MBLKNet: a large kernel convolution-driven network with multi-task self-supervised learning for SAR maritime target classification

Synthetic aperture radar (SAR) maritime target classification serves as a critical component in modern maritime surveillance. While deep learning networks, particularly convolutional neural networks (CNNs), have driven substantial progress in this domain, three key challenges constrain their performance and practical deployment: 1) In SAR maritime images, complex inshore backgrounds and speckle noise are prevalent. Targets such as ships span a wide range of scales due to different imaging resolutions and intrinsic size variability, exacerbating inter-class similarity and intra-class variability, 2) Labeled data for SAR maritime target classification are scarce, and sensor imaging modes differ markedly across platforms, and 3) Existing CNNs that fuse traditional hand-crafted features often explicitly treat hand-crafted feature extraction as a necessary component of the network and primarily focus on classification performance, overlooking the requirement to efficiently leverage their feature extraction capabilities in downstream tasks. To overcome these challenges, this article proposes a novel SAR maritime target classification network (MBLKNet) based on large kernel convolution and multi-task self-supervised learning. In MBLKNet, four improved designs for network structure are proposed to enhance classification accuracy: 1) macro design, 2) multi-branch large kernel convolution module (MBLKCM), 3) lightweight channel-interactive multi-layer perceptron (LCIMLP), and 4) micro design. In addition, a multi-resolution unlabeled SAR maritime target dataset (SL-SARShip) and a masked image modeling framework, HOGSparK, are proposed to enable the pre-training of MBLKNet under joint supervision of pixel and HOG features. Comparison results on OpenSARShip 2.0 and FUSAR-Ship with state-of-the-art networks, as well as experiments on SSDD for SAR downstream target detection and instance segmentation, demonstrate that the proposed MBLKNet achieves superior performance and strong feature extraction ability.