Inverse synthetic aperture radar (ISAR) image quality is significantly affected by the structural characteristics of the observed target, the accuracy of motion compensation and the sparsity of echoes. Because these factors interact in a complicated manner, a standardized and objective method for ISAR image quality evaluation remains underdeveloped. To address this challenge, we propose an intelligent evaluation method using a two-stream attention network to automatically grade image quality without reference images. The network comprises two branches: an image stream with a high-low frequency attention module that captures global perceptual cues, and a saliency stream with a multi-scale attention module leveraging saliency priors to extract local features from multiple scales in both spatial and channel dimensions. A weighted loss function allows the saliency stream to guide and refine the image stream’s learning process. For network training, a dataset comprising ISAR images with varying structural characteristics and quality levels derived from differences in noise, motion compensation accuracy, and echo sparsity is constructed. Experimental validation on simulated electromagnetic data confirms the effectiveness of the proposed method.

Inverse synthetic aperture radar (ISAR) and optical image fusion aims to generate a composite image that simultaneously emphasizes the prominent contours of spacecraft from optical images and preserves the rich texture information inherent in ISAR images. However, the limited receptive fields of spatial-domain methods restrict their ability to capture global contextual dependencies among strong scattering points in ISAR images and to effectively integrate complementary optical features. To tackle this challenge, we propose a phase-guided cross-frequency integration module (PGCFIM), which exploits the intrinsic global modeling capability of the frequency domain and the semantic expressiveness of the phase spectrum. Specifically, a deep Fourier transform is employed to establish an image-wide receptive field for intra-domain global modeling. Subsequently, phase components are explicitly aggregated, and a gating mechanism is introduced to guide the integration of inter-domain long-range dependencies, enabling effective learning of complementary cross-modal representations. To eliminate reliance on hand-crafted fusion strategies, we design an end-to-end network, named PGCFINet. By jointly enhancing cross-domain interaction, frequency-domain global awareness, and explicit complementary feature integration, PGCFINet significantly strengthens cross-domain and cross-modal information interaction representation. Furthermore, to mitigate the current lack of ISAR and optical image datasets, we construct a new dataset comprising various spacecraft models, offering an alternative benchmark for evaluation. Extensive experiments demonstrate show that PGCFINet achieves superior performance than state-of-the-art methods in both qualitative and quantitative assessments. Moreover, PGCFINet is extended to infrared and visible image fusion, and the favorable results further validate its robust generalization ability. The codes of our fusion method and the dataset are forthcoming at https://github.com/WangZe0622/PGCFINet.

Recently, road graph extraction has garnered increasing attention due to its crucial role in autonomous driving, navigation, etc. However, accurately and efficiently extracting road graphs remains a persistent challenge, primarily due to the severe scarcity of labeled data. To address this limitation, we collect a global-scale satellite road graph extraction dataset, i.e. Global-Scale dataset. Specifically, the Global-Scale dataset is ∼ 20× larger than the largest existing public road extraction dataset and spans over 13,800 km2globally. Additionally, we develop a novel road graph extraction model, i.e. SAM-Road++, which adopts a node-guided resampling method to alleviate the mismatch issue between training and inference in SAM-Road [17], a pioneering state-of-the-art road graph extraction model. Furthermore, we propose a simple yet effective "extended-line" strategy in SAM-Road++ to mitigate the occlusion issue on the road. Extensive experiments demonstrate the validity of the collected Global-Scale dataset and the proposed SAM-Road++ method, particularly highlighting its superior predictive power in unseen regions. The dataset and code are available at https://github.com/earth-insights/samroadplus.

Because of the constraints of observation conditions, it is difficult to obtain a large amount of measured data for real targets in the inverse synthetic aperture radar (ISAR) system. Existing deep networks usually use the simulated data of random points for training, which will lead to the degradation of the imaging performance of measured data when the distribution of measured data is different from that of simulated data, i.e., poor generalization performance. A high-resolution ISAR imaging method based on Swin Transformer-based deep reinforcement learning (SwinRL) is proposed to address this problem. The 2-D alternating direction method of multipliers (ADMM) is modeled as a sequential decision problem in this method. The internal adjustable parameters are modeled as actions, and the Swin Transformer is used as the backbone network of the policy network and value network. The optimization of the actions, i.e., the internal adjustable parameters of the 2-D ADMM algorithm, is then guided through network training in a reinforcement learning framework. After that, the trained agent can automatically give optimal internal parameters according to different input data, and then well-focused imaging results can be obtained by executing a 2-D ADMM algorithm with optimal parameters. Finally, experimental results based on simulated and measured data show the performance priority of the proposed method compared to existing deep unrolling networks with fixed parameters.

The models currently used for Polarimetric Synthetic Aperture Radar (PolSAR) image classification tasks have problems such as complex network structures, poor distinction of detailed features, and fixed loss weights during the training process. In response to these problems, this paper proposes a PolSAR image classification method based on knowledge distillation using adaptive learning and channel amplification features. Firstly, this paper builds a knowledge distillation framework for PolSAR. Using a teacher network trained in advance that can acquire global knowledge to guide the student. This framework reduces the computational complexity and improves the classification accuracy of the student. Then, an adaptive loss weight learning mechanism is designed, which sets the weight of the Kullback-Leibler divergence loss during training into a learnable mode. The weight can be automatically adjusted according to the actual training situation of the student. Finally, a scheme for channel amplification to enhance features is proposed. This scheme obtains channel weights based on the student’s feature map information. These weights are amplified, strengthening the network’s ability to obtain feature information. Compared with the five PolSAR image classification algorithms, the method proposed in this paper uses lower computational complexity to obtain higher classification accuracy on the Flevoland, San Francisco, and Xi’an datasets.

Inverse synthetic aperture radar (ISAR) images and optical images exhibit a certain degree of complementarity due to their imaging in different microwave frequency bands. To enhance the representation capability of spacecraft structures and details, we propose a novel ISAR-and-optical image fusion method based on the features of multisubbands. First, given the different imaging planes of spacecraft in ISAR and optical images, we propose an ISAR-and-optical image registration method that combines keypoint detection and homography transformation, based on the obvious geometric features of the spacecraft. Second, the multiscale decomposition of the source images is achieved by the nonsubsampled shearlet transform (NSST). Subsequently, we propose a novel activity level measurement function based on brightness, contours, and textures to achieve the fusion of low-pass subbands. Simultaneously, the texture of high-pass subbands is effectively fused based on the parameter-adaptive dual-channel pulse-coupled neural network (PADCPCNN). Finally, the fused image is obtained by the inverse-NSST. Compared to the existing state-of-the-art fusion methods, the proposed method has a better performance in both qualitative and quantitative evaluations across multiple imaging instants for different satellite models.

Scatterer trajectory association is a critical step of 3-D target reconstruction from the inverse synthetic aperture radar (ISAR) image sequence. To cope with the complex scatterer trajectory association of the noncooperative target, a novel method based on multiple hypothesis tracking (MHT) algorithm is proposed. First, the scatterer trajectory association situation is modeled as a multiple hypothesis tree, in which each branch represents a possible association. Then, to generate the hypothesis in each specific branch, a general trajectory motion model is constructed and the parameters are estimated based on the current trajectory association situation. The parameter estimation precision will increase with the growth of the trajectory length. Besides, to eliminate the influence of inaccurate association initialization and direction selection, a fusion algorithm is proposed to merge the forward and backward associated trajectories. Finally, experimental results based on the simulated data and electromagnetic data verify the effectiveness and robustness of the proposed method.

Estimation of the instantaneous attitude of spacecraft plays significant roles in space situation awareness activities, such as on-orbit status monitoring and collision warning. With the rapid development of both optical sensors and inverse synthetic aperture radar (ISAR), it becomes possible to achieve accurate instantaneous attitude estimation of spacecraft by multistatic optical-and-ISAR joint observation. In view of this, this article proposes a novel spacecraft attitude estimation method based on a joint optical-and-ISAR observation system, which includes one optical sensor and two ISARs. Specifically, the proposed method first estimates the orientation and the length of typical components utilizing optical and ISAR images with the same observation instant. Then, it solves the target instantaneous rotation vector from the orientation, length, and Doppler of typical components, and finally, it obtains the instantaneous attitude of the spacecraft. Experimental results have verified the effectiveness of the proposed method.

By analyzing the motion characteristics and the radar observation model of triaxial stabilized space targets, a new 3-D geometry reconstruction method is proposed based on the energy accumulation of inverse synthetic aperture radar (ISAR) image sequence. According to the radar line of sight (LOS), we first construct the projection vectors of the 3-D geometry of a space target on the imaging planes. Then, by projecting the 3-D scatterer candidates on each imaging plane, we can accumulate the scattering energy of the corresponding 2-D projection position in each image. The 3-D scatterer candidates occupying the larger accumulated energy will be reserved as the real scatterers. To improve the efficiency, the real 3-D scatterers will be searched by using the particle swarm optimization (PSO) algorithm one by one. Compared with traditional 3-D geometry reconstruction methods, the proposed one never needs the 2-D scatterer extraction and trajectory association, which remains the challenges in ISAR image processing. Experimental results based on the simulated point target and electromagnetic data are presented to validate the effectiveness and robustness of the proposed method.

Radar echoes simulation has played a significant role in human detection and classification in the scenarios, e.g., antiterrorism, rescue after a disaster and medical, where the real-measured data are generally unavailable and limited. Therefore, a novel radar echoes simulation method of human movements is proposed based on motion capture (MOCAP) and electromagnetic (EM) calculation. First, we generate the trajectories of body segments from the true shape and MOCAP data of a human body. On the basis of that, the radar echoes are simulated by calculating the EM scattering characteristics, i.e., radar cross sections (RCSs) of all the gestures of each body segment's trajectory. Meanwhile, the micro-Doppler characteristics induced by the micromotion of human body segments are modulated in simulated radar echoes. Finally, comparisons between the simulated radar echoes and measured ones prove the validity of the proposed method. Some refinement for RCS calculation of the human body will be investigated in our future work.

Space debris damage orbiting spacecraft and astronauts and ISAR imaging is an important method to recognize and classify debris. Compared with 2-D imaging, 3-D imaging is able to provide more information. However, debris with rapid spinning have great migration through range-cells, so common methods are unproductive. A novel method of ISAR 3-D imaging based on nonparametric Bayesian model is proposed aimed at debris with spinning. Firstly, a motion model and a signal model are proposed. Secondly, PSO algorithm is utilized to preprocess the data and obtain the height of target. Finally, Nonparametric Bayesian model is imposed to elaborately reconstruct the target in range and cross-range. For monostatic radar, point-target simulation data and electromagnetism data confirm that the method will obtain refined 3-D imaging results. Meanwhile, this method is capable to surmount the obstacle of Doppler aliasing and data missing caused by rapid spinning.

To improve the performance of expectation maximization (EM) in retrieving the missing data of inverse synthetic aperture radar (ISAR) scatterer trajectory matrix, a novel initialization method is proposed. Firstly, we derive the ellipse motion dynamics of the projected scatterer trajectory. Then, based on estimated ellipse parameters using known data of each scatterer trajectory, we propose the bidirectional Kalman filter to initialize the missing data. Finally, EM algorithm is applied to estimate the missing data and factorization method is performed on the complete trajectory matrix to obtain the three-dimensional geometry of scatterers. Experimental results using simulated data verify the effectiveness of the proposed initialization method.

As tracked vehicles play significant roles in a battlefield, effective jamming measures is necessary to protect them from being perceived by hostile radar. Moving tracked vehicles exhibit strong Doppler and micro-Doppler signatures. Therefore, the jamming signal should include micro-Doppler modulation generated by metallic caterpillars for successful deception jamming. Based on detailed analysis of kinetic characteristics of tracked vehicles, this paper proposes a new deception jamming method for tracked vehicles against continuous-wave ground surveillance radar. To obtain precise deception jamming effect, this method achieves both translational modulation for rigid parts and micro-Doppler modulation for the caterpillars. Finally, simulation results have proven the effectiveness of the proposed method.

The anisotropy of radar cross section of scatterers makes the scatterer trajectory matrix incomplete in sequential inverse synthetic aperture radar images. As a result, factorization methods cannot be directly applied to reconstruct the 3-D geometry of scatterers without additional consideration. We propose a modified expectation-maximization (EM) algorithm to retrieve the complete scatterer trajectory matrix. First, we derive the motion dynamics of the projected scatterer, which approximates an ellipse. Then, based on the estimated ellipse parameters using the known data of each scatterer trajectory, we use the Kalman filter to initialize the missing data. To address the limitations of a traditional EM, which only considers the rank-deficient characteristics of the scatterer trajectory matrix, we propose to augment EM by using both the known rank-deficient and elliptical motion characteristics. Experimental results on simulated data verify the effectiveness of the proposed method.

Traditionally, the factorization method is applied to reconstruct the 3D geometry of a target from its sequential inverse synthetic aperture radar images. However, this method requires performing cross-range scaling to all the sub-images and thus has a large computational burden. To tackle this problem, this paper proposes a novel method for joint cross-range scaling and 3D geometry reconstruction of steadily moving targets. In this method, we model the equivalent rotational angular velocity (RAV) by a linear polynomial with time, and set its coefficients randomly to perform sub-image cross-range scaling. Then, we generate the initial trajectory matrix of the scattering centers, and solve the 3D geometry and projection vectors by the factorization method with relaxed constraints. After that, the coefficients of the polynomial are estimated from the projection vectors to obtain the RAV. Finally, the trajectory matrix is re-scaled using the estimated rotational angle, and accurate 3D geometry is reconstructed. The two major steps, i.e., the cross-range scaling and the factorization, are performed repeatedly to achieve precise 3D geometry reconstruction. Simulation results have proved the effectiveness and robustness of the proposed method.