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

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成果类型:
期刊论文
作者:
Li, Qingquan*;Tao, Jianbin;Hu, Qingwu;Liu, Pengcheng
通讯作者:
Li, Qingquan
作者机构:
[Li, Qingquan] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China.
[Tao, Jianbin; Liu, Pengcheng] Cent China Normal Univ, Sch Urban & Environm Sci, Wuhan 430079, Peoples R China.
[Hu, Qingwu] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.
通讯机构:
[Li, Qingquan] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China.
语种:
英文
关键词:
multisource features;decision fusion;Bayesian network;urban land-cover mapping
期刊:
Journal of Applied Remote Sensing
ISSN:
1931-3195
年:
2013
卷:
7
期:
1
页码:
073551-
文献类别:
WOS:Article;EI:Journal article (JA)
所属学科:
ESI学科类别:地球科学;WOS学科类别:Environmental Sciences;Imaging Science & Photographic Technology;Remote Sensing
入藏号:
WOS:000319797400003;EI:20143318052257
基金类别:
Hubei Provincial Natural Science Foundation of China [2011CDB275]; National Natural Science Foundation of China [41071285, 41271452]
机构署名:
本校为其他机构
院系归属:
城市与环境科学学院
摘要:
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 multipleclassifier 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. ©2013 Society of Photo-Optical Instrumentation Engineers (SPIE).
参考文献:
Benediktsson JA, 2007, LECT NOTES COMPUT SC, V4472, P501
Bruzzone L, 2006, IEEE T GEOSCI REMOTE, V44, P2587, DOI 10.1109/TGRS.2006.875360
Calders T, 2010, DATA MIN KNOWL DISC, V21, P277, DOI 10.1007/s10618-010-0190-x
Camps-Valls G, 2006, IEEE GEOSCI REMOTE S, V3, P93, DOI 10.1109/LGRS.2005.857031
Camps-Valls G, 2008, IEEE T GEOSCI REMOTE, V46, P1822, DOI 10.1109/TGRS.2008.916201

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