Single Object Tracking in Satellite Videos: A Correlation Filter-Based Dual-Flow Tracker

Chen, Yuzeng; Tang, Yuqi; Yin, Zhang‐qi; Han, Te; Zou, Bin; Feng, Huihui

doi:10.1109/jstars.2022.3185328

Cited by 20 publications

(8 citation statements)

References 57 publications

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“…Jilin‐1 GF‐03, launched by the Chang Guang Satellite Technology Company, is another high‐resolution commercial satellite video constellation. Jilin‐1 collects 0.5–1.2 m 4K high definition color video at frame rates of up to 10 fps and revisit capability of up to six times a day globally (Y. Chen et al., 2022; European Space Agency, 2022). Currently, both platforms are limited to 30–120 s duration video captures.…”

Section: Methodsmentioning

confidence: 99%

Satellite Video Remote Sensing for Flood Model Validation

Masafu,

Williams

2024

Water Resources Research

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Satellite‐based optical video sensors are poised as the next frontier in remote sensing. Satellite video offers the unique advantage of capturing the transient dynamics of floods with the potential to supply hitherto unavailable data for the assessment of hydraulic models. A prerequisite for the successful application of hydraulic models is their proper calibration and validation. In this investigation, we validate 2D flood model predictions using satellite video‐derived flood extents and velocities. Hydraulic simulations of a flood event with a 5‐year return period (discharge of 722 m3 s−1) were conducted using Hydrologic Engineering Center—River Analysis System 2D in the Darling River at Tilpa, Australia. To extract flood extents from satellite video of the studied flood event, we use a hybrid transformer‐encoder, convolutional neural network (CNN)‐decoder deep neural network. We evaluate the influence of test‐time augmentation (TTA)—the application of transformations on test satellite video image ensembles, during deep neural network inference. We employ Large Scale Particle Image Velocimetry (LSPIV) for non‐contact‐based river surface velocity estimation from sequential satellite video frames. When validating hydraulic model simulations using deep neural network segmented flood extents, critical success index peaked at 94% with an average relative improvement of 9.5% when TTA was implemented. We show that TTA offers significant value in deep neural network‐based image segmentation, compensating for aleatoric uncertainties. The correlations between model predictions and LSPIV velocities were reasonable and averaged 0.78. Overall, our investigation demonstrates the potential of optical space‐based video sensors for validating flood models and studying flood dynamics.

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Section: Methodsmentioning

confidence: 99%

Satellite Video Remote Sensing for Flood Model Validation

Masafu,

Williams

2024

Water Resources Research

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“…Also, the inertial mechanism (IM) is designed to prevent model tracking drift adaptively by adopting the characteristics of object motion. A correlation filter based dual-flow tracker is proposed to explore the spatial-spectral feature fusion and motion model for small object tracking [222].…”

Section: Multi-oriented Object Representationmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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“…These methods may be numb to a slight rotational issue. To address tracking drift, some approaches [29][30][31]36,37,41,42] have built motion models based on the relatively stable motion patterns of objects in SVs. In [30,36,37,41,42], the authors use the properties of the Kalman filter [43] to predict the object position at low tracking confidence, which attenuates the tracking drift.…”

Section: Satellite Video Single-object Trackingmentioning

confidence: 99%

“…To address tracking drift, some approaches [29][30][31]36,37,41,42] have built motion models based on the relatively stable motion patterns of objects in SVs. In [30,36,37,41,42], the authors use the properties of the Kalman filter [43] to predict the object position at low tracking confidence, which attenuates the tracking drift. In [29,31], the motion smoothness and centroid inertia models are embedded into the tracking framework to reduce tracking drift.…”

Section: Satellite Video Single-object Trackingmentioning

confidence: 99%

“…In [29,31], the motion smoothness and centroid inertia models are embedded into the tracking framework to reduce tracking drift. However, most of them [29,31,36,37,41,42] place high demands on positioning accuracy during the initial stage. Other methods [30,38,39,44] extract the motion features contained in adjacent frames to prevent tracking drift.…”

Section: Satellite Video Single-object Trackingmentioning

confidence: 99%

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RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking

et al. 2022

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Single-object tracking (SOT) in satellite videos (SVs) is a promising and challenging task in the remote sensing community. In terms of the object itself and the tracking algorithm, the rotation of small-sized objects and tracking drift are common problems due to the nadir view coupled with a complex background. This article proposes a novel rotation adaptive tracker with motion constraint (RAMC) to explore how the hybridization of angle and motion information can be utilized to boost SV object tracking from two branches: rotation and translation. We decouple the rotation and translation motion patterns. The rotation phenomenon is decomposed into the translation solution to achieve adaptive rotation estimation in the rotation branch. In the translation branch, the appearance and motion information are synergized to enhance the object representations and address the tracking drift issue. Moreover, an internal shrinkage (IS) strategy is proposed to optimize the evaluation process of trackers. Extensive experiments on space-born SV datasets captured from the Jilin-1 satellite constellation and International Space Station (ISS) are conducted. The results demonstrate the superiority of the proposed method over other algorithms. With an area under the curve (AUC) of 0.785 and 0.946 in the success and precision plots, respectively, the proposed RAMC achieves optimal performance while running at real-time speed.

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Single Object Tracking in Satellite Videos: A Correlation Filter-Based Dual-Flow Tracker

Cited by 20 publications

References 57 publications

Satellite Video Remote Sensing for Flood Model Validation

Satellite Video Remote Sensing for Flood Model Validation

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking

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