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.

With traditional three-dimensional (3-D) reconstruction methods for space targets, it is difficult to achieve 3-D structure and attitude reconstruction simultaneously. To tackle this problem, a 3-D reconstruction method for space targets is proposed, and the alignment and fusion of optical and ISAR images are investigated. Firstly, multiple pairs of optical and ISAR images are acquired in the joint optical-and-ISAR co-location observation system (COS). Then, key points of space targets on the images are used to solve for the Doppler information and the 3-D attitude. Meanwhile, the image offsets of each pair are further aligned based on Doppler co-projection between optical and ISAR images. The 3-D rotational offset relationship and the 3-D translational offset relationship are next deduced to align the spatial offset between pairs of images based on attitude changes in neighboring frames. Finally, a voxel trimming mechanism based on growth learning (VTM-GL) is designed to obtain the reserved voxels where mask features are used. Experimental results verify the effectiveness and robustness of the OC-V3R-OI method.

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 features of multi-sub-bands. Firstly, given the different imaging planes of spacecraft in ISAR and optical images, we propose an ISAR-and-optical image registration method that combines key-point detection and homography transformation, based on the obvious geometric features of the spacecraft. Secondly, the multiscale decomposition of the source images is achieved by the non-subsampled 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 sub-bands. Simultaneously, the texture of high-pass sub-bands 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 better performance in both qualitative and quantitative evaluations across multiple imaging instants for different satellite models.

Complex-valued convolutional neural networks (CVCNNs) have demonstrated strong capabilities for polarimetric synthetic aperture radar (PolSAR) image classification by effectively integrating both amplitude and phase information inherent in polarimetric data. However, their practical deployment faces significant challenges due to high computational costs and performance degradation caused by extremely limited labeled samples. To address these challenges, a lightweight CV Siamese network (LCVSNet) is proposed for few-shot PolSAR image classification. Considering the constraints of limited hardware resources in practical applications, simple one-dimensional (1D) CV convolutions along the scattering dimension are combined with two-dimensional (2D) lightweight CV convolutions. In this way, the inter-element dependencies of polarimetric coherency matrix and the spatial correlations between neighboring units can be captured effectively, while simultaneously reducing computational costs. Furthermore, LCVSNet incorporates a contrastive learning (CL) projection head to explicitly optimize the feature space. This optimization can effectively enhance the feature discriminability, leading to accurate classification with a limited number of labeled samples. Experiments on three real PolSAR datasets demonstrate the effectiveness and practical utility of LCVSNet for PolSAR image classification with a small number of labeled samples.

Inverse synthetic aperture radar (ISAR) is extensively used in space situational awareness, with its imaging resolution improving as signal bandwidth increases. However, high-order migration through range cell (MTRC) and phase errors due to target rotation can degrade image focus. Traditional algorithms model rotation as a polynomial to compensate for these errors, but this approach becomes computationally intensive and less accurate with higher bandwidths. Additionally, residual errors from previous compensation steps can accumulate, further reducing overall accuracy. In contrast, polar interpolation with actual rotation parameters can effectively compensate for MTRC and phase errors across all orders without the drawbacks of sequential compensation. Motivated by this point, this paper proposes a modified polar format algorithm (PFA) that establishes a link between ISAR imaging quality and effective rotation parameters. By applying the minimum entropy criterion, the algorithm iteratively refines and optimizes the estimation of these parameters, compensating for all MTRC and phase errors in a single step, thus ensuring high overall compensation accuracy. With the optimized parameters, the algorithm also achieves precise cross-range scaling in imaging results. Extensive experiments verify the effectiveness and superiority of this proposed algorithm compared with other existing methods.

Multi-view inverse synthetic aperture radar (ISAR) image sequences provide multi-dimensional observation information about space targets, enabling precise attitude estimation that is fundamental to both non-cooperative target monitoring and critical space operations including active debris removal and space collision avoidance. However, directly utilizing all images within an ISAR sequence for attitude estimation can result in a substantial data preprocessing workload and reduced algorithm efficiency. Given the inherent overlap and redundancy in the target information provided by these ISAR images, this paper proposes a novel space target attitude estimation method based on the selection of multi-view ISAR image sequences. The proposed method begins by establishing an ISAR imaging projection model, then characterizing the target information differences through variations in imaging plane normal, and proposing an image selection method based on the uniform sampling across elevation and azimuth angles of the imaging plane normal. On this basis, the method utilizes a high-resolution network (HRNet) to extract the feature points of typical components of the space target. This method enables simultaneous feature point extraction and matching association within ISAR images. The attitude estimation problem is subsequently modeled as an unconstrained optimization problem. Finally, the particle swarm optimization (PSO) algorithm is employed to solve this optimization problem, thereby achieving accurate attitude estimation of the space target. Experimental results demonstrate that the proposed methodology effectively filters image data, significantly reducing the number of images required while maintaining high attitude estimation accuracy. The method provides a more informative sequence than conventional selection strategies, and the tailored HRNet + PSO estimator resists performance degradation in sparse-data conditions, thereby ensuring robust overall performance.