier)were selected for operator to label with active learning algorithm.Finally,both labeled samples and unlabeled samples were used for semi-supervised classification.The proposed algorithm could guarantee that boundary samples were selected,which would help construct the boundary of a classifier.Simultaneously,even when less labeled samples were available,the proposed method achieved a good classification result by introducing a large number of unlabeled samples.The experimental results on three real hyperspectral images confirmed that the proposed method can obtain higher classifica-tion accuracy. |
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| Keywords: | hyperspectral image classification semi-supervised graph-based learning active learning spatio-spectral feature |
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