Synthetic Aperture Radar (SAR) imagery provides all-weather, all-day, and high-resolution imaging capabilities but its unique imaging mechanism abstract imagery that severely lacks the high-fidelity contours and textures essential for automated interpretation and demanding pixel-level downstream tasks. Translating SAR images into optical images is a promising solution to enhance interpretation and support downstream tasks. Most existing research focuses on scene-level translation, with limited work on object-level translation due to the scarcity of paired data and the challenge of accurately preserving contour and texture details. To address these issues, this study proposes a keypoint-guided diffusion model KeypointDiff for SAR-to-optical image translation of unpaired aircraft targets. leverages keypoints as modality-agnostic structural anchors, enabling a novel training strategy that establishes structural-level correspondence between the unpaired SAR and optical domains. Based on the classifier-free guidance diffusion architecture, a class-angle guidance module (CAGM) is designed to integrate class and angle information into the diffusion generation process. Furthermore, a detector-based supervision loss and a visual consistency loss are employed to improve image fidelity and detail quality, tailored for aircraft targets. During sampling, aided by a pre-trained keypoint detector, the model eliminates the requirement for manually labeled class and azimuth information, enabling automated SAR-to-optical translation. Experimental results demonstrate that the proposed method outperforms existing approaches across multiple metrics, providing an efficient and effective solution for object-level SAR-to-optical translation and pixel-level detail recovery. Moreover, the method exhibits strong zero-shot generalization to untrained aircraft types, highlighting the model's practical applicability.

Three-dimensional shape retrieval from synthetic aperture radar (SAR) images has long presented a significant research challenge, with single-view reconstruction proving even more complex due to constraints such as the scarcity of labeled data, limited sample diversity, and heightened sensitivity to radar scattering characteristics. Recently developed deep learning-based methods have made progress in single-view target reconstruction from SAR images. However, these methods still rely heavily on 3-D ground-truth supervision and fail to fully leverage SAR imaging mechanisms for 3-D reconstruction. To address these limitations, an end-to-end unsupervised single-view 3-D reconstruction framework based on a differentiable SAR renderer (DSR) is proposed, achieving precise reconstruction while eliminating the need for ground-truth data. Specifically, the encoder-decoder architecture effectively extracts 3-D and angular features, utilizing template deformation to preserve both fine details and global structures across scales, along with essential pose information for 3-D shape reconstruction. The reconstructed 3-D model is projected onto a 2-D plane, and pixel-level intersection over union (PIoU) loss is employed for unsupervised learning, enabling the extraction of discriminative latent structures and patterns. This approach effectively reduces low-frequency noise, sharpens critical edges, and enhances high-frequency details, improving spatial structure accuracy while minimizing shape distortions and height errors in complex targets. Extensive quantitative and qualitative experiments on both simulated and real datasets demonstrate the framework’s superior performance in single-view SAR 3-D target reconstruction, offering a promising solution with broad potential applications.

In recent years, supervised learning has seen notable progress in research on aircraft target detection using synthetic aperture radar (SAR) images, but its performance is heavily restricted by the amount of labeled data. Self-supervised learning (SSL) can address this problem by pre-training on unlabeled data to extract generalizable features. However, existing SSL methods are predominantly developed based on approaches designed for natural image processing, making them unsuitable for fully understanding SAR images. Therefore, this study proposes a self-supervised contrastive learning method (SARDet-CL) that integrates feature enhancement and imaging mechanism constraints, tailored to the characteristics and requirements of aircraft target detection tasks. Specifically, SARDet-CL enhances the model’s ability to represent spatiotemporal features through spatial feature masking and multi-temporal consistency learning, and robustly extracts target features using a dynamic threshold quantization strategy. In addition, structural information is inferred based on the SAR imaging mechanism and incorporated as an auxiliary self-supervisory signal to guide the model toward a more accurate understanding of SAR images. Experimental results based on various SAR datasets show that the proposed method outperforms other pre-training techniques in down-stream SAR target detection tasks.

Compared to optical sensors, Synthetic Aperture Radar (SAR) can acquire remote sensing imagery under all-weather and all-time conditions. This technology is extensively applied in maritime vessel detection. Recently, deep learning approaches have shown promising performance in ship detection in SAR images. However, accurate detection still poses challenges in coastal scenes, especially in areas with densely arranged ships. To address this issue, this paper presents a ship target detection method for nearshore areas based on cluster detection. This method employs a cluster detection module to focus the detector on densely arranged ship areas and performs refined secondary detection. Subsequently, local and global results are strategically combined to derive the final outcome. Experiments conducted on the public datasets SSDD and RSDD-SAR validate the efficiency of the proposed method in detecting ships in densely arranged areas.

The electromagnetic inverse task has long been recognized as a challenging research problem, which attracted substantial attention from the microwave community. In this paper, our objective is to explore the intricate relationship between geometric model imaging and radar view angles in Synthetic Aperture Radar (SAR) images, mainly focusing on the inverse problem of radar view angle estimation given a target model. However, the high cost and limited availability of SAR data acquisition, along with background interference and complex imaging mechanisms in SAR images, present significant challenges to the generalization, robustness, and accurate feature extraction of existing methods. To address these issues, we propose an interactive deep reinforcement learning (DRL) framework, which facilitates the interaction between the agent and an embedded electromagnetic simulator environment to simulate a human-like process of angle prediction step-by-step. A large number of experimental results verified that the proposed method can accurately predict the radar perspective of SAR images.

Synthetic aperture radar (SAR) has emerged as a vital tool for ship monitoring due to its all-weather, all-day high-resolution imaging capabilities. In practical operations, the wide coverage and sparse ship distribution in very large full-scene SAR images pose challenges in terms of low efficiency and high false alarm rates. Traditional methods perform poorly in complex scenarios, while deep learning (DL) methods have high computation cost. This study proposes a fast progressive detection algorithm for ship targets in large SAR images, combining the advantages of traditional Non-DL methods and DL approaches. First, at a global scale, image preprocessing operations based on traditional methods are designed to quickly extract candidate regions. Then, at regional scale, an oriented ship detector is designed for refined ship detection within candidate regions. Finally, at individual-target scale, a false alarm discrimination network is constructed to further remove false alarms. Experimental results on GF-3 full-scene SAR images demonstrate that the proposed method can achieve minutes-level detection efficiency in images of billion-pixel-level size, while achieving high detection accuracy.

Amidst the landscape of contemporary remote sensing technology, the endeavor to detect and recognize aircraft within remote sensing images (RSIs) assumes pivotal strategic and practical significance. The complex nature of fine-grained aircraft recognition is a result of the intricate interplay between aircraft and their background environments, alongside category imbalance, which collectively lead to the emergence of a long-tail distribution within the dataset. However, experts proficient in RSIs interpretation can effectively address these challenges through the application of prior knowledge. This paper introduces the Aircraft Reasoning Network (ARNet), a framework tailored for aircraft detection and fine-grained recognition in RSIs, building upon prior knowledge employed in expert interpretation. Specifically, the Knowledge Reasoning Module (KRM) introduces a knowledge graph that incorporates both common and expert knowledge into the end-to-end network. Additionally, the network encompasses a Spatial Context Module (SCM) and an Airport Facility Relationship Module (AFRM). These components facilitate highly accurate detection and recognition of fine-grained aircraft in diverse environmental contexts by employing adaptive prior knowledge reasoning and optimizing target spatial location. Furthermore, an independent Aircraft Component Discrimination Module (ACDM) distinguishes aircraft based on their predominant component features, contributing to improved classification performance in both the few-shot and easily confused categories. Moreover, this paper introduces the AR-RSI dataset, a compilation of RSIs capturing fine-grained aircraft targets from diverse locations. The effectiveness and superiority of ARNet are exemplified on AR-RSI, achieving a minimum of 3.7 percentage higher mAP than the mainstream aircraft detection framework.

This article attempts to infer the orientation angle of the target in synthetic aperture radar (SAR) images using reinforcement learning (RL). It is intended to address the challenges like limited interpretability, the scarcity of SAR data, and complex imaging mechanisms restrict the broader application of learning-based approaches. We propose an interactive deep RL (DRL) framework, where an electromagnetic simulator named differentiable SAR renderer (DSR) is embedded to facilitate the interaction between the agent and the environment. Specifically, DSR generates SAR images at arbitrary orientation angles in real time, helping to simulate a human-like process of angle estimation. The differences in sequential and semantic aspects between images of different orientation angles are leveraged to construct the state space in DRL, which effectively suppress the complex background interference, and enhance the sensitivity to temporal variations. Moreover, to maintain the stability and convergence of our approach, reward mechanisms such as memory difference, smoothing and boundary penalty are incorporated to contribute to the formulation of the comprehensive reward function. Extensive experiments performed on both simulated and real datasets demonstrate the effectiveness and robustness of our proposed method. In addition, when utilized in the cross-domain area, the proposed method mitigates inconsistency between simulated and real domains, outperforming reference methods significantly.

Synthetic Aperture Radar (SAR) is extensively employed in both civilian and military sectors, with recent advancements leveraging deep learning for automatic SAR image interpretation. However, the effectiveness of these techniques, particularly Convolutional Neural Networks (CNN), is challenged by insufficient angle range in actual samples due to satellite incident angle constraints. This article proposes a method for generating multi-view samples of SAR targets based on a CNN module integrated with Differentiable SAR Renderer (DSR). Specifically, a polygon mesh is reconstructed from two-dimensional (2D) SAR images through the CNN module, and the DSR is utilized to reversely render SAR target images of various viewpoints from the reconstructed mesh, including the samples used to match with original input 2D images. Then, the generated images is used to compute the loss in training phase, and no three-dimensional (3D) ground truth is required. Experiments are conducted on simulated SAR images and the results demonstrate the efficacy of multi-view sample generation for SAR targets.

Detecting infrared targets in complex backgrounds is a demanding task, especially for small targets. Infrared small target is usually difficult to be detected accurately because of the complex background and the lack of rich color information and edge information. Based on this difficulty, a Multiscale Interactive Attention Network (MIA-Net) is proposed to accurately detect small infrared targets in complex backgrounds. Specifically, the feature skip interactive module (FSIM) is firstly designed for realizing multiscale feature information transmission and fusion, which solves the problem of losing infrared small target features after multiple convolutions. Based on this, a multiscale edge reconstruction (MER) block is designed to learn the edge features of small targets and achieve optimal reconstruction of their edges. Extensive experiments were conducted on the SIRST dataset to evaluate the proposed MIA-Net and the results show that our MIA-Net performs better in terms of IoU, Pd, and Fa.

The model performance of object detection task may dramatically deteriorate when meeting the new dataset with discrepant data distribution compared with trained images. Especially for Synthetic Aperture Radar (SAR) images, the complicated imaging mechanism and diverse environments probably induce intense changes in image appearance and hurt the detection capability and robustness of models based on deep learning. In this paper, a method of learning strong interference characteristics of SAR images is proposed and conducted to generate artificial SAR images as extra training samples in the downstream task -- object detection to improve the detection accuracy and decrease the missing rate of models. Our approach utilized as a data augmentation strategy without annotation cost is confirmed to be efficacious and reliable by multiple experiments.

In real-world recognition of remote sensing (RS) targets, large amount of RS data is hard to be acquired at once, but arrives in batches, which means the constantly adaption of models for new data and new classes. However, training old and new data together from scratch has certain requirements on data storage space and retraining time, so incremental learning come to be desirable for future RS recognition systems. In this article, a class incremental learning method based on knowledge distillation is proposed for RS image classification. In order to better retain the knowledge of old tasks, a relation-based loss is used as a new matching manner in distillation loss, which frees the student model from the burden of matching the exact output of the teacher model. Experiment results based on UC Merced 21, NWPU-RESISC45 and plane objects of FAIR1M demonstrate the advantages of the proposed method.

Three-dimensional (3D) reconstruction of complex targets on the ground from multi-view synthetic aperture radar (SAR) images is of great interests. The inherently-integrated forward-inverse architecture of the differentiable SAR renderer (DSR) provides a promising solution to the general inverse problem of SAR target reconstruction. In this context, the target’s shadow provides complementary information to its scattering image. Hence, this paper proposes a novel DSR-based target reconstruction approach using both the target image and its shadows. The capabilities of DSR are extended to generate not only target scattering images but also shadows. Furthermore, the gradients of the outputs, specifically illumination map and shadow map, with respect to the inputs, i.e., target geometry represented as a mesh, are derived. This enables us to develop a gradient-descent inverse approach for solving the general reconstruction problem. Extensive simulations and quantitative evaluations demonstrate that incorporating both the target scattering image and its shadows significantly improves the reconstruction performance. Moreover, our analyses indicate that achieving optimal reconstruction effects requires a minimum of 9 views with a relatively even distribution. Finally, the proposed algorithm is validated using real SAR images of vehicle targets.

With the continuous development of Deep Learning (DL), ship detection in SAR (Synthetic Aperture Radar) images based on convolutional neural networks (CNN) has become a common approach. CNNs with complex structures have achieved good performance in SAR images, but face challenges such as high time consumption and high false alarm rate, because of the sparsity of ships in the remote sensing images. In this paper, a rotated ship detection network based on the CFAR (Constant False Alarm Rate) preprocessing is proposed to address these problems. It first uses a CFAR preprocessing to fast narrow down the scope of detection so as to save processing time. Then, a classification network is designed to reduce the false alarm rate. The experiment results based on the Gaofen-3(GF-3) dataset show that the proposed method can reduce the false alarm rate greatly and use much less CPU time.

In the 2020 Gaofen Challenge on Automated High- Resolution Earth Observation Image Interpretation [1], there is a topic on airplane detection and recognition in optical images. The task is to detect and classify 10 types of civil airplanes, and the difficulty lies in the classification of similar airplanes. We propose a novel airplane detection and recognition method incorporating component detection, which is based on data preprocessing, basic detection network and object head detection. With the experiments between our approach and other detection networks in local training dataset, online validation and test dataset, our method effectively improved the performance of detection and recognition of airplanes in optical images.