属类概率距离构图的半监督高光谱图像分类

doi:10.13485/j.cnki.11-2089.2014.0182

测绘学报 ›› 2014, Vol. 43 ›› Issue (11): 1182-1189.doi: 10.13485/j.cnki.11-2089.2014.0182

属类概率距离构图的半监督高光谱图像分类

邵远杰¹,吴国平¹,马丽²

1. 中国地质大学(武汉)
2. 中国地质大学机械与电子信息学院通信工程系

收稿日期:2013-01-15 修回日期:2014-03-26 出版日期:2014-11-20 发布日期:2014-12-02
通讯作者: 马丽 E-mail:maryparisster@gmail.com
基金资助:
国家自然科学基金项目资助;中央高校基本科研业务费专项资金资助

Graph based Semi-Supervised Learning with Class-Probability Distance for Hyperspectral Remote Sensing Image Classification

SHAO Yuanjie,WU Guoping,MA Li

China University of Geosciences

Received:2013-01-15 Revised:2014-03-26 Online:2014-11-20 Published:2014-12-02
Contact: MA Li E-mail:maryparisster@gmail.com

摘要/Abstract

摘要：

提出一种利用属类概率距离构图的半监督学习算法，并应用于高光谱图像分类。首先，该算法利用基于分类的稀疏表达方法来预估未标记样本的属类概率向量，然后，利用这个概率向量对描述数据相似性的距离函数进行改造，改造后的距离函数能有效扩大异类样本点之间的距离，在新的距离函数的度量下，每个样本点的邻域中可包含更多同类的样本点。最后，将该距离函数应用于半监督学习线性邻域传播算法和标签传播算法中。在Hyperion 和AVIRIS高光谱遥感图像上的实验结果表明：相比于传统的基于图的半监督学习算法，该算法能有效提高高光谱遥感图像分类精度。

关键词: 高光谱图像分类, 图, 半监督学习, 稀疏表达, 属类概率距离

Abstract:

A class-probability distance based semi-supervised learning method is proposed for hyperspectral remote sensing image classification. In the method, Sparse Representation based Classification (SRC) is adopted for estimating the class-probability of unlabeled sample. Then a distance metric that describes the data similarity is developed based on the estimated class-probability. With this new distance metric, the distance between samples of different classes is enlarged effectively, and the neighbors of each sample can contain more samples belonging to the same class. Finally, this distance metric is applied to Linear Neighborhood Propagation and Label Propagation algorithms. Experimental results using Hyperion and AVIRIS hyperspectral remote sensing images show that the approach outperforms the existing semi-supervised learning methods in terms of classification accuracy.

Key words: hyperspectral remote sensing image classification, graph, semi-supervised learning, sparse representation, class-probability distance

中图分类号:

TP751

邵远杰吴国平马丽. 属类概率距离构图的半监督高光谱图像分类[J]. 测绘学报, 2014, 43(11): 1182-1189.

SHAO Yuanjie WU Guoping MA Li. Graph based Semi-Supervised Learning with Class-Probability Distance for Hyperspectral Remote Sensing Image Classification[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(11): 1182-1189.

参考文献

[1] KIM W，CRAWFORD M M. Adaptive Classification for Hyperspectral Image Data Using Manifold Regularization Kernel Machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(11): 4110-4121.
[2] MILLER D J, UYAR H S. A Mixture of Experts Classifier with Learning Based on Both Labeled and Unlabelled Data[C]//Advances in Neural Information Processing Systems 9. Cambridge, MA:MIT Press. 1997. 571-577.
[3] NIGAM K, MCCALLUM A K, THRUM S, MITCHELL T. Text Classification from Labeled and Unlabeled Documents Using EM[J]. Machine Learning. 2000. 39 (2-3). 103-134.
[4] RATSABY J, VENKATESH S. Learning from a Mixture of Labeled and Unlabeled Examples with Parametric Side Information[C]//Proceedings of the Eighth Annual Conference on Computational Learning Theory. 1995. 412-417.
[5] CASTELLI V, COVER T. The Exponential Value of Labeled Samples[J]. Pattern Recognition Letters. 1995. 16 (1). 105-111.
[6] YAROWSKY D. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods[C]// Proceedings of the 33rd annual meeting of the Association of Computational Linguistics. Cambridge, MA. 1995. I 89-196.
[7] RILOFF E, WIEBE J, WILSON T. Learning Subjective Nouns Using Extraction Pattern Bootstrapping[C]// Proceedings of the 7th Conference on Natural Language Learning. 2003.
[8] ROSENBERG C, HEBERT M, SCHNEIDERMAN H. Semi-supervised Self-training of Object Detection Models[C]//Proceedings.of the 7th IEEE Workshop on Applications of Computer Vision.2005.
[9] BLUM A, MITCHELL T. Combining Labeled and Lnlabeled Data with Co-training[C]//Proceedings of the 11thAnnual Conference on Learning Theory. Madison, WI. 1998. 92-100.
[10] CHAPELLE O, ZIEN A. Semi-supervised Classification by Low Density Separation[C]//Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. 2005. 57-64.
[11] JOACHIMS T. Transductive Inference for Text Classification Using Support Vector Machines[C]//Proceedings of the 16th International Conference on Machine Learning. Bled,Slovenia. 1999. 200-209.
[12] ZHU X J, LAFFERTY J, GHAHRAMANI Z. Semi-supervised Learning using Gaussian Fields and Harmonic Functions[C]//Proceedings of the 20th International Conference on Machine Learning, Menlo Park, California: AAAI, 2003: 912-919.
[13] ZHOU D Y, BOUSQUET O, LAL T, et al. Learning with Local and Global Consistency[C]//Advances in Neural Information Processing Systems 16, Massachusetts, 2004, 16: 321-328.
[14] WANG F, ZHANG C S. Label Propagation through Linear Neighborhoods[J]. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(1): 55-67.
[15] ZHAO Yinghai, CAI Junjie, WU Xiuqing, et al. Sparse Graph Based Transductive Multi-label Learning for Video Concept Detection[J]. Jounal of Pattern Recognition & Artificial Intelligence, 2011, 24(6): 825-831. (赵英海, 蔡俊杰, 吴秀清, 等. 基于稀疏化图结构的转导多标注视频概念检测算法[J]. 模式识别与人工智能, 2011, 24(6):825-831.)
[16] ROHBAN M H , RABIEE H R.Supervised Neighborhood Graph Construction for Semi-supervised Classification[J]. Pattern Recognition Letters, 2012, 45(4):1363-1372.
[17] KOBAYASHI T, WATANABE K , OTSU N. Logistic Label Propagation[J]. Pattern Recognition Letters, 2012, 33(5):580-588.
[18] ZHU X J, GOLDBERG A B. Introduction to Semi-supervised Learning [M]. USA:Morgan & Claypool, 2009.
[19] WRIGHT J, YANG A Y, GANESH A, et al. Robust Face Recognition via Sparse Representation[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2009, 312(2):210-227.
[20] RIDDER D D, KOUROPTEVA O, OKUN O, et al. Supervised Locally Linear Embedding[J]. Lecture Notes in Computer Science, 2003: 333-341.
[21] GAO Hengzhen. Research on Classification Techinique for Hyperspectral Remote Sensing Imagery[D].[Ph.D.dissertation],Nat-
ional University of Defense Technology, 2011.(高恒振. 高光谱遥感图像分类技术研究 [D].[博士论文]. 国防科学技术大学,2011.)

属类概率距离构图的半监督高光谱图像分类

Graph based Semi-Supervised Learning with Class-Probability Distance for Hyperspectral Remote Sensing Image Classification

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