Complex ISAR target recognition using deep adaptive learning

Target recognition in real-world environment is a most important and challenging computer vision task. In this paper, a real-world complex ISAR target recognition method is proposed on a computationally limited platform using deep adaptive learning (DAL). Particularly, adaptive multimodal mechanism (AMM) is presented to efficiently handle the complex multimodal recognition problem, which substantially improves CNNs’ sampling and transformation capability and significantly increases output feature maps’ resolutions. Moreover, feature learning, attribute prediction, relation mining, image matching, image understanding, image summarization, and label expansion are considered in a unified framework. Extensive qualitative and quantitative experiments are performed, and the results show the proposed method outperforms the several state-of-the-art methods.