The rapid development of remote sensing technology has led to a sharp increase in the amount of synthetic aperture radar (SAR) measurements, which put forward higher requirements for remote sensing image processing. As an important application of SAR, fast and accurate ship detection has always been a research hotspot. In this article, an improved lightweight RetinaNet for ship detection in SAR images is proposed. Compared with the standard RetinaNet, the shallow convolutional layers of the backbone are replaced by ghost modules and the number of the deep convolutional layers is reduced. The spatial and channel attention modules are embedded into the model to enhance detectability. K -means clustering algorithm is applied to adjust the initial aspect ratios of the model. The effectiveness and robustness of the proposed method is demonstrated by numerical experiments with SSDD dataset, Gaofen-3 mini dataset, and a large-scale SAR image of Hisea-1 satellite, it is shown that the proposed method can significantly reduce the floating-point operations and the number of parameters without decreasing the detection accuracy and recall ratio. Moreover, the experimental results also show the proposed model's robustness and the ability to detect ship targets in small datasets.

Object detection and recognition (OD&R) based on deep learning is a hot topic in the application of synthetic aperture radar (SAR). These methodologies based on deep learning are inherently data-driven, which means that their performance is subjected to the corresponding datasets. Although existing datasets have included some common targets collected from real-valued intensity SAR images, they still exist some limitations in terms of quantity, categories, diversities, data domain. Hence, it is urgent to establish a large-quantity benchmark for fine-grained OD&R on complex-valued SAR images, which contains rich signal-domain features well coupled with classical physical modeling. Additionally, considering the unique imaging characteristics and diverse imaging conditions, some important attribute information, such as incidence and attitude angles, is necessary to be attached. In this paper, we propose a novel benchmark dataset with more than 340k instances for Fine-grAIned object detection and Recognition based on single look Complex SAR images, which is named as FAIR-CSAR. We collected complex-valued SAR images with a resolution of 1m to 5m from 175 entire images of Gaofen-3 covering 32 cities and multiple sea areas worldwide. All instances in the FAIR-CSAR are annotated by oriented bounding boxes (OBB), covering 5 major categories and 22 subcategories. Compared with existing datasets dedicated to OD&R, the FAIR-CSAR dataset has four particular advantages: (1) it contains complex-valued SAR images from various acquisition modes and polarization modes, including full-scale signal-domain features for object recognition; (2) it is much larger than other existing OD&R datasets in terms of quantity of instances; (3) it provides more fine-grained category annotation, and more detailed attribute information; (4) it provides more challenging images with some common imaging phenomena, such as speckle noise, azimuth ambiguities and so on. To establish a baseline adapted for single look complex SAR images, a multi-domain feature extraction and fusion network (MDNet) is proposed as a novel framework to mine detailed information underlying in various domains. And a series of state-of-the-art (SOTA) algorithms are applied on the FAIR-CSAR to build the fine-grained OD&R benchmark. Experimental results indicate that FAIR-CSAR is closer to practical application and more challenging than existing datasets for SAR images.

Synthetic aperture radar (SAR) possesses significant advantages in aircraft detection due to its all-day and all-weather monitoring capability, but some unique problems in SAR images decrease the performance of aircraft detection. The speckle effect and excessive dynamic range are the most common problems that interfere with the visual features in SAR images and deteriorate detection performance. However, there lacks a detection-oriented image enhancement algorithm to collaboratively solve these two problems. An adaptive image enhancement algorithm is proposed to the improve the performance of aircraft detection in SAR images. The proposed image enhancement algorithm provides a pseudo-color image through speckle suppression and channel combination, which consists of the speckle noise suppression channel, strong scattering feature enhancement channel, and weak scattering feature enhancement channel. The speckle noise suppression is achieved by a despeckle network and the radiational feature enhancement channels are derived from an adaptive quantization method based on the characteristics of amplitude distribution. By optimizing the quality of the input image, the proposed image enhancement algorithm improves the performance of aircraft detection. Experiments based on datasets acquired by GaoFen-3 satellites indicate that the proposed algorithms significantly improve the detection performance of various types of detectors. The source project is available at https://github.com/suoyuxi/ChannelEnhancement.

Traditionally, synthetic aperture radar (SAR)-based ship target detection is performed in the image domain, where SAR imaging processing has to be applied first. However, SAR imaging processing is complex and time-consuming, especially in the wide-swath working mode. Actually, for open sea scenes, most echoes are sea surface signals with no ship targets, and there is no need for imaging processing in those areas. Therefore, non-image domain ship target detection is studied in this letter, and a novel Incept-text convolutional neural network (TextCNN) model is proposed for ship target detection in the SAR range-compressed domain (RCD). In the proposed method, the SAR echo data are converted into a 1-D range profile signal first by range compression and mean pooling, and then, the Incept-TextCNN model is proposed and applied, and information about existence of ship targets in relevant range cells will be its output. Finally, the effectiveness and efficiency of the proposed method is testified by simulation and real spaceborne SAR data, and the results demonstrate that the proposed model can filter out the invalid range-compressed data of the sea surface area, which can significantly reduce the amount of data for subsequent SAR imaging and ship classification.

Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric features, and their scattering characteristics and edge information are sensitive to variations in target attitude angles (TAAs). These factors pose challenges for existing methods to obtain satisfactory results. To address these challenges, a novel target attitude angle-guided network (TAG-Net) is proposed in this article. The core idea of TAG-Net is to leverage TAA information as guidance and use an adaptive feature-level fusion strategy to dynamically learn more representative features that can handle the target imaging diversity caused by TAA. This is achieved through a TAA-aware feature modulation (TAFM) module. It uses the TAA information and foreground information as prior knowledge and establishes the relationship between the ship scattering characteristics and TAA information. This enables a reduction in the intra-class variability and highlights ship targets. Additionally, considering the different requirements of the detection and classification tasks for the scattering information, we propose a layer-wise attention-based task decoupling detection head (LATD). Unlike general deep learning methods that use shared features for both detection and classification tasks, LATD extracts multi-level features and uses layer attention to achieve feature decoupling and select the most suitable features for each task. Finally, we introduce a novel salient-enhanced feature balance module (SFB) to provide richer semantic information and capture the global context to highlight ships in complex scenes, effectively reducing the impact of background noise. A large-scale ship detection dataset (LSSDD+) is used to verify the effectiveness of TAG-Net, and our method achieves state-of-the-art performance.

Aircraft detection in synthetic aperture radar (SAR) images plays a crucial role in supporting essential tasks like airport management and airspace monitoring. Most existing SAR aircraft detection algorithms are predominantly designed based on the scattering characteristics of full-aperture images, which provide high-resolution and rich detail information. However, in full-aperture images, responses from different angles are aliased, making it challenging to disentangle angular-specific details for anisotropic aircraft. Furthermore, the extended synthetic aperture time exacerbates the defocusing of slow-moving aircraft, increasing the likelihood of missed detections. Considering the above challenges, for targets exhibiting anisotropy and dispersion under high resolution, their non-stationary characteristics can be extracted by decomposing sub-apertures and employed as additional information to enhance detection accuracy. Therefore, we propose a dual-aperture feature fusion network (DAF-Net) designed to improve aircraft detection by mining, enhancing, and fusing features from both full-aperture and sub-aperture images.Specifically, a siamese backbone network with position-consistent weight supervision is introduced to efficiently and accurately extract features from the parallel input of full-aperture and sub-aperture images.Subsequently, an aperture-based feature enhancement module (AFEM) is proposed to leverage the scattering characteristics of aircraft and their context, thereby reinforcing the unique semantic information present in both full-aperture and sub-aperture images. Finally, a region-aware cross-attention fusion module (RCFM) is developed, which adaptively associates and fuses full-aperture and sub-aperture features based on spatial mapping relationships. Furthermore, experiments conducted on Complex-valued SAR Aircraft dataset (CSAR-A) demonstrate the effectiveness of the proposed method.DAF-Net improves AP50 and AP75 by 5.8% and 5.3% compared to the baseline, respectively, and achieves optimal performance compared to other detectors.