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TopTraditional image processing approach work quite well on low resolution remote sensing images(LRRS) but obtain low hit rate high resolution remote sensing images (HRRS). Depend on features template (Zhanga & Zhou, 2004), the researcher achieve image and template matching, and high hit rate of object recognition.
Object recognition of HRRS can depend on intra-domain knowledge provided by experienced experts (Durand, Derivaux, Forestier, Wemmert, Gançarski, Boussaid & Puissant, 2007). The results of experiments show relatively good results and high hit rate when distinguish objects in urban and rural areas.
Different kernel-based approaches of hyperspectral image classification are compared with each other, which include kernel fisher discriminant(KFD), regularized radial basis function neural networks(Reg-RBFNN), regularized AdaBoost (Reg-AB), standard support vector machines (SVMs). The results show that SVMs achieve better results than other several kernel-based approaches and at a much less time in most cases. (Gustavo Camps-Valls, Lorenzo Bruzzone, 2005).