Abstract: A new classification and fusion method for multi-source of remote sensing images was put forward based on the D-S evidence theory. Select the interesting region of class by experience and obtain basic probability assignment function by extracting feature at first, then combine multi-source image to be grouped with Dempster's orthogonal rule to get the result of classification. Experiments show that the proposed method is superior to the K-mean. The uncertainty in classification is effectively decreased, as well as the classification accuracy is improved.

Key words: D-S evidence theory, remote sensing, image classification, multisource information fusion

摘要： 基于D-S证据理论提出了一种多源遥感图像分类融合的新方法。首先通过人为选择感兴趣的分类区域，提取特征获取基本概率分配函数，将待分类的多源图像进行分类融合，从而得到最终的分类结果。试验表明，相比于K-mean分类方法，这种分类融合方法可以有效地减少分类过程中的不确定性信息，提高分类精度。

关键词: D-S证据理论, 遥感, 图像分类, 多源信息融合