Physical parameter estimation of satellite is crucial in space situation awareness as it reflects valuable information. Polarimetric inverse synthetic aperture radar (Pol-ISAR) is a powerful sensor for space surveillance, providing rich information for satellite physical parameter estimation. Parabolic antennas, which are widely loaded in remote sensing and communication satellites, have received great attention recently. Dedicated to the space situation awareness issue using Pol-ISAR, a physical parameter joint estimation method of satellite parabolic antenna with key frame Pol-ISAR images is developed in this work. The core idea is to utilize the mapping relationship between parabolic antenna in 3-D space and its projection ellipse in 2-D ISAR image. Under special observation geometry, the closed-form expressions of parabolic antenna physical parameters are deduced for the first time, providing an efficient way for parameter estimation. Moreover, the abundant information within Pol-ISAR images is mined and utilized. Various polarimetric features are adopted for ellipse extraction and the subsequent physical parameter estimation. Compared with single-polarization channel data, the superiorities of polarimetric feature are validated using electromagnetic simulation data.

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.

Attitude estimation of space targets can reveal crucial details about payload orientation, movement intentions, and observation area, all of which are vital in space situational awareness. Till now, inverse synthetic aperture radar (ISAR) has become a mainstream sensor for space target observation, providing rich information for space target attitude estimation. Based on projection matrix and linear structure extracted from ISAR images, a space target attitude estimation method is proposed in this work. The main contribution falls on two parts. On the one hand, linear structure is derived based on the peak accumulation values of original ISAR images rather than binary images. On the other hand, the space target attitude information is effectively estimated based on the projection matrix theory and linear structure extraction results. Experimental studies with measured and simulated data demonstrate the effectiveness of the proposed method.

VideoSAR realizes continuously scene observation and expands SAR's range-azimuth two-dimensional scattering information to range-azimuth-time three-dimensional scattering information. Considering the effects of speckle noise, a novel speckle filter for VideoSAR image was proposed in this paper using the three-dimensional information of VideoSAR image. The proposed method mainly contains three steps. Firstly, a context covariance matrix that contains spatial and temporal context information is constructed for each pixel. Then, the similar samples are selected for each pixel according to the similarity test of covariance matrices. Finally, a sample averaging estimator is applied based on the similar samples. Experiments on real VideoSAR data show that the proposed algorithm achieves effective speckle filtering performance while well preserving the edge texture.

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.

For polarimetric synthetic aperture radar (PolSAR) data, the correlation of two polarization channels is sensitive to the target orientation relative to the sensor's illumination direction. In this letter, the concept of polarimetric correlation pattern is proposed to explore this scattering diversity. The core idea is to extend the polarimetric correlation from a fixed angle to the rotation domain along the radar line of sight. A set of new polarimetric features are derived, and three features with high target-to-clutter ratio (TCR) are selected for PolSAR ship detection. The proposed ship detection method mainly contains three steps: the features selection, thresholding procedure, and morphological filtering. Experimental studies with Radarsat-2 and GaoFen-3 data validate the advantage of the proposed approach, especially for inshore dense ship discrimination.

Ship detection is one of important applications for synthetic aperture radar (SAR). Speckle effects usually make SAR image understanding difficult and speckle reduction becomes a necessary pre-processing step for majority SAR applications. This work examines different speckle reduction methods on SAR ship detection performances. It is found out that the influences of different speckle filters are significant which can be positive or negative. However, how to select a suitable combination of speckle filters and ship detectors is lack of theoretical basis and is also data-orientated. To overcome this limitation, a speckle-free SAR ship detection approach is proposed. A similar pixel number (SPN) indicator which can effectively identify salient target is derived, during the similar pixel selection procedure with the context covariance matrix (CCM) similarity test. The underlying principle lies in that ship and sea clutter candidates show different properties of homogeneity within a moving window and the SPN indicator can clearly reflect their differences. The sensitivity and efficiency of the SPN indicator is examined and demonstrated. Then, a speckle-free SAR ship detection approach is established based on the SPN indicator. The detection flowchart is also given. Experimental and comparison studies are carried out with three kinds of spaceborne SAR datasets in terms of different polarizations. The proposed method achieves the best SAR ship detection performances with the highest figures of merits (FoM) of 97.14%, 90.32% and 93.75% for the used Radarsat-2, GaoFen-3 and Sentinel-1 datasets, accordingly.

Speckle reduction and target detection are usually two independent and successive procedures in SAR information processing systems. The separated implementation scheme makes the determination of a suitable combination of speckle filter and target detector a challenging task in practice. This work attempts to propose a novel integrated approach for both SAR speckle reduction and target detection. Firstly, a new representation in terms of the context scattering vector and context covariance matrix is established for information augmentation and mining for SAR data. Then, similar pixels within a large moving window are selected using matrix similarity test and the similar pixel number (SPN) is recorded. The speckle reduction can be conducted with the determined similar samples and salient targets (e.g. manmade targets) can be detected with the SPN index simultaneously. Finally, the speckle reduction and ship detection integrated processing procedure is established. Experimental studies are carried out with space-borne SAR datasets. The proposed integrated framework shows superiority in both speckle reduction and target detection performances.

Ship detection is an important application in polarimetric synthetic aperture radar (PolSAR) image. A novel saliency detector for PolSAR ship detection has been proposed in this work, which considers ship targets as salient candidates from sea clutter in low and medium sea conditions. Firstly, the similarity test based on polarimetric covariance matrix is applied on each pixel within its neighborhood. Then, saliency feature named Similar Pixel Number (SPN) is generated by calculating the similar samples within its neighborhood. Finally, ship targets can be obtained through proper thresholding procedure and morphological filtering. Experimental studies with two Radarsat-2 datasets validate the advantages of the proposed method.