Multisource Heterogeneous Transfer Learning via Feature Augmentation for Ship Classification in SAR Imagery

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 TL from a single source domain (SD) [automatic identification system (AIS) or optical remote sensing (ORS)] to the target domain (TD) (SAR) based on the homogeneous TL (HoTL), which requires all domains to be represented by homogeneous features with the same dimensions. Our work takes a step forward and attempts to address a more meaningful and challenging problem that transfers knowledge from multiple SDs for the purpose of exploring and exploiting complementarity cross SDs to assist ship classification in the TD. To this end, our study develops a multisource heterogeneous transfer learning (MS-HeTL) method that 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 multisource 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
.