The hyperspectral remote sensing image is rich in spectrum information, so it has advantages in object recognition.One-Class Support Vector Machine (OCSVM) not only holds the advantages of support vector machines but also only needs the train samples of the recognized objects.The algorithm proposed in this paper selected mathematical model,designed kernel function, adjusted parameter adaptively, and added the theory of OCSVM into the object recognition algorithm for hyperspectral image which improved the precision of recognition and reduced the demand of train samples.Lastly, the experiments were conducted on two hyperspectral images, and the results prove the validity of the proposed method.%高光谱遥感影像具有丰富的光谱信息,在地物识别方面具有明显的优势.一类支持向量机(OCSVM)不仅保留了支持向量机的原有优势,而且只需要待识别类型的训练样本.为此提出了算法,通过数学模型选择、核函数设计与参数的自适应调整将OCSVM原理融入到高光谱影像的地物识别算法中,提高了识别的精度,降低了对训练样本的要求.最后利用两幅高光谱影像进行了实验分析,实验结果证明了所提算法的有效性.
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