Efficient complex ISAR object recognition using adaptive deep relation learning

Complex inverse synthetic aperture radar (ISAR) object recognition is a critical and challenging problem in computer vision tasks. An efficient complex object recognition method for ISAR images is proposed based on adaptive deep relation learning. (i) An adaptive multimodal mechanism is proposed to greatly improve the multimodal sampling and transformation capabilities of convolutional neural networks and increase the resolutions of the feature maps. In particular, an adaptive ranking and selection strategy for related regions are proposed. (ii) Deep graphical learning is proposed to jointly estimate large numbers of heterogeneous attributes and leverage the relations among features and attributes. Extensive experiments performed on real‐world datasets show that the proposed method outperforms other state‐of‐the‐art methods.