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Journal of Applied Remote Sensing

Decision fusion of very high resolution images for urban land-cover mapping based on Bayesian network
Author(s): Qingquan Li; Jianbin Tao; Qingwu Hu; Pengcheng Liu
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Paper 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.

Paper Details

Date Published: 3 June 2013
PDF: 17 pages
J. Appl. Rem. Sens. 7(1) 073551 doi: 10.1117/1.JRS.7.073551
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Qingquan Li, Shenzhen Univ. (China)
Jianbin Tao, Central China Normal Univ. (China)
Qingwu Hu, Central China Normal Univ. (China)
Pengcheng Liu, Central China Normal Univ. (China)

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