Quad polarimetric (Quad-Pol) and compact polarimetric (Compact-Pol) Inverse Synthetic Aperture Radar (ISAR) are two main configuration modes for space targets imaging. Compared with Quad-Pol ISAR mode, Compact-Pol ISAR mode can reduce radar system complexity at the price of polarimetric information loss. In order to fulfill this gap, this work dedicates to reconstructing the Quad-Pol information of space targets from the Compact-Pol mode, thereby reconciling the need for system simplicity with the retention of abundant Quad-Pol data. The main idea is to design a quad polarimetric reconstruction network (QPRNet) based on the Compact-Pol ISAR data characteristics. Firstly, a group feature fusion (GFF) module is designed to collect the coupling polarimetric features between channels of Compact-Pol ISAR data, making the network better learn the implicit mapping relationships between polarimetric channels. Then, the receptive field expansion (RFE) module is used to obtain large-scale spatial features through the network, which is beneficial to extract polarimetric modulation mechanism between adjacent components of spatial targets. Experimental studies have been carried out in Quad-Pol ISAR data reconstruction. Comparison results show that the Quad-Pol ISAR data reconstructed by proposed method is more similar to the truth. Moreover, compared with the state-of-the-arts, the mean absolute error (MAE), coherence index (COI) and peak signal-to-noise ratio (PSNR) have improved by 4.22%, 4.64% and 2.01%, respectively.

Identifying satellite components in polarimetric inverse synthetic aperture radar (ISAR) images is beneficial for monitoring their operation and health status. Most target recognition methods rely on network structures designed for optical images and fail to consider the inherent polarimetric scattering characteristics. Furthermore, the aliasing of scattering mechanisms caused by the complex structure of man-made targets, along with the scattering diversity resulting from observation perspectives, poses challenges to target polarimetric interpretation. To address these challenges, this study proposes a structure recognition framework embedded within scattering mechanism to achieve pixel-level to component-level structure (CS) recognition. First, through semi-supervised representation learning, the 3-D polarimetric correlation pattern (3-D PCP) of typical polarimetric scattering structures (PSSs) is used as expert knowledge to guide a deep-learning network, enabling pixel-level scattering mechanism separation. On this basis, a relation module is employed to explore the relationships between different pixels’ scattering mechanisms to accomplish component-level recognition. Finally, polarimetric ISAR satellite images and component annotation datasets are constructed. Pixel-level and component-level comparisons verify the advantages of the proposed method.

ISAR enables continuous space surveillance irrespective of weather conditions, while the compact polarization (CP) mode balances hardware costs with the provision of polarization information. Recognizing components of space targets provides valuable insights into attitude inversion and the detection of abnormal motion. However, existing ISAR space target recognition methods lack the capability to transition from target-level classification to component-level recognition. Additionally, components with weak and non-uniform scattering intensity distributions pose challenges to their precise positioning and classification in ISAR images. To address these limitations, a coarse-to-fine dual-branch correlation aggregation graph attention network (DCA-Net) is proposed, featuring a novel graph neural network construction strategy. Considering target sparsity, a multi-channel graph node filter (MGNF) module combining compact polarimetric features is devised to enhance computation efficiency. Subsequently, a dual-branch correlation aggregation graph (DCAG) module is constructed concerning the local correlation and global topology. Information suppressed by the non-maximum suppression (NMS) algorithm is reutilized to construct a local graph, which is then aggregated to improve the recognition probability of weak scattering intensity components. Meanwhile, a global graph is constructed, allowing the utilization of structural topology relationships for robust inference in heterogeneous scattering scenarios. Experimental results on ISAR dataset demonstrate that the proposed method achieves superior performance, with at least a 5.38% improvement in the F1 score index and 5.21% improvement in the mean average precision (mAP) index.

Urban damage investigation is an important application for polarimetric synthetic aperture radar (SAR), which is capable of sensing the target scattering mechanism changes before and after a natural disaster. Quad-pol SAR with fully polarimetric acquisition capability can better sense the scattering mechanism changes. Meanwhile, dual-pol SAR with wider swath is suitable for large area monitoring. In this vein, this work dedicates to generating pseudo quad-pol SAR data from dual-pol SAR mode to partially reconstruct fully polarimetric information. The main contributions contain two aspects. Firstly, a multi-scale feature aggregation convolutional neural network (CNN) has been proposed to reconstruct quad-pol SAR data, which includes a feature extraction (FE) module to collect multi-scale features from dual-pol SAR data in spatial and polarimetric domain, and a feature translation (FT) network aggregated with attention modules to deeply fuse the stacked multi-scale features and map them to quad-pol SAR covariance matrices. Then, a urban damage level estimation approach has been established with reconstructed quad-pol SAR data based on polarimetric coherence pattern interpretation tool. Experimental studies have been carried out in terms of both pseudo quad-pol SAR data reconstruction and urban damage level estimation. Comparison results demonstrate that the proposed method achieves better quad-pol SAR data reconstruction accuracy and urban damage level estimation accuracy. Moreover, compared with the urban damage level estimated by real quad-pol SAR data, the proposed method can achieve 99.83% estimation consistency within 5% error tolerance.

Speckle reduction is a pre-processing for synthetic aperture radar (SAR) image interpretation and application. With the advances of convolutional neural network (CNN) models, excellent speckle filters have been continuously developed. However, supervised learning based models suffer from a generalization deficiency due to the lack of clean SAR images. Additionally, other self-supervised learning based methods rely on multiple independent SAR images of the same scene to generate the filtered SAR image. In practice, these additional auxiliary datasets are not always available, which limits the application of these methods. To fulfill this gap, a novel self-supervised framework named Sublook2Sublook is proposed for single SAR images speckle filtering. The main contribution of this work lies in that a new theorem is founded which guarantee the cost function defined on the paired sublook SAR images is statistically equivalent to the supervised counterpart based on the speckled-clean SAR image pairs. Thereby, the sublook SAR images can be alternatively used for model training instead of the clean SAR images or additional auxiliary datasets. From this fashion, a complete self-supervised speckle filter is developed. Firstly, sublook decomposition is performed in both azimuth and range directions. Then, optimal paired sublook images are selected based on a criteria of minimum L1 norm distance. Finally, the established self-supervised speckle filter can be trained with the paired sublook images. Extensive experimental studies are conducted with various SAR datasets in terms of different frequency bands and spatial resolutions from the Radarsat-2, COSMO-SkyMed, and ALOS-2 SAR satellites. Comparisons studies with four state-of-the-art despeckling methods confirm the superiority of the proposed method. The results demonstrate that the proposed Sublook2Sublook framework can better smooth speckles in homogeneous areas while well preserve image details.

Compared with quad polarization, dual polarization (DP) not only has twice wide-swath of observation but also decreases the synthetic aperture radar (SAR) system energy budget. In this paper, an end-to-end full convolutional neural network is proposed to achieve full polarimetric SAR image reconstruction based on dual polarimetric SAR data. Firstly, the feature extraction (FE) network is utilized to extract the multi-scale features of the dual-pol SAR data. Then, a feature translation (FT) network is proposed to achieve the stacked multi-scale features fusion and the quad-pol SAR image space mapping. The weighted cross-entropy loss function is designed to resolve the unbalanced reconstruction of different polarimetric channels. The measured ALOS/PALSAR data is utilized to validate the superiority of the proposed method.

Compared with the optical imaging system, polarimetric inverse synthetic aperture radar (ISAR) can work all-day and all-weather, which plays an important role in space surveillance. However, high-resolution (HR) ISAR images usually require large bandwidth and coherent integration angle, which is limited by the equipment’s physical conditions. In this vein, the super-resolution (SR) of ISAR images is of vital importance. At present, supervised learning methods are often used in image SR of computer vision. By constructing low-resolution (LR) and HR data pairs, the neural network can learn the mapping relationship between them. However, the low-frequency information in LR image data is less considered. In addition, to obtain different scales of SR reconstruction results, multiple network training repetitions are usually needed, which consumes time and hardware resources. Based on the idea of implicit neural representation, this paper constructs an implicit neural network representation framework for polarimetric ISAR image SR, which can obtain multiscale SR results through one training. A semi-supervised module is also constructed to make the network have the ability of supervised and unsupervised learning, which is conducive to mine and make better use of LR images. A polarimetric ISAR image SR dataset is constructed for satellite targets while four indexes are adopted for quantitative evaluation in global and local aspects. Experiments demonstrate that the proposed approach achieves better SR performance, where the PSNR index can be increased at least by 0.93dB.

Polarimetric inverse synthetic aperture radar (ISAR) can operate all-day and all-weather, making it crucial for space surveillance. The compact polarimetric mode balances hardware complexity and polarimetric information, which is commonly equipped with ISAR systems. Given the constraints of limited physical conditions, exploring ISAR image super-resolution is worthwhile. Currently, deep learning models have been employed for enhancing ISAR image super-resolution. However, the super-resolution performance is limited by local interpolation and the occurrence of artifacts. To address these limitations, this work presents a Non-Local Scene Awareness Network (NLSAN), which incorporates a non-local interpolation approach to capture global textures. Furthermore, a scene awareness scheme is established by integrating semantic and super-resolution information, concerning the varying levels of artifacts in different regions. The training process can be regulated by a designed penalty function to mitigate potentially generated artifacts. A dataset of compact polarimetric ISAR images of satellite targets is constructed for comparison analysis. The proposed NLSAN method yields more elaborate super-resolution results with fewer artifacts. Quantitative evaluations are also carried out using global and local indexes such as the Peak-Signal-to-Noise (PSNR), the image entropy, and the 3dB width of strong scatters. Compared with the typical state-of-the-art methods, the proposed approach achieves superior super-resolution performance, with an overall performance improvement of at least 9.2% and enhanced generalization capabilities.

Ship monitoring is an important application of synthetic aperture radar (SAR). The constant false alarm rate (CFAR) methods are commonly used for ship detection. However, CFAR detectors usually face challenges for inshore dense ship detection. Due to the significant mixture of ship candidates and sea clutters within the clutter window, the detection threshold may be overestimated leading to many missed detections. To mitigate this issue, a superpixel-level CFAR detector is proposed. The main contribution contains two aspects. First, a labeling procedure is established for pure clutter superpixels and mixture superpixels discrimination in terms of unsupervised clustering. Second, a nonlocal topology strategy is proposed to adaptively determine a sufficient number of pure clutter superpixels for detection threshold estimation. In this vein, an adaptive superpixel-level CFAR approach is constructed and validated with Radarsat-2, Sentinel-1, and AIRSARShip-1 data sets. Comparison studies demonstrate the superiority of the proposed method. Compared with a traditional CFAR detector and two recent superpixel methods, the proposed method achieves clearly better performance for inshore dense ship regions.

Man-made target recognition is of great significance for many applications within microwave remote sensing. The scattering diversity of various man-made target structures makes radar target identification a difficult task. This work aims at mitigating this issue by mining and utilization of man-made target scattering diversity in polarimetric rotation domain with the interpretation tool of polarimetric correlation pattern. The optimal polarimetric roll-invariant feature set is collected from polarimetric correlation pattern. Then, a polarimetric roll-invariant feature coding scheme is developed for man-made target structure recognition. Moreover, polarimetric radar measurement errors in terms of channel coupling and imbalance are also considered. Experimental studies with electromagnetic computation datasets including canonical structures and an unmanned aerial vehicle (UAV) target and real spaceborne polarimetric synthetic aperture radar (PolSAR) data of a ship target are carried out. Compared with the Cameron decomposition, the proposed method exhibits better recognition performance and stronger robustness, especially for oriented man-made structures.

Polarimetric radar can acquire complete polarization information and is widely used in many applications. However, target orientation relative to the radar line of sight usually exhibits significant influences on the scattering mechanisms. Recently, such target scattering diversity has been successfully characterized and utilized with the polarimetric rotation domain interpretation techniques. In radar polarimetry, target scattering responses are affected by both polarization orientation angle and polarization ellipticity angle. In this vein, this work aims at exploring and utilizing the complete target scattering diversity by extending polarimetric rotation domain techniques to the polarization ellipticity angle dimension. The main idea is to develop a three-dimension polarimetric correlation pattern (3-D PCP) interpretation tool, which can visualize and exhibit targets’ polarimetric rotation domain properties in terms of both the polarimetric orientation and ellipticity angles. Then, a set of global, local, and mutual polarimetric features are proposed to characterize the responses of a 3-D PCP interpretation tool. Especially, the curvatures in differential geometry are first introduced to describe the properties of the 3-D surface. The performance of these new polarimetric features is investigated with spaceborne polarimetric synthetic aperture radar (PolSAR) data. Experimental results demonstrate the advantage of the proposed 3-D PCP features in enhancing the target clutter ratio (TCR), especially for the weak ship area. Based on this, a non-local superpixel-level contrast measure (NSLCM) method for ship detection is proposed. Pure sea samples can be determined adaptively for salient map construction. Comparison results demonstrate better detection performance for both the inshore dense ship area and weak ship area.

The interpretation and recognition of man-made target is an important application of polarimetric radar. The scattering diversity of radar target contains rich information. The polarization interpretation theory in the rotation domain has been proposed to mine and characterize target hidden information. Recently, this interpretation technique has received plenty of attention and achieved successful applications. Based on the study, this work further utilizes the polarimetric correlation pattern interpretation tool for man-made target characterization and recognition. Its potential and advantages are verified by using canonical structures, the Slicy model with electromagnetic computation data and ship targets with polarimetric synthetic aperture radar (PolSAR) data. The experimental studies show that polarimetric correlation pattern has great potential for man-made targets recognition.