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Multi-temporal remote sensing data can provide much more information that could be used to improve the accuracy of classification of vegetation types. However, it is always required to manually select a set of training samples by using conventional supervised classification methods, which is a time-consuming and costly task. In this paper, a new classification method based on spectral knowledge database...
In order to implement the remote sensing applied in land use research, we use a series of remote sensing image processing commercial software to extract land use/land cover information. The article are based on compare and analyze the ability of identify information of land use and land cover between two software (Erdas imagine 8.5 and ENVI4.1) which using the same classification method in a imagine...
With the rapid growth of requirements on satellite remote sensing image data, effective management and efficient publication of increasingly large image data has become an urgent problem. Based on the features of massive image data and Web publication, this paper does a deep research and a thorough discussion on multi-resolution pyramid model of global satellite remote sensing images, gives a reasonable...
Support vector machine (SVM) has been widely applied in the classification of remotely sensed image. How to reduce support vector number in SVM classifier so as to reduce classification time still an important open problem, especially in the case of mass data. To obtain fast classifier with high accuracy, an active learning schema is proposed in the SVM based image classification. Experimental results...
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