提出一种基于粗糙集理论和分层判别回归技术的光学遥感舰船目标识别方法。该方法首先提出新的光学遥感舰船识别特征——面积比编码,并与四类特征组合作为备选特征;然后基于粗糙集理论按同可区分度来计算各备选特征的重要性权值,自动选择出对正确识别贡献较大的特征组合;最后根据分层判别回归原理生成分类判决树来识别光学遥感舰船目标。实验结果表明,本文方法在识别精度和速度方面优于最近邻和支持向量机方法,且通用可行。

A new method for ship recognition using optical remote sensing data based on rough set and hierarchical discriminant regression (HDR) is presented in this paper. First, a new shape feature called area ratio code (ARC) is proposed and extracted as a candidate feature. Based on the rough set theory, the common discernibility degree is used to compute the significance weight of each candidate feature and select valid recognition features automatically. Ultimately, a decision tree based on the HDR theory is built to recognize ships in data from optical remote sensing systems. Experimental results on real data show that the proposed method is generalizable and can get better classification rates at a higher speed than the KNN or SVM method.