A Target-Oriented Multisource Association Model Based on Triple-Unit-Graph and Feature Constraint Representation Learning for SAR Target Detection Tasks

When it comes to multisource collaboration issues, previous registration-based studies always attempt to build reliable connections between two sources. However, these pixel-oriented algorithms often highly rely on mapping functions or require other auxiliary data. In this letter, a target-oriented multisource association model (ToMsAM) is proposed. It is a new perspective of multiinformation integration driven by practical applications in remote sensing. The two main parts in ToMsAM gradually use the similarity information in pixel and feature level. First, to cover the comprehensive prior information, a triple-unit-graph is built. Then, a feature-constraint deep neural networks for graph representations (FC_DNGRs) model is adopted to automatically learn the compressed representation. So far, the heterogeneous samples are unified in a new feature space. These new abstractions associated with the source domain can solve the problem of insufficient samples in the target domain. Real remote sensing images acquired by synthetic aperture radar (SAR) and optical satellites are utilized to conduct experiments on airplane and vessel detection tasks. Experimental results show that the representation obtained by ToMsAM outperformed the baseline representations. The support vector machine (SVM) trained by associated features can complete the detection tasks even in the case of limited quantity of prior samples in the target domain.