融合光谱-空间信息的高光谱遥感影像增量分类算法

doi:10.11947/j.AGCS.2015.20140388

测绘学报 ›› 2015, Vol. 44 ›› Issue (9): 1003-1013.doi: 10.11947/j.AGCS.2015.20140388

融合光谱-空间信息的高光谱遥感影像增量分类算法

王俊淑^1,2, 江南^1,2, 张国明³, 李杨^1,2, 吕恒^1,2

1. 南京师范大学虚拟地理环境教育部重点实验室, 江苏南京 210023;
2. 江苏省地理信息资源开发与利用协同创新中心, 江苏南京 210023;
3. 江苏省卫生统计信息中心, 江苏南京 210008

收稿日期:2014-07-21 修回日期:2015-06-08 出版日期:2015-09-24 发布日期:2015-09-24
作者简介:王俊淑(1985—),女,博士生,助理研究员,研究方向为高光谱遥感影像智能信息提取。E-mail:jlsdwjs@126.com
基金资助:
国家自然科学基金(41171269);环保公益性行业科研专项(201309037);江苏高校优势学科建设工程资助项目(164320H101);地球系统科学数据共享平台项(2005DKA32300);江苏省高校自然科学研究面上项目(14KJB170010);江苏省普通高校研究生科研创新计划(1812000002A403)

Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information

WANG Junshu^1,2, JIANG Nan^1,2, ZHANG Guoming³, LI Yang^1,2, LV Heng^1,2

1. Key Laboratory for Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China;
2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;
3. Center of Health Statistics and Information of Jiangsu Province, Nanjing 210008, China

Received:2014-07-21 Revised:2015-06-08 Online:2015-09-24 Published:2015-09-24
Contact: 江南,njiang@njnu.edu.cn E-mail:njiang@njnu.edu.cn
Supported by:
The National Natural Science Foundation of China (No.41171269) The National Environmental Protection Public Welfare Science and Technology Research Program of China (No.201309037) A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (No.164320H101) Data-sharing Network of Earth System Science (No.2005DKA32300) Program of Natural Science Research of Jiangsu Higher Education Institutions of China (No.14KJB170010) Colleges and Universities in Jiangsu Province Plans to Graduate Research and Innovation (No.1812000002A403)

摘要/Abstract

摘要： 提出了一种融合光谱和空间结构信息的高光谱遥感影像增量分类算法INC_SPEC_MP_ext。通过主成分分析(PCA)提取高光谱影像的若干主成分,利用数学形态学提取各主分量影像对应的形态学剖面(MP),再将所有主分量影像的形态学剖面归并联结,组成扩展的形态学剖面(MP_ext)。将MP_ext与光谱信息相结合以增加知识,最大限度地挖掘未标记样本的有用信息,优化分类器的学习能力。不断从分类器对未标记样本的预测结果中甄选置信度高的样本加入训练集,并迭代地利用扩大的训练集进行分类器构建和样本预测。以不同地表覆盖类型的AVIRIS Indian Pines和Hyperion EO-1 Botswana作为测试数据,分别与基于光谱、MP_ext、光谱和MP_ext融合的分类方法进行比对。试验结果表明,在训练样本数量有限情况下,INC_SPEC_MP_ext算法在降低分类成本的同时,分类精度和Kappa系数都有不同程度的提高。

关键词: 高光谱遥感影像, 形态学, 空间信息, 光谱信息, 增量分类

Abstract: An incremental classification algorithm INC_SPEC_MP_ext was proposed for hyperspectral remote sensing images based on spectral and spatial information. The spatial information was extracted by building morphological profiles based on several principle components of hyperspectral image. The morphological profiles were combined together in extended morphological profiles (MP_ext). Combine spectral and MP_ext to enrich knowledge and utilize the useful information of unlabeled data at the most extent to optimize the classifier. Pick out high confidence data and add to training set, then retrain the classifier with augmented training set to predict the rest samples. The process was performed iteratively. The proposed algorithm was tested on AVIRIS Indian Pines and Hyperion EO-1 Botswana data, which take on different covers, and experimental results show low classification cost and significant improvements in terms of accuracies and Kappa coefficient under limited training samples compared with the classification results based on spectral, MP_ext and the combination of sepctral and MP_ext.

Key words: hyperspectral remote sensing image, morphology, spatial information, spectral information, incremental classification

中图分类号:

P237

王俊淑, 江南, 张国明, 李杨, 吕恒. 融合光谱-空间信息的高光谱遥感影像增量分类算法[J]. 测绘学报, 2015, 44(9): 1003-1013.

WANG Junshu, JIANG Nan, ZHANG Guoming, LI Yang, LV Heng. Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(9): 1003-1013.

参考文献

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[19] FAUVEL M, BENEDIKTSSON J A, CHANUSSOT J, et al. Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(11): 3804-3814.
[20] CRESPO J, SERRA J, SCHAFER R W. Theoretical Aspects of Morphological Filters by Reconstruction[J], Signal Processing, 1995, 47(2): 201-225.
[21] FAUVEL M, TARABALKA Y, BENEDIKTSSON J A, et al. Advances in Spectral-spatial Classification of Hyperspectral Images[J]. Proceedings of the IEEE, 2013, 101(3): 652-675.
[22] CHANG C C, LIN C J. LIBSVM: A Library for Support Vector Machines[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2011, 2(3): 27."
[1] KUO B C, LANDGREBE D A. A Robust Classification Procedure Based on Mixture Classifiers and Nonparametric Weighted Feature Extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2486-2494.
[2] LEE C, LANDGREBE D A. Feature Extraction Based on Decision Boundaries[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(4): 388-400.
[3] LIU Chunhong, ZHAO Chunhui, ZHANG Lingyan. A New Method of Hyperspectral Remote Sensing Image Dimensional Reduction[J]. Journal of Image and Graphics, 2005, 10(2): 218-222. (刘春红, 赵春晖, 张凌雁. 一种新的高光谱遥感图像降维方法[J]. 中国图象图形学报, 2005, 10(2): 218-222.)
[4] LUO Jiancheng, ZHOU Chenghu, LIANG Yi, et al. Support Vector Machine for Spatial Feature Extraction and Classification of Remotely Sensed Imagery[J]. Journal of Remote Sensing, 2002, 6(1): 50-55. (骆剑承, 周成虎, 梁怡, 等. 支撑向量机及其遥感影像空间特征提取和分类的应用研究[J]. 遥感学报, 2002, 6(1): 50-55.)
[5] WANG Junshu, JIANG Nan, ZHANG Guoming, et al. Semi-supervised Classification Algorithm for Hyperspectral Remote Sensing Image Based on DE-self-training[J]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(5): 239-244. (王俊淑, 江南, 张国明, 等. 高光谱遥感图像DE-self-training半监督分类算法[J]. 农业机械学报, 2015, 46(5): 239-244.)
[6] ZHOU Zhihua, ZHAN Dechuan, YANG Qiang. Semi-supervised Learning with Very Few Labeled Training Examples[C]//Proceedings of the National Conference on Artificial Intelligence. Cambridge, MA London: [s.n.], 2007, 22(1): 675-680.
[7] CHAWLA N V, KARAKOULAS G. Learning from Labeled and Unlabeled Data: An Empirical Study across Techniques and Domains[J]. Journal of Artificial Intelligence Research, 2005, 23(1): 331-366.
[8] LI Yuanqing, GUAN Cuntai, LI Huiqi, et al. A Self-training Semi-supervised SVM Algorithm and Its Application in an EEG-based Brain Computer Interface Speller System[J]. Pattern Recognition Letters, 2008, 29(9): 1285-1294.
[9] TAN Kun, DU Peijun. Wavelet Support Vector Machines Based on Reproducing Kernel Hilbert Space for Hyperspectral Remote Sensing Image Classification[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(2): 142-147. (谭琨, 杜培军. 基于再生核Hilbert空间的小波核函数支持向量机的高光谱遥感影像分类[J]. 测绘学报, 2011, 40(2): 142-147.)
[10] MELGANI F, BRUZZONE L. Classifcation of Hyperspectral Remote Sensing Images with Support Vector Machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1778-1790.
[11] MANTHIRA MOORTHI S, MISRA I, KAUR R, et al. Kernel Based Learning Approach for Satellite Image Classification Using Support Vector Machine[C]//IEEE Recent Advances in Intelligent Computational Systems (RAICS). Trivandrum: IEEE, 2011: 107-110.
[12] DELL'ACQUA F, GAMBA P, FERRARI A, et al. Exploiting Spectral and Spatial Information in Hyperspectral Urban Data with High Resolution[J]. IEEE Geoscience and Remote Sensing Letters, 2004, 1(4): 322-326.
[13] LIANG Liang, YANG Minhua, LI Yingfang. Hyperspectral Remote Sensing Image Classification Based on ICA and SVM Algorithm[J]. Spectroscopy and Spectral Analysis, 2010, 30(10): 2724-2728. (梁亮, 杨敏华, 李英芳. 基于ICA与SVM算法的高光谱遥感影像分类[J]. 光谱学与光谱分析, 2010, 30(10): 2724-2728.)
[14] WU Jian. PENG Daoli. Vegetation Classification Technology of Hyperspectral Remote Sensing Based on Spatial Information[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(5): 150-153. (吴见, 彭道黎. 基于空间信息的高光谱遥感植被分类技术[J]. 农业工程学报, 2012, 28(5): 150-153.)
[15] GAO Hengzhen, WAN Jianwei, WANG Libao, et al. Research on Classification Technique for Hyperspectral Imagery Based on Spectral-spatial Composite Kernels[J]. Signal Processing, 2011, 27(5): 648-652. (高恒振, 万建伟, 王力宝, 等. 基于谱域-空域组合核函数的高光谱图像分类技术研究[J]. 信号处理, 2011, 27(5): 648-652.)
[16] CHEN Shanjing, HU Yihua, SHI Liang, et al. Classification of Hyperspectral Imagery Based on Ant Colony Compositely Optimizing SVM in Spatial and Spectral Features[J]. Spectroscopy and Spectral Analysis, 2013, 33(8): 2192-2197. (陈善静, 胡以华, 石亮, 等. 空-谱二维蚁群组合优化SVM 的高光谱图像分类[J]. 光谱学与光谱分析, 2013, 33(8): 2192-2197.)
[17] POGGI G, SCARPA G, ZERUBIA J B. Supervised Segmentation of Remote Sensing Images Based on a Tree-structure MRF Model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1901-1911.
[18] JACKSON Q, LANDGREBE D A. Adaptive Bayesian Contextual Classification Based on Markov Random Fields[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2454-2463.
[19] FAUVEL M, BENEDIKTSSON J A, CHANUSSOT J, et al. Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(11): 3804-3814.
[20] CRESPO J, SERRA J, SCHAFER R W. Theoretical Aspects of Morphological Filters by Reconstruction[J], Signal Processing, 1995, 47(2): 201-225.
[21] FAUVEL M, TARABALKA Y, BENEDIKTSSON J A, et al. Advances in Spectral-spatial Classification of Hyperspectral Images[J]. Proceedings of the IEEE, 2013, 101(3): 652-675.
[22] CHANG C C, LIN C J. LIBSVM: A Library for Support Vector Machines[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2011, 2(3): 27.

融合光谱-空间信息的高光谱遥感影像增量分类算法

Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information

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