On-Orbit Moving Target Detection Using Dual-Linear Array Push-broom Imaging
Moving target detection plays a pivotal role in extracting temporal information from time-series images,particularly from satellite data.This method enables the rapid acquisition,analysis,and utilization of dynamic change information,meeting the demand for"real-time target discovery and delivery."In the processing of optical image-based moving target detection,existing methods often fall short of meeting the requirements for large-scale target discovery,accommodating diverse speeds,and ensuring hardware acceleration compatibility.This study aims to achieve swift perception of large-scale moving targets using optical remote sensing satellites,with a primary focus on both camera innovation and algorithm research in terms of target discovery and target information processing.This paper proposes a novel imaging mode,leveraging a dual-linear array push-broom optical remote sensing camera to capture dual-strip images containing temporal changes associated with moving targets.The camera principle prototype was successfully deployed on the"Taijing-4 Satellite"on February 27,2022,thereby validating the technical approach for large-scale detections.Furthermore,this paper introduces a pioneering approach for detecting moving targets based on saliency region proposal for dual-band images,which significantly enhances the temporal information captured in dual-linear array push-broom imaging.Subsequently,we employ a sophisticated saliency region proposal method to extract the prominent regions of moving targets by utilizing the temporal and spatial change information within the image.These salient regions encompass dynamic targets across the entire image,effectively reducing the amount of intermediate data processed by the algorithm.Finally,a lightweight and efficient deep learning object detection model is leveraged to classify moving targets and eliminate false positives from the initial detection outcomes.The results indicate that the proposed method can efficiently detect moving targets in dual-strip images,substantially improving the accuracy of dynamic target shape extraction and optimizing the results of target matching.Notably,by enhancing the recall rate of the moving target detection algorithm,the algorithm's execution efficiency is also increased by 61.4%.This paper demonstrates two remarkable strengths in its viewpoints and discussion.Firstly,it puts forth a groundbreaking imaging mode and method to enhance the temporal information of images,effectively addressing the challenge of observing large-scale moving targets without relying on satellite attitude maneuvering.Secondly,it proposes a highly efficient moving target detection model based on saliency region proposal,resolving the problem of detecting moving targets in complex backgrounds.The acquisition of key information about moving targets can significantly reduce the bandwidth requirements for ground transmission of remote sensing data,providing a new way of data acquisition and on-orbit processing for mega Earth observation systems.
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