Collaborative Static-Dynamic Teaching: A Semi-Supervised Framework for Stripe-Like Space Target Detection

Stripe-like space target detection (SSTD) is crucial for space situational awareness. Traditional unsupervised methods often fail in low signal-to-noise ratio and variable stripe-like space targets scenarios, leading to weak generalization. Although fully supervised learning methods improve model generalization, they require extensive pixel-level labels for training. In the SSTD task, manually creating these labels is often inaccurate and labor-intensive. Semi-supervised learning (SSL) methods reduce the need for these labels and enhance model generalizability, but their performance is limited by pseudo-label quality. To address this, we introduce an innovative Collaborative Static-Dynamic Teacher (CSDT) SSL framework, which includes static and dynamic teacher models as well as a student model. This framework employs a customized adaptive pseudo-labeling (APL) strategy, transitioning from initial static teaching to adaptive collaborative teaching, guiding the student model's training. The exponential moving average (EMA) mechanism further enhances this process by feeding new stripe-like knowledge back to the dynamic teacher model through the student model, creating a positive feedback loop that continuously enhances the quality of pseudo-labels. Moreover, we present MSSA-Net, a novel SSTD network featuring a multi-scale dual-path convolution (MDPC) block and a feature map weighted attention (FMWA) block, designed to extract diverse stripe-like features within the CSDT SSL training framework. Extensive experiments verify the state-of-the-art performance of our framework on the AstroStripeSet and various ground-based and space-based real-world datasets.

One Shot is Enough for Sequential Infrared Small Target Segmentation

Infrared small target sequences exhibit strong similarities between frames and contain rich contextual information, which motivates us to achieve sequential infrared small target segmentation with minimal data. Inspired by the success of large segmentation models led by Segment Anything Model (SAM) across various downstream tasks, we propose a one-shot and training-free method that perfectly adapts SAM's zero-shot generalization capabilities to sequential infrared small target segmentation. Given one annotated frame as a reference, our method can accurately segment small targets in other frames of the sequence. Specifically, we first obtain a confidence map through local feature matching between reference image and test image. Then, the highest point in the confidence map is as a prompt, and we design the Point Prompt-Centric Focusing (PPCF) module to address the over-segmentation of small targets with blurry boundaries. Subsequently, to prevent miss and false detections, we introduce the Triple-Level Ensemble (TLE) module that ensembles the masks obtained at different levels from the first two steps to produce the final mask. Experiments demonstrate that our method requires only one shot to achieve comparable performance to state-of-the-art methods based on traditional many-shot supervision and even superior performance in a few-shot setting. Moreover, ablation studies confirm the robustness of our approach to variations in one-shot samples, changes in scenes, and the presence of multiple targets.

SSTD: Stripe-Like Space Target Detection using Single-Point Supervision

Stripe-like space target detection (SSTD) plays a key role in enhancing space situational awareness and assessing spacecraft behaviour. This domain faces three challenges: the lack of publicly available datasets, interference from stray light and stars, and the variability of stripe-like targets, which complicates pixel-level annotation. In response, we introduces `AstroStripeSet', a pioneering dataset designed for SSTD, aiming to bridge the gap in academic resources and advance research in SSTD. Furthermore, we propose a novel pseudo-label evolution teacher-student framework with single-point supervision. This framework starts with generating initial pseudo-labels using the zero-shot capabilities of the Segment Anything Model (SAM) in a single-point setting, and refines these labels iteratively. In our framework, the fine-tuned StripeSAM serves as the teacher and the newly developed StripeNet as the student, consistently improving segmentation performance by improving the quality of pseudo-labels. We also introduce `GeoDice', a new loss function customized for the linear characteristics of stripe-like targets. Extensive experiments show that the performance of our approach matches fully supervised methods on all evaluation metrics, establishing a new state-of-the-art (SOTA) benchmark. Our dataset and code will be made publicly available.