Multisource Heterogeneous Transfer Learning via Feature Augmentation for Ship Classification in SAR Imagery
Haitao Lang, Guang’an Yang, Chunnan Li, Jianwen Xu
Abstract
Improving ship classification performance in synthetic aperture radar (SAR) imagery by the methods based on transfer learning (TL) is a newly emerging research topic and has great potential. The existing studies merely address the problem of transfer learning from a single source domain (AIS or ORS) to the target domain (SAR) based on the homogeneous transfer learning (HoTL) which requires all domains are represented by homogeneous features with same dimensions. Our work takes a step forward and attempts to address a more meaningful and challenging problem that transfers knowledge from multiple source domains for the purpose of exploring and exploiting complementarity cross source domains to assist ship classification in the target domain. To this end, our study develops a multi-source heterogeneous transfer learning (MS-HeTL) method which liberates the restriction of utilizing the exact same features for all domains, allows each domain represented by a more appropriate feature and thus improves the ship classification performance even further. Specifically, we first propose multi-source heterogeneous feature augmentation (MS-HFA) to effectively solve the challenges brought by feature heterogeneity and fully exploit the complementarity cross domains. Then a support vector machine (SVM) classification framework is specific-designed to be incorporated with the augmented feature representations in the common space to conduct knowledge transfer cross domains. Extensive experiments on two benchmark datasets, HR-SAR and FUSAR, show that the proposed method outperforms the existing methods, and demonstrates its effectiveness and advantages. All datasets and source codes are available at https://github.com/BUCT-RS-ML/MS-HeTL-via-MS-HFA.