Real-World ISAR Object Recognition Using Deep Multimodal Relation Learning

Abstract

Real-world inverse synthetic aperture radar (ISAR) object recognition is a critical and challenging problem in computer vision tasks. In this article, an efficient real-world ISAR object recognition method is proposed, namely, real-world ISAR object recognition (RIOR), based on deep multimodal relation learning (DMRL). It cannot only handle the complex multimodal recognition problem efficiently but also exploit the relations among the features, attributes, labels, and classes with semantic knowledge: 1) an adaptive multimodal mechanism (AMM) is proposed in convolutional neural network (CNN) to substantially promote the CNN sampling and transformation capability and significantly raise the output feature map resolutions by keeping almost all of the information; 2) deep attribute relation graph learning (DARGL) is proposed to jointly estimate the large numbers of heterogeneous attributes and collaboratively explore the relations among the features, attributes, labels, and classes with common knowledge graphs; and 3) relational-regularized convolutional sparse learning (RCSL) is proposed to further achieve good translation invariance and improve the accuracy and speed of the entire system. Extensive qualitative and quantitative experiments are performed on two real-world ISAR datasets, demonstrating that RIOR outperforms the state-of-the-art methods while running quickly.