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.