基于光谱特征和纹理特征协同学习的高光谱图像数据分类
李吉明, 贾森, 彭艳斌. 基于光谱特征和纹理特征协同学习的高光谱图像数据分类[J]. 光电工程, 2012, 39(11): 88.
李吉明, 贾森, 彭艳斌. Hyperspectral Data Classification with Spectral and Texture Features by Co-training Algorithm[J]. Opto-Electronic Engineering, 2012, 39(11): 88.
[2] Camps-Valls G, Gomez-Chova L, Munoz-Mari J, et al. Kernel-based framework for multitemporal and multisource remote sensing data classication and change detection [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2008, 46(6): 1822–1835.
[3] Munoz-Mari J, Bruzzone L, Camps-Valls G. A support vector domain description approach to supervised classification of remote sensing images [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2007, 45(8): 2683–2692.
[4] Tarabalka Y, Benediktsson J A, Chanussot J, et al. Multiple Spectral-Spatial Classification Approach for Hyperspectral Data [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2010, 48: 4122-4132.
[5] LI Jun, Bioucas-Dias J M, Plaza A. Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2012, 50: 809-823.
[6] Lee C H, Kuo B C, LIN C T, et al. A Spatial-Contextual Support Vector Machine for Remotely Sensed Image Classification [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2012, 50: 784-799.
[7] Blum A, Mitchell T. Combining labeled and unlabeled data with co-training [C]//Proceedings of the Eleventh Annual Conference on Computational Learning Theory, Madison, Wisconsin, USA, July 24-26, 1998: 92-100.
[8] Jain A, Farrokhnia F. Unsupervised texture segmentation using Gabor Filters [J]. Pattern Recognition(S0031-3203), 1991, 24(12): 1167–1186.
[9] CHI M M, Bruzzone L. Semisupervised classication of hyperspectral images by svms optimized in the primal [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2007, 45(6): 1870–1880.
[10] Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2004, 42(8): 1778–1790.
[11] Archibald G F R. Feature selection and classification of hyperspectral images with support vector machines [J]. IEEE Geoscience and Remote Sensing Letters(S1545-598X), 2007, 4: 674–677.
[12] WU T, LIN C, WENG R. Probability estimates for multiclass classification by pairwise coupling [J]. The Journal of Machine Learning Research(S1532-4435), 2004, 5: 1005.
[13] AVIRIS NW indiana’s indian pines 1992 data set [OL]. ftp: //ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C.
[14] Neher R, Srivastava A. A Bayesian MRF Framework for Labeling Terrain Using Hyperspectral Imaging [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2005, 43(6): 1363-1374.
[15] CHANG C C, LIN C J. LIBSVM: a library for support vector machines [OL](/2001). http: //www.csie.ntu.edu.tw/.cjlin/ libsvm.
李吉明, 贾森, 彭艳斌. 基于光谱特征和纹理特征协同学习的高光谱图像数据分类[J]. 光电工程, 2012, 39(11): 88. 李吉明, 贾森, 彭艳斌. Hyperspectral Data Classification with Spectral and Texture Features by Co-training Algorithm[J]. Opto-Electronic Engineering, 2012, 39(11): 88.