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To investigate remote sensing classification approach based on support vector machines (SVMs), we classify a remotely sensed image of urban area of Kunming, China, by SVM-based classifiers with radial basis function (RBF) as kernel function. The best values of parameter gamma (gamma) of RBF and penalty parameter C are chosen carefully through training phase. Then, data are classified by the SVM-based...
This paper reports our preliminary study that aims to examine the effectiveness of training methods for land cover classification by artificial neural networks. We consider three training methods, namely, the gradient descent method, the conjugate gradient method, and the Quasi-Newton method. We apply these methods to derive land cover information from a Landsat Enhanced Thematic Mapper Plus (ETM+)...
In this paper, we report the result of our preliminary research that aims to examine how spatio-temporal landscape patterns can be related to socio-economic and accessibility attributes with a coastal urban area as the case. Our research methodology emphasizes the use of remotely sensed data, in combination with census data and other geographically referenced data. We classify two satellite images...
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