SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). According to our investigation, up to 46.59% of the total 161 public reports confidently select SSDD to study DL-based SAR ship detection. Undoubtedly, this situation reveals the popularity and great influence of SSDD in the SAR remote sensing community. Nevertheless, the coarse annotations and ambiguous standards of use of its initial version both hinder fair methodological comparisons and effective academic exchanges. Additionally, its single-function horizontal-vertical rectangle bounding box (BBox) labels can no longer satisfy the current research needs of the rotatable bounding box (RBox) task and the pixel-level polygon segmentation task. Therefore, to address the above two dilemmas, in this review, advocated by the publisher of SSDD, we will make an official release of SSDD based on its initial version. SSDD’s official release version will cover three types: (1) a bounding box SSDD (BBox-SSDD), (2) a rotatable bounding box SSDD (RBox-SSDD), and (3) a polygon segmentation SSDD (PSeg-SSDD). We relabel ships in SSDD more carefully and finely, and then explicitly formulate some strict using standards, e.g., (1) the training-test division determination, (2) the inshore-offshore protocol, (3) the ship-size reasonable definition, (4) the determination of the densely distributed small ship samples, and (5) the determination of the densely parallel berthing at ports ship samples. These using standards are all formulated objectively based on the using differences of existing 75 (161 × 46.59%) public reports. They will be beneficial for fair method comparison and effective academic exchanges in the future. Most notably, we conduct a comprehensive data analysis on BBox-SSDD, RBox-SSDD, and PSeg-SSDD. Our analysis results can provide some valuable suggestions for possible future scholars to further elaborately design DL-based SAR ship detectors with higher accuracy and stronger robustness when using SSDD.

Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials in how deep learning can further improve performance and enable new functionalities for DH. Here, we survey recent developments in various DH applications powered by deep learning algorithms. This article starts with a brief introduction to digital holographic imaging, then summarizes the most relevant deep learning techniques for DH, with discussions on their benefits and challenges. We then present case studies covering a wide range of problems and applications in order to highlight research achievements to date. We provide an outlook of several promising directions to widen the use of deep learning in various DH applications.

Ship detection in synthetic aperture radar (SAR) images is a significant and challenging task. However, most existing deep learning-based SAR ship detection approaches are confined to single-polarization SAR images and fail to leverage dual-polarization characteristics, which increases the difficulty of further improving the detection performance. One problem that requires a solution is how to make full use of the dual-polarization characteristics and how to excavate polarization features using the ship detection network. To tackle the problem, we propose a group-wise feature enhancement-and-fusion network with dual-polarization feature enrichment (GWFEF-Net) for better dual-polarization SAR ship detection. GWFEF-Net offers four contributions: (1) dual-polarization feature enrichment (DFE) for enriching the feature library and suppressing clutter interferences to facilitate feature extraction; (2) group-wise feature enhancement (GFE) for enhancing each polarization semantic feature to highlight each polarization feature region; (3) group-wise feature fusion (GFF) for fusing multi-scale polarization features to realize polarization features’ group-wise information interaction; (4) hybrid pooling channel attention (HPCA) for channel modeling to balance each polarization feature’s contribution. We conduct sufficient ablation studies to verify the effectiveness of each contribution. Extensive experiments on the Sentinel-1 dual-polarization SAR ship dataset demonstrate the superior performance of GWFEF-Net, with 94.18% in average precision (AP), compared with the other ten competitive methods. Specifically, GWFEF-Net can yield a 2.51% AP improvement compared with the second-best method.

Mask-based lensless imaging is an emerging imaging modality, which replaces the lenses with optical elements and makes use of computation to reconstruct images from the multiplexed measurements. Most existing reconstruction algorithms are implemented assuming that the forward imaging process is a convolution operation, with a point spread function based on the system model. In reality, there is model mismatch, leading to inferior image reconstruction results. In this paper, we investigate the impact of model mismatch in mask-based lensless imaging and for the first time, illustrate the accumulated artifacts and information loss due to mismatch error in the state-of-the-art approaches, which perform model-based reconstruction and learning-based enhancement in separate stages. To overcome this, we develop a novel physics-informed deep learning architecture that aims at addressing such mismatch error. The proposed hybrid reconstruction network consists of both unrolled model-based optimization to apply system physics and deep learning layers for model correction. Besides a cascaded enhancement network, we introduce a data-driven branch in parallel, making use of both input measurement and all intermediate outputs from the model-based layers to correct the bias and compensate for the information loss due to model mismatch. The effectiveness and robustness of the proposed model mismatch compensation network, referred to as MMCN, is demonstrated on real lensless images. Experimental results show noticeably better performance for MMCN compared with the alternative methods.

A capsule network, as an advanced technique in deep learning, is designed to overcome information loss in the pooling operation and internal data representation of a convolutional neural network (CNN). It has shown promising results in several applications, such as digit recognition and image segmentation. In this work, we investigate for the first time the use of capsule network in digital holographic reconstruction. The proposed residual encoder-decoder capsule network, which we call RedCap, uses a novel windowed spatial dynamic routing algorithm and residual capsule block, which extends the idea of a residual block. Compared with the CNN-based neural network, RedCap exhibits much better experimental results in digital holographic reconstruction, while having a dramatic 75% reduction in the number of parameters. It indicates that RedCap is more efficient in the way it processes data and requires a much less memory storage for the learned model, which therefore makes it possible to be applied to some challenging situations with limited computational resources, such as portable devices.

Effective ship detection in synthetic aperture radar (SAR) imagery is crucial for maritime safety and surveillance. Despite the advancements in deep learning for SAR ship detection, significant challenges remain, particularly in large scenes. These challenges are twofold: the detection of extremely small ships is often hindered by inadequate feature extraction, and the presence of inshore ships is obscured by pronounced land-based interference, both of which lead to reduced detection accuracy. To address these issues, we present a novel deep learning framework that integrates constant false alarm rate (CFAR) processing with dual-polarization data, termed the CFAR-guided dual-polarization fusion framework (CFAR-DP-FW). The integration is designed to enhance the detection sensitivity for small-scale maritime targets by utilizing dual-polarization's rich feature representation, and CFAR's strength in suppressing background noise, highlighting potential targets. The proposed CFAR-DP-FW consists of three core components: the CFAR dual-polarization detector (CFAR-DPD) provides initial target indication; the CFAR field generator (CFAR-FG) constructs a probabilistic ship presence map, reducing reliance on CFAR's hard thresholds; and the CFAR guidance dual-polarization network (CFAR-GDP-Net) incorporates a novel feature extractor and enhancement module, tailored to amplify relevant features, and suppress noise. This strategic fusion within our framework markedly improves the detection of small and inshore ships. Evaluated on the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0), our framework demonstrates superior performance, surpassing twenty state-of-the-art models. It achieves a 3.28% increase in mean average precision for inshore ships over the next best-performing model, validating its efficacy in tackling the intricate challenges of large-scale SAR ship detection.