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An improved multi-classification algorithm of weighted support vector data description (WSVDD) was applied for the recognition of infected pine by utilizing the visible and near-infrared images acquired by the double spectrum camera fixed on the unmanned aerial vehicle (UAV) platform. Each color component for visible and near-infrared images acquired by the double spectrum camera was extracted as the color feature of the corresponding pixel on the basis of the difference of content information. Then the texture feature of the central pixel was acquired by extracting the gray level co-occurrence matrix of the adding window image block. The weight coefficient was used for the WSVDD of each kind of sample in order to realize the multi-classification and recognition of pine state. Here the weight coefficient was determined by building the weight function on the center distance of the training sample. Compared with the other methods such as manual work, aerial and satellite remote sensing, this method for acquiring the remote sensing image by using the UAV platform and the double spectrum camera was more operable, more low-cost etc. The experiment results showed that the WSVDD multi-classification algorithm could recognize the infected pine more accurately than the traditional methods of support vector machine(SVM) and support vector data description(SVDD).