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Object registration for remote sensing images using robust kernel pattern vectors

science china(Information sciences) 2012年第11期55卷 2611-2623页

作者： DING MingTao1,JIN Zi2,TIAN Zheng1,3.DUAN XiFa1,ZHAO Wei1 & YANG LiJuan1 1Department of Applied Mathematics,Northwest Polytechnical University,Xi'an 710072,china 2Department of Statistics,University of British Columbia,3.3.-63.6 Agricultural Road,Vancouver,BC V677 1Z2,Canada 3.tate key {3. of Remote sensing science,institute of Remote sensing applications,chinese academy of sciences,bejing 100101,china 1. Department of Applied Mathematics Northwest Polytechnical University Xi’an 710072 China2. Department of Statistics University of British Columbia 3333-6356 Agricultural Road Vancouver BC V677 1Z2 Canada3. State Key Laboratory of Remote Sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences Bejing 100101 China

The registration of multitemperal remote sensing images before and after a disaster is a challenging *** main reason is that there is a dearth of homologous features that can be used as tie points for registration of variform *** paper proposes a new registration method based on robust kernel pattern vector(KPV) for water objects in the remote sensing images,which have complex deformation before and after a *** show how the variform objects can be precisely registered using their robust kernel pattern vectors(RKPVs).The contribution can be divided into three ***,a novel shape descriptor,named as kernel pattern vector(KPV),is ***,a robust kernel principal component analysis(RKPCA) method is proposed,which can not only capture the common pattern of the variform objects but can also act as the Criterion for outlier ***,a registration approach is derived based on the implicit *** demonstrate the power of the proposed approach by comparing it with other existing methods using two real cases:one for lake monitoring in the Jiayu region,and the other for damage mapping of earthquake-induced barrier lake at Tangjiashan(2008 Wenchuan Earthquake).The results show that the method is effective in capturing the common structural pattern of the variform objects before and after a disaster.

关键词： remote sensing image object registration robust kernel pattern vectors(RKPVs) variform object outlier data effective

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