Man-made targets subjected to different polarized waves will produce different depolarization effects, and these differences contain abundant information beneficial for recognition. However, traditional manually designed features struggle to fully utilize polarimetric information for scattering characterization. This letter proposes a target scattering characteristic learning network based on the Null-Pol response, which adaptively extracts the proportions of typical scattering mechanisms from mixed scattering mechanisms. Firstly, by leveraging polarimetric modulation, the Discrete Null-Pol Synthesis Pattern (DNSP) is designed to fully reveal the differences in target scattering mechanisms. On this basis, we propose an end-to-end scattering inversion network module to learn the DNSPs of different typical targets under scattering ambiguity conditions, obtaining polarimetric scattering contribution of 10 typical structures. Finally, we conduct structure recognition experiments to demonstrate the effectiveness of the proposed module. The results show that the proposed method can effectively characterize scattering behavior and significantly improve the performance of target structure recognition.

Vehicle detection is an important application of polarimetric synthetic aperture radar (PolSAR). Geometrical perturbation polarimetric notch filter (GP-PNF) establishes a feature space based on the local background polarimetric characteristics to achieve adaptive detection of ship targets. However, the complexity of the ground background presents additional challenges compared to sea surface. In this work we model the distribution of the GP-PNF and prove its effectiveness and accuracy compared with other common distribution models based on real airborne mini-SAR data. And then we introduce a numerical calculation of logarithm cumulants for parameters estimation, derive the constant false alarm rate (CFAR) threshold computation formula and apply the filter to vehicle detection. Experiments performed on real high-resolution PolSAR images verify the good performance of the detection method.

Large-scale, diverse and high-quality data is the foundation and key to achieving good generalization in target detection and recognition for deep learning-based algorithms. Directly collecting synthetic aperture radar (SAR) image data faces the difficulty in acquisition and high costs. Traditional SAR image simulation methods are limited by geometric and electromagnetic computation errors in their modeling process, and the high computational burden as well. Generative adversarial networks (GANs) offer a new approach for SAR image generation, but they struggle to achieve satisfactory results in terms of image quality and diversity. In order to overcome this problem, we propose a new type of GAN to learn the spatial relationship of the targets more effectively. Taking the real SAR images as input, we extract the target information through the capsule network, perturb the extracted features and adopt the attention mechanism to improve the quality and diversity of the augmented data.

Deep learning methods have made profound advancements in the field of synthetic aperture radar (SAR) target recognition. Typically, a significant amount of training data is required. However, due to the high degree of prior expert knowledge required for the annotation of SAR images, it is challenging to obtain a large amount of labeled data, which significantly impacts the performance of target recognition. To address this issue, this paper introduces an Azimuth-Aware Multi-Head Residual Self-Attention Network (MHRA-Net) that can extract high discriminative features of targets. Initially, this model employs a sub-aperture decomposition method to expand target information across multiple azimuth angles. Subsequently, we design a multi-head residual self-attention mechanism that can extract salient features of targets from multiple perspectives. Finally, a multi-scale feature fusion module is used to extract both global and local information about the target, enhancing model robustness and allowing the network to achieve satisfactory recognition performance even under sample-constrained conditions. Experimental results on a 10-class vehicle SAR image dataset demonstrate the effectiveness of the proposed approach.

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. Firstly, 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 by at least 4.28% and has increased the target clutter ratio by at least 8.43 decibel (dB) on average.

Superpixel segmentation is essential to the rapid information extraction and image interpretation. In this paper, we develop a superpixel segmentation method for polarimetric synthetic aperture radar (PolSAR) images, utilizing minimum spanning tree algorithm (MST) to achieve a hierarchy of superpixels. Thereinto, the revised Wishart distance and region intensity distance is applied to accurately measure the dissimilarity between two neighboring pixels. The proposed method can generate superpixels of different scales in real time, so it has significant application value. The performance of the proposed method is validated on experimental PolSAR dataset from the ESAR system.