Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos (2411.15895v1)

Abstract: Moving object detection in satellite videos (SVMOD) is a challenging task due to the extremely dim and small target characteristics. Current learning-based methods extract spatio-temporal information from multi-frame dense representation with labor-intensive manual labels to tackle SVMOD, which needs high annotation costs and contains tremendous computational redundancy due to the severe imbalance between foreground and background regions. In this paper, we propose a highly efficient unsupervised framework for SVMOD. Specifically, we propose a generic unsupervised framework for SVMOD, in which pseudo labels generated by a traditional method can evolve with the training process to promote detection performance. Furthermore, we propose a highly efficient and effective sparse convolutional anchor-free detection network by sampling the dense multi-frame image form into a sparse spatio-temporal point cloud representation and skipping the redundant computation on background regions. Coping these two designs, we can achieve both high efficiency (label and computation efficiency) and effectiveness. Extensive experiments demonstrate that our method can not only process 98.8 frames per second on 1024x1024 images but also achieve state-of-the-art performance. The relabeled dataset and code are available at https://github.com/ChaoXiao12/Moving-object-detection-in-satellite-videos-HiEUM.

• The paper presents an unsupervised method that iteratively refines pseudo labels to boost moving object detection accuracy in satellite videos.
• It employs a sparse convolutional anchor-free network that converts dense imagery into a spatio-temporal sparse point cloud, enabling real-time processing at 98.8 fps.
• Experimental results demonstrate 2890% and 2870% speed improvements over traditional and SVMOD methods, along with superior F1 scores.

An Evaluation of Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos

The paper "Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos" presents a novel approach for addressing the challenge of detecting moving objects in satellite video footage. The authors articulate a method that departs from traditional supervised learning techniques by proposing an unsupervised framework that leverages pseudo labeling and sparse convolutional networks. This method promises enhanced efficiency and accuracy in detecting small and dim objects from satellite video data, which are notoriously difficult due to their low contrast against backgrounds and the vast amount of video data that needs to be processed.

The proposed framework introduces pseudo label generation through traditional detection methods, which then evolves iteratively to enhance detection performance. This process minimizes the requirement for manually annotated data, which is often expensive and labor-intensive. Initial pseudo labels are generated using a modified traditional method, which are then refined over multiple training iterations. Each iteration utilizes the model to update pseudo labels, enhancing label quality based on object trajectory consistency, thereby improving object detection accuracy over time.

A key innovation is the sparse convolutional anchor-free detection network, which transforms dense, multi-frame satellite imagery into a sparse spatio-temporal point cloud format. This transformation allows the framework to skip redundant computations typical of backgrounds in satellite imagery, focusing computational power more effectively on foreground objects. The sparse representation thus facilitates real-time processing capabilities, promoting efficiency.

Extensive experiments underscore the effectiveness of this methodology. The authors report a processing speed of 98.8 frames per second for images of dimensions 1024 × 1024, a substantial improvement compared to existing models. Moreover, the method sets a new benchmark by achieving superior F1 scores compared to both traditional and several learning-based SVMOD methods. Particularly noteworthy are claims of 2890% and 2870% speedup over traditional B-MCMD and learning-based DSFNet models, respectively, paired with significant F1 score improvements.

The implications of this research are extensive, particularly for applications demanding real-time satellite surveillance, such as military, security, and transportation monitoring systems. By reducing dependence on manual labels and leveraging long-term spatio-temporal data through sparse representations, the authors pave the way for scalable solutions applicable to vast and growing satellite datasets. This research could herald advances in applying unsupervised and sparse techniques to other remote sensing tasks, signaling potential future developments in broader satellite image processing and analysis.

Looking to the future, this work suggests several avenues for improvement and exploration. Further refinements in pseudo-label quality and background modeling could yield even greater efficiencies, and expanding the framework to accommodate additional types of moving objects would increase its utility. Moreover, applying similar methodologies to other domains within AI and remote sensing could lead to new paradigms in efficient data processing architectures. The fusion of these methods with advanced AI models, such as transformers or neural architecture search, is also a potential direction worth exploring to further enhance performance and applicability.