光电工程, 2012, 39 (11): 88, 网络出版: 2012-11-22   

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

Hyperspectral Data Classification with Spectral and Texture Features by Co-training Algorithm
作者单位
1 浙江警察学院刑事科学技术系, 杭州 310053
2 深圳大学计算机与软件学院, 广东 深圳 518060
3 浙江科技学院 信息与电子工程学院,杭州 310023
引用该论文

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.

参考文献

[1] 彭艳斌, 艾解清. 基于谱聚类波段选择的高光谱图像分类 [J].光电工程, 2012, 39(2): 63-67. PENG Yan-bin, AI Jie-qing. Hyperspectral Imagery Classification Based on Spectral Clustering Band Selection [J]. Opto-Electronic Engineering, 2012, 39(2): 63-67.

[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.

本文已被 3 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!