Active tracking of space noncooperative object that merely relies on vision camera is greatly significant for autonomous rendezvous and debris removal. Considering its Partial Observable Markov Decision Process (POMDP) property, this letter proposes a novel deep recurrent neural network architecture, named as recurrent and attention module based active visual tracking (RAMAVT), incorporating Multi-Head Attention (MHA) module and Squeeze-and-Excitation (SE) layer that remarkably improve the representative ability of neural network with almost no extra computational cost. It has been successfully applied to value-based and policy gradient-based deep reinforcement learning algorithm, and learned to drive the chasing spacecraft to follow arbitrary space noncooperative object with high-frequency and near-optimal velocity control commands. Extensive experiments and robustness evaluations implemented on space non-cooperative object active tracking (SNCOAT) benchmark show the betterment and robustness of our method compared with other state-of-the-art active visual trackers. In addition, we make further ablation study and interpretability research on RAMAVT of which validity and rationality have been demonstrated.

Due to the adverse illumination in space, non-cooperative object perception based on multi-source image fusion is crucial for on-orbit maintenance and orbital debris removal. In this paper, we first propose a dual-branch multi-scale feature extraction encoder combining transformer block and Inception block to extract global and local features of visible and infrared images and establish high-dimensional semantic connections. Secondly, different from the traditional artificial design Fusion strategy, we propose a Feature Fusion Module called Cross-Convolution Feature Fusion (CCFF) Module, which can achieve image feature level fusion. Based on the above, we propose a Dual-branch Fusion Network based on Transformer and Inception (DFTI) for space non-cooperative object, which is an image fusion framework based on auto-encoder architecture and unsupervised learning. The fusion image can simultaneously retain the color texture details and contour energy information of space non-cooperative objects. Finally, we construct a fusion dataset of infrared and visible images for space non-cooperative objects (FIV-SNO) and compare DFTI with seven state-of-the-art methods. In addition, object tracking as a follow-up high-level visual task proves the effectiveness of our method. The experimental results demonstrate that compared with other advanced methods, the Entropy (EN) and average gradient (AG) of the fusing images using DFTI network are increased by 0.11 and 0.06 respectively. Our method exhibits excellent performance in both quantitative measures and qualitative evaluation.

Due to the interference of outer space environment, the real image data of non-cooperative targets collected by a single sensor often has problems such as low contrast and insufficient color information display. In this paper, we propose an image fusion method with iterative wavelet transform, which utilizes the respective advantages of visible image and near-infrared image to improve the clarity of space non-cooperative target and retain color information. The dataset in this paper comes from the satellite model group taken by ourselves. Due to different shooting angles and resolutions, we need to carry out accurate registration of near-infrared images and visible light. Therefore, we also propose an improved registration algorithm based on ORB feature matching. In order to verify the effectiveness of the method, other image fusion methods are selected for comparative analysis. The method proposed can preserve texture features and color information of space non-cooperative targets.

Accurate pose estimation of non-cooperative space objects is crucial for applications such as satellite maintenance, space debris removal, and on-orbit assembly. However, monocular pose estimation methods face significant challenges in environments with limited visibility. Different from the traditional pose estimation methods that use images from a single band as input, we propose a novel deep learning-based pose estimation framework for non-cooperative space objects by fusing visible and infrared images. First, we introduce an image fusion subnetwork that integrates multi-scale features from visible and infrared images into a unified embedding space, preserving the detailed features of visible images and the intensity information of infrared images. Subsequently, we design a robust pose estimation subnetwork that leverages the rich information from the fused images to achieve accurate pose estimation. By combining these two subnetworks, we construct the Visible and Infrared Fused Pose Estimation Framework (VIPE) for non-cooperative space objects. Additionally, we present a Bimodal-Vision Pose Estimation (BVPE) dataset, comprising 3,630 visible-infrared image pairs, to facilitate research in this domain. Extensive experiments on the BVPE dataset demonstrate that VIPE significantly outperforms existing monocular pose estimation methods, particularly in complex space environments, providing more reliable and accurate pose estimation results.

With the continuous advancement of space technology, the number of defunct spacecraft, abandoned rocket bodies, and debris in space is increasing. These non-cooperative objects occupy a significant amount of orbital resources and pose a substantial threat to the safety of on-orbit spacecraft. This paper focuses on close-proximity operations in space and aims to address the limitation of camera resolution by proposing an optical flow-based multi-frame super-resolution reconstruction algorithm. This algorithm employs a multi-level wavelet convolutional network (MWCNN) for feature extraction and uses SpyNet to obtain multi-level optical flow between different frames. The multi-level optical flow pyramid alignment network is used to align features, and a recurrent network is utilized for frame-by-frame feature fusion. Finally, a reconstruction network generates high-resolution images. Extensive experiments have demonstrated that our proposed method effectively enhances the perception capabilities of space non-cooperative objects.