Intelligent detection for moving targets in space-borne optical remote sensing:A review
Moving object detection in spaceborne optical remote sensing images(particularly for satellite videos)is a critical technique for interpreting remote sensing data.It has numerous applications,including surveillance,environmental monitoring,and military intelligence.Moving object detection in satellite videos aims to locate and classify moving targets accurately,such as moving vehicles,ships,trains,and airplanes.The advancements in spaceborne optical remote sensing satellite technology and the emergence of deep learning techniques allow the traditional model-driven methods for moving object detection to evolve toward data-driven deep learning methods and achieve high reliability,efficiency,and performance.This study introduces the current status of optical remote sensing satellite systems and summarizes model-driven and data-driven approaches for optical remote sensing moving target detection.First,the current state of video satellites is presented.Spaceborne optical video satellites hold great potential for advancing our ability to detect and monitor dynamic phenomena across various domains,thereby forking the data foundation for moving object detection.Second,the development of model-driven methods for moving object detection is analyzed,including frame differencing-based,optical flow-based,and background subtraction-based methods.However,these methods rely heavily on handcrafted features and prior knowledge to identify moving objects.Thus,they often struggle with complex backgrounds,varying lighting conditions,and occlusions.By contrast,with their powerful feature learning capabilities,data-driven deep learning-based methods have brought significant progress in moving object detection in satellite videos.Third,we present the development of data-driven deep learning-based methods.We also summarize the supervised and unsupervised deep learning-based methods for moving object detection in satellite videos,and their trends are discussed.In deep learning-based methods,extracting spatiotemporal information is crucial for efficient and effective moving object detection in satellite videos.Furthermore,the methods of alleviating annotation costs and addressing massive data are underdeveloped and need further exploration.Then,we introduce the datasets,evaluation criteria,and state-of-the-art experimental results.We evaluate nine representative model-driven and data-driven methods(supervised and unsupervised)for moving object detection in satellite videos on the VISO car dataset.We conclude from the results that deep learning methods with superior performance can adapt effectively to diverse environmental conditions and target characteristics.The unsupervised deep learning methods are underdeveloped and need considerable focus.Moreover,the combination of model-driven and data-driven methods has shown great potential for moving object detection,which leads to a new development trend for the community.Lastly,on the basis of the summary above and the discussion,we conclude the future trends of moving object detection in satellite videos.On the one hand,the development of satellite constellations can open up new possibilities for collaborative and distributive multisatellite and multisensor systems.On the other hand,the potential to merge feature representation,detection,and tracking into an end-to-end network with multitask learning needs further exploration.Weakly supervised and unsupervised methods are worth further development and research to alleviate the burden of annotation cost.The ability to tackle massive data and the construction of an end-to-end detection and recognition network for high-resolution satellite videos are also worth exploring.
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