A Remote Sensing Ship Recognition Method Based on Co-Training Model

Abstract

Aiming at detecting sea targets efficiently, an approach using optical remote sensing data based on co-training model is proposed. Firstly, using size, texture, shape, moment invariants features and ratio codes, feature extraction is realized. Secondly, based on rough set theory, the common discernibility degree is used to select valid recognition features automatically. Finally, a co-training model for classification is introduced. Firstly, two diverse ruducts are generated, and then the model employs them to train two base classifiers on labeled dada, and makes two base classifiers teach each other on unlabeled data to boot their performance iteratively. Experimental results show the proposed approach can get better performance than K-Nearest Neighbor (KNN), Support Vector Machines (SVM), traditional hierarchical discriminant regression (HDR).