Polarimetric inverse synthetic aperture radar (ISAR) offers continuous, all-weather space surveillance capabilities. Identifying satellite components can be beneficial for monitoring their operational status and health. Nevertheless, target orientation relative to the radar line of sight usually exhibits significant influences on the scattering mechanisms. Additionally, ISAR projection introduces orientational characteristics to satellite components in polarimetric ISAR images, which poses challenges for their precise localization and identification. Recently, this diversity in target scattering has been successfully characterized and employed using the three-dimension polarimetric correlation pattern (3-D PCP) interpretation technique, enabling the differentiation of various scattering structures. This study analyzes the relationship between satellite polarimetric responses and both polarimetric orientation angle and polarimetric ellipticity angle based on 3-D PCP. Then a hybrid approach combining 3-D PCP and data-driven model is designed for oriented satellite component recognition. In contrast to using a single polarimetric channel as input for deep neural networks, our approach transforms the data domain and utilizes three independent 3-D PCPs to drive the network. On one hand, the network training is guided by manually extracted features derived from 3D-PCP, including statistical characteristics such as mean, standard deviation, extreme values, and contrast. On the other hand, adaptive feature extraction is performed through a simple convolutional structure and integrated with electromagnetic scattering characteristics. Six satellites are utilized for constructing polarimetric ISAR dataset. Experimental results demonstrate that the proposed method achieves a superior performance, evidenced by a 2.3% improvement in the mean average precision (mAP) index.
Polarimetric inverse synthetic aperture radar (ISAR) plays an important role in space surveillance. In comparison to the quad-polarization (quad-pol) mode, the compact polarimetric (CP) mode can balance the hardware costs and polarization information. For satellite observation, the recognition of satellite component can offer valuable insights for attitude inversion and the perception of abnormal motion. Comprehending the interrelationships among components across various categories is expected to enhance the recognition performance and refine the spatial position of detection results. In this vein, a topology graph is constructed, and a component-level attention mechanism is introduced. Consequently, a multi-stage recognition framework is employed for feature enhancement. Experimental results indicate that there is a 5.5% improvement for the mean average precision (mAP) index in two scenarios.
Polarimetric synthetic aperture radar (PolSAR) serves as a crucial instrument in marine remote sensing. Making use of polarimetric information is paramount to enhancing the performance of PolSAR ship detection. The challenge lies in sufficiently utilizing comprehensive polarimetric information to simultaneously achieve superior false alarm suppression and ship detection. In this vein, this work dedicates to this issue and develops a novel PolSAR ship detection approach that employs multi-dimensional information fusion. The main contributions contain two aspects. Firstly, a novel polarization-space-time context covariance matrix (PSTCCM) within a local spatial neighborhood of sublook PolSAR images to characterize the target whole information is developed. This matrix amalgamates multi-dimensional information, including space, time, and polarization, derived from PolSAR data. Secondly, a similar pixel number (SPN) indicator based on PSTCCM that can effectively identify salient targets is further derived for ship detection. The underlying principle is that ships and sea clutter candidates exhibit different properties of homogeneity within a moving window, and the SPN indicator can clearly reflect these differences. The sensitivity and efficiency of the SPN indicator is examined and demonstrated. Comprehensive comparison studies are conducted using GaoFen-3 and Radarsat-2 PolSAR datasets. Quantitative investigations in terms of the figure of merit (FOM) index validate the superiority of the proposed method, especially for inshore false signals discrimination.
Polarimetric inverse synthetic aperture radar (ISAR), with its ability to operate in all conditions, plays an important role in space surveillance. The compact polarimetric mode balances hardware complexity and polarimetric information, commonly equipped with ISAR systems. However, the generation of high-resolution ISAR images usually requires a large bandwidth and coherent integration angle, which is constrained by the equipment’s physical conditions. At present, supervised learning methods are often used for image super-resolution in computer vision. However, super-resolution performance is often hampered by the occurrence of artifacts and the inadequate consideration of low-frequency information in low-resolution image data. To address these limitations, this work presents a semantic information guided semi-supervised deep-learning method. This framework incorporates implicit neural representation to extract and better utilize information from low-resolution ISAR images. In addition, semantic and super-resolution information are integrated to regulate the training process. Datasets comprising images and semantic information of compact polarimetric ISAR for satellite targets are constructed. The proposed method yields more elaborate super-resolution results with fewer artifacts. Quantitative evaluations are also carried out using the Peak-Signal-to-Noise (PSNR) metric. Compared with the typical methods, the proposed approach achieves superior super-resolution performance, with a performance improvement of at least 1.394 dB.
Accurate parameter estimation of space target attitude and size with inverse synthetic aperture radar (ISAR) image is a tough task, which plays an important role in analyzing and monitoring space awareness situation. Key point extraction is one of the crucial procedures for parameter estimation. Cross-polarization ISAR data works well in edge structure reservation. Moreover, considering the characteristics of ISAR image, U-net model, which performs well in sample-background-image segmentation, is more suitable for key point extraction. Therefore, a joint estimation method for space target attitude and size is proposed in this work based on polarimetric ISAR images and modified U-net model. The main contribution falls on two parts. Firstly, key point extraction procedure is conducted with modified U-net model, the architecture and loss function of which are modified according to the characteristics of polarimetric ISAR images. Secondly, the attitude and size parameter of space target are jointly estimated based on the extracted key points and ISAR projection relationship. Compared with comparative method, the superiority of the proposed method is validated using simulated data. Quantitatively, the mean estimation error of attitude parameter is 0.88° and that of size parameter is 0.12%.
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