异质影像融合研究现状及趋势

石强; 张斌; 陈喆; 时公涛; 陈东; 秦前清

doi:10.3724/SP.J.1004.2014.00385

异质影像融合研究现状及趋势

doi: 10.3724/SP.J.1004.2014.00385

石强^1, ,,
张斌^1,2,
陈喆¹,
时公涛³,
陈东³,
秦前清¹

1.
武汉大学测绘遥感信息工程国家重点实验室武汉 430079;
2.
中国地质大学公共管理学院武汉 430074;
3.
空军装备研究院情报所北京 100085

基金项目:

国家高技术研究发展计划（863计划）（2012AA12A305），国家自然科学基金（61101213，61040043，41101425），中央高校基本科研业务费专项基金（2012619020214）资助

详细信息

作者简介:
张斌中国地质大学（武汉）公共管理学院讲师. 分别于2007年、2009年、2013 年获得武汉大学电子信息学院学士、硕士、博士学位. 主要研究方向为图像分割、分类、目标检测以及乘性噪声的去除.E-mail: bin.zhang.whu@gmail.com

通讯作者:
石强

计量
- 文章访问数: 1872
- HTML全文浏览量: 94
- PDF下载量: 1804
- 被引次数: 0
出版历程
- 收稿日期: 2012-12-09
- 修回日期: 2013-10-08
- 刊出日期: 2014-03-20

Fusion Techniques for Heterogeneous Images：a Survey

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079;
2.
School of Public Administration, China University of Geosciences, Wuhan 430074;
3.
The Tenth Laboratory of Intelligence Institute, Academy of Air Force0s Equipment, Beijing 100085

Funds:

Supported by National High Technology Research and Development Program of China (863 Program) (2012AA12A305), National Natural Science Foundation of China (61101213, 61040043, 41101425), and Fundamental Research Funds for the Central Universities (2012619020214)

摘要

摘要: 成像机理上的差异导致了异质影像数据之间存在着本质区别，这使得其在像素级融合存在很大困难，因此异质影像融合主要集中于特征级和决策级.本文从信息融合的基本原理出发，详细论述了异质影像融合结构、特征级融合算法、决策级融合算法的研究现状.同时，深入分析了异质影像融合中存在的问题，并指出了未来的发展方向.
- 图像融合 /
- 异质影像 /
- 特征层融合 /
- 决策层融合
Abstract: The diversity in the imaging mechanism leads to the essential difference between heterogeneous images. This makes it very difficult to fuse heterogeneity images at pixel levels. So the fusion methods mainly focus on feature and decision levels. Based on the principles of multisource information fusion, this paper describes the research status of image fusion structure, feature level fusion algorithm and decision level fusion algorithm in details. Meanwhile, in-depth analysis of the problems existing in heterogeneous image fusion is presented. Finally, we suggest the future research directions for heterogeneous image fusion.
- Heterogeneous image /
- image fusion /
- feature level fusion /
- decision level fusion

HTML全文

参考文献(88)

[1]	He You, Wang Guo-Hong, Guan Xin. Information Fusion Theory With Applications. Beijing: Electronic Industry Press, 2010(何友, 王国宏, 关欣. 信息融合理论及应用. 北京: 电子工业出版社, 2010)
[2]	[2] Simonea G, Farinab A, Morabitoa F C, Serpicoc S B, Bruzzoned L. Image fusion techniques for remote sensing applications. Information Fusion, 2002, 3(1): 3-15
[3]	[3] Ashraf S, Brabyn L, Hicks B J. Image data fusion for the remote sensing of freshwater environments. Applied Geography, 2012, 32(2): 619-628
[4]	[4] Du P J, Chen Y, Xia J S, Tan K. A novel remote sensing image classification scheme based on data fusion, multiple features and ensemble learning. Journal of the Indian Society of Remote Sensing, 2013, 42(2): 213-222
[5]	[5] Huang B, Zhang H K, Song H H, Wang J, Song C Q. Unified fusion of remote-sensing imagery: generating simultaneously high-resolution synthetic spatial-temporal-spectral earth observations. Remote Sensing Letters, 2013, 4(6): 561-569
[6]	[6] Du P J, Liu S C, Xia J S, Zhao Y D. Information fusion techniques for change detection from multi-temporal remote sensing images. Information Fusion, 2013, 14(1): 19-27
[7]	Luo Bin, Wang Yong-Tian, Liu Yue. Multi-sensor data fusion for optical tracking of head pose. Acta Automatica Sinica, 2013, 36(9): 1239-1249 (罗斌, 王涌天, 刘越. 光学头部姿态跟踪的多传感器数据融合研究. 自动化学报, 2013, 36(9): 1239-1249)
[8]	Han Chong-Zhao, Zhu Hong-Yan, Duan Zhan-Sheng. Multi-Source Information Fusion (2nd Edition). Beijing: Tsinghua University Press, 2010(韩崇昭, 朱红艳, 段战胜. 多源信息融合. 第2版. 北京: 清华大学出版社, 2010)
[9]	[9] Hall D L, Llinas J. Handbook of Multisensor Data Fusion. New York: CRC Press, 2001
[10]	Llinas J, Bowman C, Rogova G, Steinberg A, Waltz E, White F E. Revisiting the JDL data fusion model II(C). In: Proceedings of the 2004 International Conference on Information Fusion. Stockholm, Sweden, 2004. 1218-1230
[11]	Mitchell H B. Data Fusion: Concepts and Ideas. Berlin and Heidelberg: Springer-Verlag, 2012
[12]	Dasarathy B V. Decision Fusion. Los Alamitos, CA: IEEE Computer Society Press, 1994
[13]	Solano M A, Ekwaro-Osire S, Tanik M M. High-level fusion for intelligence applications using recombinant cognition synthesis. Information Fusion, 2012, 13(1): 79-98
[14]	Quaranta C, Balzarotti G. Technique for radar and infrared search and track data fusion. Optical Engineering, 2013, 52(4): 046401
[15]	Amarsaikhan D, Saandar M, Ganzorig M, Blotevogel H H, Egshiglen E, Gantuyal R, Nergui B, Enkhjargal D. Comparison of multisource image fusion methods and land cover classification. International Journal of Remote Sensing, 2011, 38(8): 2532-2552
[16]	Lei Lin. Ship Feature Extraction and Fusion in Multiple Remote Sensing Images [Ph.D. dissertation], National University of Defense Technology, China, 2008 (雷琳. 多源遥感图像舰船目标特征提取与融合技术研究 [博士学位论文], 国防科学技术大学, 中国, 2008)
[17]	Zhao Shu-He. Decision Level Fusion of Multiple Remote Sensing Images and Its Application [Ph.D. dissertation], Nanjing University, China, 2003 (赵书河. 多源遥感影像决策级融合及其应用研究 [博士学位论文], 南京大学, 中国, 2003)
[18]	McCullough C L, Dasarathy B V, Lindberg P C. Multi-level sensor fusion for improved target discrimination. In: Proceedings of the 35th Conference on Decision and Control. Kobe, Japan: IEEE, 1996. 3674-3675
[19]	Hussain M S, Calvo R A, Pour P A. Hybrid fusion approach for detecting affects from multichannel physiology. Affective Computing and Intelligent Interaction Lecture Notes in Computer Science, 2011, 6974: 568-577
[20]	Deng Xiao-Ling, Ni Jiang-Qun, Li Zhen, Dai Fen. Foreground extraction from low depth-of-field images based on colour-texture and HOS features. Acta Automatica Sinica, 2013, 39(6): 846-851 (邓小玲, 倪江群, 李震, 代芬. 多特征融合的低景深图像前景提取算法. 自动化学报, 2013, 39(6): 846-851)
[21]	Hou Shu-Dong, Sun Quan-Sen. Sparsity preserving canonical correlation analysis with application in feature fusion. Acta Automatica Sinica, 2012, 38(4): 659-665 (候书东, 孙权森. 稀疏保持典型相关分析及在特征融合中的应用. 自动化学报, 2012, 38(4): 659-665)
[22]	Wang Da-Wei. Research on Target Recognition Based on Feature-Level Image Fusion [Ph.D. dissertation], Chinese Academy of Sciences, China, 2010 (王大伟. 基于特征级图像融合的目标识别技术研究 [博士学位论文], 中国科学院研究生院, 中国, 2010)
[23]	Yang Jian, Yang Jing-Yu, Gao Jian-Zhen. Handwritten character recognition based on parallel feature combination and generalized K-L expansion. Journal of Software, 2003, 14(3): 490-495(杨建, 杨静宇, 高建贞. 基于并行特征组合与广义K-L变换的字符识别. 软件学报, 2003, 14(3): 490-495)
[24]	Lang Fang-Nian, Zhou Ji-Liu, Zhong Fan, Yan Bin. Quaternion based image information parallel fusion. Acta Automatica Sinica, 2013, 33(11): 1136-1143 (朗方年, 周激流, 钟钒, 闫斌. 基于四元数的图像信息并行融合. 自动化学报, 2007, 33(11): 1136-1143)
[25]	Bebis G, Gyaourova A, Singh S, Pavlidis I. Face recognition by fusing thermal infrared and visible iamgery. Image and Vision Computing, 2006 24(7): 727-742
[26]	Qin Zheng, Bao Fu-Min, Li Ai-Guo. Digital Image Fusion. Xi'an: Xi'an Jiaotong University Press, 2004(覃征, 鲍褔民, 李爱国. 数字图像融合. 西安: 西安交通大学出版社, 2004)
[27]	Sengupta N, Sil J. Evaluation of rough set theory based network traffic data classifier using different discretization method. International Journal of Information and Electronics Engineering, 2012, 2(3): 338-341
[28]	Shang C J, Barnes D. Fuzzy-rough feature selection aided support vector machines for Mars image classification. Computer Vision and Image Understanding, 2013, 117(3): 202-213
[29]	Jensen R, Shen Q. New approaches to fuzzy-rough feature selection. IEEE Transactions on Fuzzy Systems, 2009, 17(4): 824-838
[30]	Alex M, Vasilescu O, Terzopoulos D. A tensor approach to image sysnthesis, analysis and recognition. In: Proceedings of the 6th International Conference on 3-D Digital Imaging and Modeling. Montreal, QC: IEEE, 2007. 3-12
[31]	Hu Liang-Mei. Information Fusion-based for Image Understanding [Ph.D. dissertation], Hefei University of Technology, China, 2006(胡良梅. 基于信息融合的图像理解方法研究 [博士学位论文], 合肥工业大学, 中国, 2006)
[32]	Li Xin-De. Research on Fusion Method of Imperfect Information from Multi-source and Its Application [Ph.D. dissertation], Huazhong University of Science and Technology, China, 2007 (李新德. 多源不完善信息融合方法及其应用研究 [博士学位论文], 华中科技大学, 中国, 2007)
[33]	Klein L A. Sensor and Data Fusion Concepts and Applications. Bellingham, WA: SPIE Optical Engineering Press, 1999
[34]	Li Q Q, Tao J B, Hu Q W, Liu P C. Decision fusion of very high resolution images for urban land-cover mapping based on Bayesian network. Journal of Applied Remote Sensing, 2013, 7(1): 073551
[35]	Wu H D, Siegel M, Stiefelhagen R, Yang L. Sensor fusion using dempster-shafer theory. In: Proceedings of the 19th IEEE Conference on Instrumentation and Measurement Technology. Anchorage, AK: IEEE, 2002. 7-12
[36]	Sun S Y, Gao J, Chen M F, Xu B G, Ding Z G. FS-DS based multi-sensor data fusion. Journal of Software, 2013, 8(5): 1157-1161
[37]	Fontani M, Bianchi T, De Rosa A, Piva A, Barni M. A framework for decision fusion in image forensics based on Dempster-Shafer theory of evidence. IEEE Transactions on Information Forensics and Security, 2013, 8(4): 593-607
[38]	Dezert J. Foundations for a new theory of plausible and paradoxical reasoning. Information and Security, 2002, 9: 90-95
[39]	Elhassouny A, Idbraim S, Bekkarri A, Mammass D, Ducrot D. Multisource fusion/classification using ICM and DSmT with new decision rule. In: Proceedings of the 5th International Conference on Image and Signal Processing (ICISP, 2012). Berlin Heidelberg: Springer, 2012. 56-64
[40]	Ding Sheng-Feng. Research on Multi-source Image Fusion Based on Fuzzy Reference [Master dissertation], Nanjing University of Science and Technology, China, 2004 (丁胜峰. 基于模糊推理的多源图像融合研究 [硕士学位论文], 南京理工大学, 中国, 2004)
[41]	Bosma R, van der Bergb J, Kaymakc U, Udod H, Verreth J. A generic methodology for developing fuzzy decision models. Expert Systems with Applications, 2012, 39(1): 1200-1210
[42]	Yu D J. Multi-criteria decision making based on generalized prioritized aggregation operators under intuitionistic fuzzy environment. International Journal of Fuzzy Systems, 2013, 15(1): 47-54
[43]	Dong G J, Zhou H F. Rough set method for remote sense image classification and information fusion. In: Proceedings of the 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). Taiyuan, China: IEEE, 2010. 157-161
[44]	Wang Peng-Wei, Li Tao, Wu Xiu-Qing. An segmentation approach based on MRF and SVM posteriori probability. Journal of Remote Sensing, 2008, 12(2): 208-214 (王鹏伟, 李滔, 吴秀清. 一种基于SVM后验概率的MRF分割方法. 遥感学报, 2008, 12(2): 208-214)
[45]	Li Tao, Wang Jun-Pu, Wu Xiu-Qing, Tang Jin-Hui. Estimation of posterior probability and applications: an approach based on kernel logistic regression. Pattern Recognition and Artificial Intelligence, 2006, 19(6): 690-695 (李滔, 王俊普, 吴秀清, 唐金辉. 后验概率估计及其应用: 基于核Logistic 回归的方法. 模式识别与人工智能, 2006, 19(6): 690-695)
[46]	Jia Yong-Hong. Research on the Method of Multi-source Image Fusion and Its Application [Ph.D. dissertation], Wuhan University, China, 2001 (贾永红. 多源遥感影像数据融合方法及其应用的研究 [博士学位论文], 武汉大学, 中国, 2001)
[47]	Boudraa A O, Bentabet A, Salzenstein F, Guillon L. Dempster-Shafer's basic probability assignment based on fuzzy membership functions. Electronic Letters on Computer Vision and Image Analysis, 2004, 4(1): 1-9
[48]	Jiang W, Deng Y, Peng J Y. A new method to determine BPA in evidence theory. Journal of Computers, 2011, 6(6): 1162-1167
[49]	Zuo Z Y, Xu Y F, Chen G C. A new method of obtaining BPA and application to the bearing fault diagnoises of wind turbine. In: Proceedings of the 2009 International Symposium on Information Processing (ISIP'09). Huangshan, PR, China: IEEE, 2009. 368-371
[50]	Dai Guan-Zhong, Pan Quan, Zhang Shan-Ying, Zhang Hong-Cai. The developments and problems in evidence reasoning. Control Theory and Applications, 1999, 16(4): 465-469 (戴冠中, 潘泉, 张山鹰, 张洪才. 证据推理的进展及存在问题. 控制理论与应用, 1999, 16(4): 465-469)
[51]	Peng H P, Cao X J. Research conflict problems of D-S evidence and its application in multi-sensor information fusion technology. In: Proceedings of the 2010 IEEE International Conference on Information Theory and Information Security (ICITIS). Beijing, China: IEEE, 2010. 747-750
[52]	Zhou Zhe, Xu Xiao-Bin, Wen Cheng-Lin, Lv Feng. An optimal method for combining conflicting evidences. Acta Automatica Sinica, 2012, 38(6): 976-985 (周哲, 徐晓滨, 文成林, 吕峰. 冲突证据融合的优化方法. 自动化学报, 2012, 38(6): 976-985)
[53]	Zhu Jian-Ying. Some common key problems and their dealing methods in the application of fuzzy mathematical methods. Fuzzy Systems and Mathematics, 1992, 11(2): 57-63 (朱剑英. 应用模糊数学方法的若干关键问题及处理方法. 模糊系统与数学, 1992, 11(2): 57-63)
[54]	Yang C C, Bose N K. Generating fuzzy membership function with self-organizing feature map. Pattern Recognition Letters, 2006, 27(5): 356-365
[55]	Ang K K, Quek C. Supervised pseudo self-evolving cerebellar algorithm for generating fuzzy membership functions. Expert Systems with Applications, 2012, 39(3): 2279-2287
[56]	Ma J W, Hasi B. Remote sensing data classification using tolerant rough set and neural networks. Science in China Ser. D Earth Sciences, 2005, 48(12): 2251-2259
[57]	Deng Ting-Quan, Yang Cheng-Dong, Zhang Yue-Tong. Fuzzy similarity relation based variable precision fuzzy rough sets. CAAI Transactions on Intelligent Systems, 2012, 7(2): 148-152(邓廷权, 杨成东, 张月童. 模糊相似关系下变精度模糊粗糙集. 智能系统学报, 2012, 7(2): 148-152)
[58]	Chair Z, Varshney P K. Optimal data fusion in multiple sensor detection system. IEEE Transactions on AES, 1986, 22(1): 98-101
[59]	Thomopoulos S C, Papadakis I N, Sahinoglou H, Okello N N. Centralized and distributed hypothesis testing with structured adaptive networks and perceptron-type neural networks. SPIE, 1992, 1611(1): 35-51
[60]	Pawlak R J. A new neural network architecture for the fusion of independent sensor decision. SPIE, 1994, 2232: 521-525
[61]	Ni Guo-Qiang, Li Yong-Liang, Niu Li-Hong. New developments in data fusion technology based on neural network Journal of Beijing Institute of Technology, 2003, 23(4): 503-508 (倪国强, 李勇量, 牛丽红. 基于神经网络的数据融合技术的新进展. 北京理工大学报, 2003, 23(4): 503-508)
[62]	Yu X H, Xu Z S. Prioritized intuitionistic fuzzy aggregation operators. Information Fusion, 2013, 14(1): 108-116
[63]	Virrantaus K. Analysis of the uncertainty and imprecision of the source data sets for a military terrain analysis application. In: Proceedings of In: Proceedings of the 2nd International Symposium on Spatial Data Quality'03, Hong Kong, China, 2003. 139-145
[64]	Lee H, Lee B, Park K, Elmasri R. Fusion techniques for reliable information: a survey. International Journal of Digital Content Technology and Its Applications, 2010, 4(2): 74-88
[65]	Stein A, Hamm N A S, Ye Q G. Handing uncertainties in image mining for remote sensing studies. International Journal of Remote Sensing, 2009, 30(20): 5365-5382
[66]	Bombrun L, Vasile G, Gong M, Totir F. Hierarchical segmentation of polarimetric SAR images using heterogeneous cluster models. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(2): 726-737
[67]	Qi Yu-Juan, Wang Yan-Jiang, Li Yong-Ping. Memory-based Gaussian mixture background modeling. Acta Automatica Sinica, 2010, 36(11): 1520-1526 (齐玉娟, 王延江, 李永平. 基于记忆的混合高斯背景建模. 自动化学报, 2010, 36(11): 1520-1526)
[68]	Xiong Biao, Jiang Wan-Shou, Li Le-Lin. Gauss mixture model based semi-supervised classification for remote sensing image. Geomatics and Information Science of Wuhan University, 2011, 36(1): 108-112 (熊彪, 江万寿, 李乐林. 基于高斯混合模型的遥感影像半监督分类. 武汉大学学报: 信息科学版, 2011, 36(1): 108-112)
[69]	Xiao Jian-Yu, Tong Min-Ming, Zhu Chang-Jie, Wang Xiao-Lei. Basic probability assignment construction method based on generalized triangular fuzzy number. Chinese Journal of Scientific Instrument, 2012, 33(2): 429-434 (肖建于, 童敏明, 朱昌杰, 王小蕾. 基于广义三角模糊数的基本概率赋值构造方法. 仪器仪表学报, 2012, 33(2): 429-434)
[70]	Liang Fa-Mai, Zhang Jing, Wang Guo-Hong. Study on the method of constructing basic probability assignment function in targets identification. Fire Control and Command Control, 2008, 33(8): 8-11 (梁发麦, 张静, 王国宏. 雷达目标识别中获取基本概率赋值的方法. 火力与指挥控制, 2008, 33(8): 8-11)
[71]	Yang Jing-Hua, Yu Hua. Multi-Source Information Fusion Theory and Applications. Beijing: Beijing University of Posts and Telecommunications press, 2011(杨露菁, 余华. 多源信息融合理论与应用 (第2版). 北京: 北京邮电大学出版社, 2011)
[72]	Wanas N. Feature based Architecture for Decision Fusion [Ph.D. dissertation], University of Waterloo, Canada, 2003
[73]	Van Laere J. Challenges for IF performance evaluation in practice. In: Proceedings of the 12th International Conference on Information Fusion (FUSION'09). Seattle, WA: IEEE, 2009. 866-873
[74]	Shi Qiang, Chen Feng-E, Mei Tian-Can, Qin Qian-Qing. Remote sensing image segmentation based on SVM posterior probability and improved multi-scale MRF. Geomatics and Information Science of Wuhan University, 2013, 38(2): 193-199 (石强, 陈凤娥, 梅天灿, 秦前清. SVM 后验概率结合改进多尺度MRF的遥感影像分割方法. 武汉大学报信息科学版, 2013, 38(2): 193-199)
[75]	Zhu Jie-Hao, Zhou Jian-Jiang, Wu Jie. Radar target recognition based on semiparametric density estimation. Journal of Electronics and Information Technology, 2010, 32(9): 2161-2166 (朱劼昊, 周建江, 吴杰. 基于半参数化概率密度估计的雷达目标识别. 电子与信息学报, 2010, 32(9): 2161-2166)
[76]	Li Yan-Xin, Li Guang-Yu, Li Wen. Learning algorithm of membership functions based on RBF neural network. Journal of Dalian Jiaotong University, 2007, 28(2): 34-37 (李延新, 李光宇, 李文. 基于RBF 神经网络的隶属度函数学习算法. 大连交通大学学报, 2007, 28(2): 34-37)
[77]	Xu P D, Deng Y, Su X Y, Mahadevan S. A new method to determine basic probability assignment from training data. Knowledge-Based Systems, 2013, 46: 69-80
[78]	Zhu H, Basir O. A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing. Soft Computing Journal --- A Fusion of Foundations, Methodologies and Applications, 2006, 10(12): 1169-1180
[79]	Farah I R, Boulila W, Ettabaa K S, Solaiman B, Ahmed M B. Interpretation of multisensor remote sensing images: multiapproach fusion of uncertain information. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(12): 4142-4152
[80]	Xu X B, Wen C L. Random sets: a unified framework for multisource information fusion. Journal of Electronics (China), 2009, 26(6): 723-730
[81]	Smarandache F. A Unifying Field in Logics: Neutrosophic Logic, Neutrosophy, Neutrosophic Set, Probability, and Statistics. Rehoboth: American Research Press, 2000
[82]	Han Chong-Zhao, Han De-Qiang, Jie Jing. From biological cognition and perception to methodologies of system engineering. Systems Engineering --- Theory and Practice, 2008, 6(Supplement): 75-93 (韩崇昭, 韩德强, 介婧. 从生物感知认知到系统工程方法论. 系统工程理论与实践, 2008, 6(增刊): 75-93)
[83]	Ni Guo-Qiang, Dai Wen, Li Yong-Liang, Pu Tian. Visual/IR color image fusion based on rattlesnake bimodal cell neurodynamics: advances and prospects. Journal of Beijing Institute of Technology, 2004, 24(2): 95-100 (倪国强, 戴文, 李勇量, 蒲恬. 基于响尾蛇双模式细胞机理的可见光/红外图像彩色融合技术的优势和前景展望. 北京理工大学学报, 2004, 24(2): 95-100)
[84]	Li X R. Optimal Bayes joint decision and estimation. In: Proceedings of the 10th International Conference on Information Fusion. Quebec, Que: IEEE, 2007. 874-881
[85]	Thomas C, Balikrishnan N. Modified evidence theory for performance enhancement of intrusion detection system. In: Proceedings of the 2008 IEEE International Conference on Information Fusion. Cologne: IEEE, 2008. 1-8
[86]	Wang Gang, Huang Li-Hua, Zhang Cheng-Hong. Review of hybrid intelligent systems. Journal of Systems Engineering, 2010, 25(4): 569-578 (王刚, 黄丽华, 张成洪. 混合智能系统研究综述. 系统工程学报, 2010, 25(4): 569-578)
[87]	Wozniak M, Grana M, Corchado E. A survey of multiple classifier systems as hybrid systems. Information Fusion, to be published
[88]	Castllo O, Melin P, Janusz K. Recent Advances on Hybrid Intelligent Systems. Berlin, Heidelberg: Springer-Verlag, 2013