Research Papers

Decision fusion of very high resolution images for urban land-cover mapping based on Bayesian network

[+] Author Affiliations
Qingquan Li

Shenzhen University, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen 518060, China

Jianbin Tao

Central China Normal University, School of Urban and Environmental Sciences, Wuhan 430079, China

Qingwu Hu

Wuhan University, School of Remote Sensing and Information Engineering, Wuhan 430079, China

Pengcheng Liu

Central China Normal University, School of Urban and Environmental Sciences, Wuhan 430079, China

J. Appl. Remote Sens. 7(1), 073551 (Jun 03, 2013). doi:10.1117/1.JRS.7.073551
History: Received December 11, 2012; Revised March 31, 2013; Accepted April 15, 2013
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Abstract.  Traditional image processing techniques have been proven to be inadequate for urban land-cover mapping using very high resolution (VHR) remotely sensed imagery. Abundant features such as texture, shape, and structural information can be extracted from high-resolution images, which make it possible to distinguish land covers more effectively. However, the multisource characteristics of VHR images place significant demands on the classification method in terms of both efficiency and effectiveness. The most often used method is vector stacking fusion, in which a single classifier is trained over the whole feature space; statistical differences and separability complementarities among different features are rarely considered. Hence, appropriate feature fusion and classification of multisource features become the key issues in the field of urban land-cover mapping. A novel decision fusion method based on a Bayesian network is proposed to handle the multisource features of VHR images which provide redundant or complementary results. Subclassifiers are constructed separately based on multiple feature sets and then embedded into the naive Bayesian network classifier (NBC). The final results are obtained by fusing all the subclassifiers into the NBC framework. Experiments on aerial and QuickBird images demonstrated that the performance of the proposed method is greatly improved compared with vector stacking methods, and significantly improved compared with the multiple-classifier systems and multiple kernels learning support vector machine. Moreover, the proposed method has advantages in feature fusion of VHR images in urban land-cover mapping.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Topics

Networks ; Land cover

Citation

Qingquan Li ; Jianbin Tao ; Qingwu Hu and Pengcheng Liu
"Decision fusion of very high resolution images for urban land-cover mapping based on Bayesian network", J. Appl. Remote Sens. 7(1), 073551 (Jun 03, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.073551


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