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中文摘要：
针对支持向量数据描述多分类中模糊数据域的误判问题,提出了一种改进的加权小波支持向量数据描述（WWSVDD）多分类方法,并应用于遥感图像病害松树识别.利用无人机搭载双光谱相机获取高分辨率遥感图像,提取地物特征,构建特征向量.用WWSVDD模型描述每类样本,根据待测样本在特征空间中的不同分布,分别采用最小相对距离法和隶属度函数法进行决策分类,从而实现病害松树的识别.实验结果表明,与传统的K近邻和支持向量数据描述多分类方法相比,所提方法在识别病害松树方面准确性更高.
英文摘要：
For the problem of misjudgment in the fuzzy data domain,a modified multi-classification algorithm of weighted wavelet support vector data description（ WWSVDD） is proposed and applied to the infected pine recognition in remote sensing images. Firstly,utilizing the high resolution images acquired by the double spectrum camera fixed on the unmanned aerial vehicle,the features of ground objects are extracted to construct the corresponding feature vectors. Secondly,each kind of ground objects samples is described by WWSVDD model. Finally,according to the distribution of test samples in the feature space, the methods of minimum relative distance and membership function are respectively used to decide the labels of the samples,so that the infected pines are recognized ultimately. The experiment results show that the proposed method can recognize the infected pines more effectively than the traditional multi-classification methods of K-nearest neighbor（ KNN） and support vector data description（ SVDD）.
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