A remote sensing ship recognition method of entropy-based hierarchical discriminant regression

Aiming at recognizing the battlefield's ship targets on the sea reliably and timely, a discriminative method for ship recognition using optical remote sensing data entropy-based hierarchical discriminant regression (E-HDR) is presented. First, target features including size, texture, shape, and moment invariants features, as well as area ratio codes are extracted as candidate features, and then information entropy is used to choose the attributes in target recognition, which can reduce the interference of redundant attributes to target recognition, and the valid recognition features are selected automatically. Next, entropy is also used to realize the sub nodes splitting adaptively and automatically, which avoids manual intervention well. Ultimately, according to entropy, a decision tree based on hierarchical discriminant regression (HDR) theory is built to recognize ships in data from optical remote sensing systems. Experimental results on real data show that the proposed approach can get better classification rates at a higher speed than k-nearest neighbor (KNN), support vector machines (SVM), affinity propagation (AP) and traditional hierarchical discriminant regression (HDR) methods.