基于分类器集成的高光谱遥感图像分类方法
[1] 宋琳, 程咏梅, 赵永强. 基于稀疏表示模型和自回归模型的高光谱分类[J]. 光学学报, 2012, 32(3): 0330003.
[2] 吴超, 吴一全. 基于混沌粒子群优化投影寻踪的高光谱图像目标检测[J]. 光学学报, 2011, 31(12): 1211003.
[3] Paul M Mather, Magaly Koch. Computer Processing of Remotely-Sensed Images: An Introduction [M]. NewYork: John Wiley & Sons, 2011. 229-285.
[4] B M Shahshahani, D A Landgrebe. The effect of unlabeled samples in reducing the small sample size problem and mitigating the hughes phenomenon [J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1087-1095.
[5] 刘小刚, 赵慧洁, 李娜. 基于多重分形谱的高光谱数据特征提取[J]. 光学学报, 2009, 29(3): 844-847.
[6] Q Jackson, D A Landgrebe. An adaptive classifier design for high-dimensional data analysis with a limited training dataset [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(12): 2664-2679.
[7] L Zhang, B Du. Recent advances in hyperspectral image processing [J]. Geo-spatial Information Science, 2012, 15(3): 143-156.
[8] T G Dietterich. Machine learning research: four current directions [J]. Ai Magazine, 1997, 18(4): 97-136.
[9] 周杨, 厉小润, 赵辽英. 改进的高光谱图像线性预测波段选择算法[J]. 光学学报, 2013, 33(8): 0828002.
[10] 葛亮, 王斌, 张立明. 基于波段聚类的高光谱图像波段选择[J]. 计算机辅助设计与图形学学报, 2012, 24(11): 1447-1454.
Ge Liang, Wang Bin, Zhang Liming. Band selection based on band clustering for hyperspectral imagery [J]. Journal of Computer-Aided Design & Computer Graphics, 2012, 24(11): 1447-1454.
[11] B Guo, S R Gunn, R I Damper, et al.. Band selection for hyperspectral image classification using mutual information [J]. Geoscience and Remote Sensing Letters, 2006, 3(4): 522-526.
[12] H Z M Shafri, A Suhaili, S Mansor. The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis [J]. Journal of Computer Science, 2007, 3(6): 419-423.
[13] A Tsymbal, S Puuronena, D W Pattersonb. Ensemble feature selection with the simple bayesian classification [J]. Information Fusion, 2003, 4(2): 87-100.
[14] 谢元澄. 分类器集成研究[D]. 南京: 南京理工大学, 2009.
Xie Yuancheng. Research on Classifier ensemble [D]. Nanjing: Nanjing University of Science and Technology, 2009.
[15] Z H Zhou, J X Wu, W Tang. Ensembling neural networks: many could be better than all [J]. Artificial Intelligence, 2002, 137(1-2): 239-263.
[16] G Brown, L I Kuncheva. “Good” and “Bad” Diversity in Majority Vote Ensembles[M]. Multiple Classifier Systems Berlin Heidelberg: Springer, 2010. 124-133.
[17] L I Kuncheva, C J Whitaker, C A Shipp, et al.. Is independence good for combining classifiers [C]. IEEE 2000. Proceedings. 15th International Conference on Pattern recognition, 2000, 2: 168-171.
[18] L I Kuncheva, C J Whitaker, C A Shipp, et al.. Limits on the majority vote accuracy in classifier fusion [J]. Pattern Analysis and Applications, 2003, 6(1): 22-31.
[19] Y Liu, X Yao. Ensemble learning via negative correlation [J]. Neural Networks, 1999,12(10): 1399-1404.
[20] 唐耀华, 高静怀, 包乾宗. 一种新的选择性支持向量机集成学习算法[J]. 西安交通大学学报, 2008, 42(10): 1221-1225.
Tang Yaohua, Gao Jinghuai, Bao Qianzong. Novel selective support vector machine ensemble learning algorithm [J]. Journel of Xi′an Jiaotong University, 2008, 42(10): 1221-1225.
[21] B Wu, C Chen, T M Kechadi, et al.. A comparative evaluation of filter-based feature selection methods for hyper-spectral band selection [J]. International Journal of Remote Sensing, 2013, 34(22): 7974-7990.
[22] 刘颖. 基于半监督集成支持向量机的土地覆盖遥感分类方法研究[D]. 合肥: 中国科技大学, 2013. 44-45.
Liu Ying. The Study of Semisupervised Ensembled Support Vector Machines for Land Cover Classification [D]. Hefei: University of Chinese Academy of Sciences, 2013. 44-45.
[23] R A Montserud, R Leamans. Comparing global vegetation maps with the kappa statistic [J]. Ecological Modeling,1992, 62(4): 275-293.
[24] A Galal, H Hasan, I F Iman. Learnable hyperspectral measures [J]. Egyptian Informatics Journal, 2012, 13(2): 85-94.
[25] S V Forero, J Angulo. Classification of hyperspectral images by tensor modeling and additive morphological decomposition [J]. Pattern Recognition, 2013, 46(2): 566-577.
[26] N Alajian, Y Bazi, F Melgani, et al.. Fusion of supervised and unsupervised learning for improved classification of hyperspectral images [J]. Information Sciences, 2012, 217(6): 39-55.
[27] L David. Hyperspectral image data analysisi [J]. Signal Processing Magazine, 2002, 19(1): 17-28.
[28] G Valls, D Tuia, L Bruzzone, et al.. Advances in hyperspectral image classification: earth monitoring with statistical learning methods [J]. Signal Processing Magazine, 2014, 31(1): 45-54.
樊利恒, 吕俊伟, 邓江生. 基于分类器集成的高光谱遥感图像分类方法[J]. 光学学报, 2014, 34(9): 0910002. Fan Liheng, Lü Junwei, Deng Jiangsheng. Classification of Hyperspectral Remote Sensing Images Based on Bands Grouping and Classification Ensembles[J]. Acta Optica Sinica, 2014, 34(9): 0910002.