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

doi:10.11947/j.AGCS.2015.20140388

Abstract

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

CLC Number:

P237

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.

References

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[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.