AbstractDeep learning has shown great power in processing remote sensing data, especially for fine-grained remote sensing ship image classification. However, the lack of a large amount of effective training data greatly limits the performance of neural networks. Based on current data augmentation methods, images of ships on the sea generated for remote sensing have the problem of distortion, blurring, and poor diversity. To tackle this problem, we propose a novel progressive remote sensing ship image data augmentation method that combines ship simulation samples and a neural style transfer (NST) based network to generate a large amount of transferred remote sensing ship images. Our method consists of two stages. The first stage uses a visible light imaging simulation system (VLISS) to generate ship simulation samples through 3D models of real images. This stage can significantly increase the diversity of the training dataset. For the second stage, to eliminate the domain gap between real ship images and ship simulation samples, a few real images and a newly designed NST-based network called Sim2RealNet are employed to realize style transfer from simulation samples to real images. The proposed method was applied to a variety of ship targets to verify its effectiveness compared to other data augmentation methods on remote sensing image classification tasks. The experimental results demonstrate the effectiveness of the proposed method. Index TermsDomain gap, image classification, neural style transfer (NST), remote sensing, ship simulation samples.

The detection of dim and small space-targets is crucial in space situational awareness missions; however, low signal-to-noise ratio (SNR) targets and complex backgrounds pose significant challenges to such detection. This paper proposes a space-target detection framework comprising a space-target detection network and a k-means clustering target centroid positioning method. The space-target detection network performs a three-dimensional convolution of an input star image sequence to learn the motion features of the target, reduces the interference of noise using a soft thresholding module, and outputs the target detection result after positioning via the offsetting branch. The k-means centroid positioning method enables further high-precision subpixel-level centroid positioning of the detection network output. Experiments were conducted using simulated data containing various dim and small space-targets, multiple noises, and complex backgrounds; semi-real data with simulated space-targets added to the real star image; and fully real data. Experiments on the simulated data demonstrate the superior detection performance of the proposed method for multiple SNR conditions (particularly with very low false alarm rates), robustness regarding targets of varying numbers and speeds, and complex backgrounds (such as those containing stray light and slow motion). Experiments performed with semi-real and real data both demonstrate the excellent detection performance of the proposed method and its generalization capability.

Infrared (IR) small target detection has been widely used in military and civil fields. However, due to the insufficient feature integration capabilities of existing methods, effectively separating strong background clutter and targets in complex scenes remains difficult. To address this issue, we propose a two-stage feature complementary improved tensor low-rank sparse decomposition (TSFC-TLRSD) method. The detection process is divided into two stages: tensor initialization and tensor decomposition, effectively integrating local and non-local features. In the tensor initialization stage, inspired by the local saliency of the target and the local consistency of the background, we design a three-layer directional filtering (TLDF) operator for preliminary clutter suppression and target enhancement. Then, to promote the complementary advantages of local and non-local features, we refer to the TLDF and the original image to provide a targeted initialization strategy for the tensor low-rank sparse decomposition (TLRSD) model. In the tensor decomposition stage, we develop a robust partial sum of the tubal nuclear norm (RPSTNN) as a non-convex approximation of tensor rank, which can adaptively adjust the singular value distribution, thus adapting to diversity scenes. Meanwhile, we finely adjust the balance between low-rank and sparse components in the model-solving process through a nonlinear reweighting strategy, accelerating the optimization convergence speed and improving the model's background recovery ability. Extensive experiments on five practical datasets demonstrate that the proposed method is more effective and robust compared to ten state-of-the-art approaches.

The evolution of satellite surveillance technology, coupled with advanced on-board intelligent systems and improved attitude maneuver capabilities, has positioned mission scheduling and execution as a prominent and active research area in recent years. With the urgent need for mission scheduling and execution to evolve from the static ground target to the time-sensitive moving target, the existing satellite often yields unsatisfactory results when managed through conventional manual methods for continuously tracking a time-sensitive moving target. In response to this challenge, the paper introduces a reinforcement learning driven method enabling the satellite to schedule and execute the tracking of time-sensitive moving target autonomously. In the paper, we conduct a meticulous analysis of the target tracking process, which we decompose into three distinct sub-phases and address it utilizing attitude control, elucidating the integration of our model with satellite attitude dynamics and kinematics. Besides, we propose Flight Trajectory Prediction Module to predict the future trajectory position and minimize the position error due to the communication latency. With the dual aim of expeditiously tracking the target and concurrently enhancing payload stability, a well-designed reward function, which is applied to various reinforcement learning (RL) algorithms, effectively governs satellite attitude control to achieve accurate and stable tracking of moving target. In addition, through the eight distinct tracking scenarios simulated in our proposed Satellite Target Tracking Environment (STTE), our extensive experiments and ablation studies consistently underscore the advantages and efficacy of our proposed method. This work serves as a foundational cornerstone for future research endeavors focused on tracking multiple time-sensitive moving targets within extensive and complex intelligent satellite constellation.

Effective detection and tracking of dim and small targets with low signal-to-clutter ratio (SCR) has become a research hotspot due to its wide range of applications. However, most of the previously proposed methods seldom utilize the abundant temporal features formed by target motion, resulting in poor detection and tracking performance for low SCR targets. In this article, we analyze the difficulty based on spatial features and the feasibility based on temporal features of realizing effective detection. According to this analysis, we use a multi-frame as a detection unit and propose a detection method based on temporal energy selective scaling (TESS). Specifically, we investigated the composition of intensity temporal profiles (ITPs) formed by pixels on a multi-frame detection unit. For the target-present pixel, the target passing through the pixel will bring a weak transient disturbance on the ITP and introduce a change in the statistical properties of ITP. We use a well-designed function to amplify the transient disturbance, suppress the background and noise components, and output the trajectory of the target. Subsequently, to solve the contradiction between the detection rate and the false alarm rate brought by the traditional threshold segmentation, we associate the temporal and spatial features of the trajectory and propose a trajectory extraction method based on the 3D Hough transform. Finally, we propose a trajectory segments-based multi-target tracking method. Compared with the various state-of-the-art detection and tracking methods, experiments in multiple scenarios prove the superiority of our proposed methods for dim and small targets in low SCR.