Ship detection in polarimetric synthetic aperture radar (PolSAR) images has attracted widespread attention in recent years. However, pixel-level detection methods are heavily affected by inherent speckle noise. In this letter, we proposed a detection method that enhances the ship-sea contrast beforehand by combining local statistical saliency and scattering mechanism coherence in superpixel-level. First, simple linear iterative clustering (SLIC)-based segmentation method is adopted for PolSAR images to generate superpixels. Then, local saliency is calculated based on superpixel-level similarity from the perspective of statistical characteristics. Based on this, the superpixel-level modified polarimetric coherence metric is obtained from the perspective of physical scattering mechanisms, which can help distinguish small ships with low saliency and strong sea clutters with high saliency. Ship detection is achieved by combining the two features above. The experimental results based on real PolSAR data show that compared with other classic and state-of-the-art methods, the proposed method has improved the figure of merit (FoM) by at least 4.28% and has increased the target clutter ratio by at least 8.43 decibel (dB) on average.

Deep learning methods have made significant progress in the field of synthetic aperture radar (SAR) target recognition, effectively extracting target image domain features. However, they generally overlook the inherent electromagnetic scattering (ES) characteristics of targets, which limits recognition accuracy and physical interpretability. To address this issue, this paper proposes a multi-azimuth scattering feature fusion network (MASFF-Net) that integrates ES features with image domain features for SAR target recognition. Initially, we design a multi-azimuth scattering feature extraction module (MASF) to acquire SAR sub-aperture image data from multiple azimuths and capture the strong scattering point (SSP) features of multi-azimuth SAR images, whose spatial distribution characterizes the geometric structure information of the target. Subsequently, we propose a hierarchical multi-scale feature extraction module (HMFE) to explore the global semantic and local detail features of the target. Finally, we design a scattering-guided feature fusion module (SGFF), which achieves the organic fusion of ES features and image features under the guidance of geometric scattering prior information, and enhances key region features using a multi-branch coordinate attention mechanism. Experiments on a self-constructed measured dataset of 10 types of SAR vehicle targets, the moving and stationary target acquisition and recognition (MSTAR) benchmark dataset, and the SAR-AIRcraft-1.0 aircraft dataset demonstrate that this method achieves higher recognition performance while maintaining physical interpretability.

Nearshore ship detection has significant applications in both the military and civilian domains. Compared to synthetic aperture radar (SAR), polarimetric synthetic aperture radar (PolSAR) provides richer information for analyzing the scattering mechanisms of ships and enables better detection of ship targets. However, ships in nearshore areas tend to be highly concentrated, and ship detection is often affected by adjacent strong scattering, resulting in false alarms or missed detections. While the GP-PNF detector performs well in PolSAR ship detection, it cannot obtain satisfactory results in these scenarios, and it also struggles in the presence of azimuthal ambiguity or strong clutter interference. To address these challenges, we propose a nearshore ship detection method named ECD-PNF by integrating superpixel-level GP-PNF and refined polarimetric decomposition. Firstly, polarimetric superpixel segmentation and sea–land segmentation are performed to reduce the influence of land on ship detection. To estimate the sea clutter more accurately, an automatic censoring (AC) mechanism combined with superpixels is used to select the sea clutter superpixels. By utilizing refined eight-component polarimetric decomposition to improve the scattering vector, the physical interpretability of the detector is enhanced. Additionally, the expression of polarimetric coherence is improved to enhance the target clutter ratio (TCR). Finally, this paper combines the third eigenvalue of eigenvalue–eigenvector decomposition to reduce the impact of azimuthal ambiguity. Three spaceborne PolSAR datasets from Radarsat-2 and GF-3 are adopted in the experiments for comparison. The proposed ECD-PNF method achieves the highest figure of merit (FoM) value of 0.980, 1.000, and 1.000 for three datasets, validating the effectiveness of the proposed method.

Superpixel segmentation for polarimetric synthetic aperture radar (PolSAR) images plays a key role in remote-sensing tasks, such as ship detection and land-cover classification. However, the existing methods cannot directly generate multi-scale superpixels in a hierarchical style and they will take a long time when multi-scale segmentation is executed separately. In this article, we propose an effective and accurate hierarchical superpixel segmentation method, by introducing a minimum spanning tree (MST) algorithm called the Boruvka algorithm. To accurately measure the difference between neighboring pixels, we obtain the scattering mechanism information derived from the model-based refined 5-component decomposition (RFCD) and construct a comprehensive dissimilarity measure. In addition, the edge strength map and homogeneity measurement are considered to make use of the structural and spatial distribution information in the PolSAR image. On this basis, we can generate superpixels using the distance metric along with the MST framework. The proposed method can maintain good segmentation accuracy at multiple scales, and it generates superpixels in real time. According to the experimental results on the ESAR and AIRSAR datasets, our method is faster than the current state-of-the-art algorithms and preserves somewhat more image details in different segmentation scales.

The distinctive electromagnetic scattering characteristics of SAR target images are crucial for SAR target recognition. However, existing purely data-driven paradigms often overlook inherent physical scattering laws, which limits model robustness and interpretability. To address this issue, we propose a physics-aware interpretable framework, termed the attribute scattering center (ASC)-guided deformable capsule network (ASC-DCapsNet). The proposed network adopts a two-stage learning framework. In the first stage, an ASC-guided capsule network is constructed to extract ASC Capsules with explicit electromagnetic scattering semantics. Within the capsule architecture, we design a multi-scale densely connected deformable (MSDD) module to adaptively capture target features at different scales. In addition, we design a polarity attribute feature perception (PAFP) module to enhance the exploitation of target attribute information. In the second stage, ASC Capsules are embedded into deep attribute capsules to obtain a fused representation of physical scattering features and deep semantic features, thereby achieving reliable SAR target recognition. Furthermore, to further advance research on SAR target recognition, we construct and publicly release a new high-resolution benchmark dataset, termed ATR-SARVehicle-1.0 (https://doi.org/10.57760/sciencedb.j00001.01475), which covers multiple frequency bands and terrain backgrounds. Extensive experiments conducted on both the proposed benchmark and the MSTAR dataset demonstrate that the proposed method outperforms existing approaches in terms of recognition accuracy and interpretability.