Automatic Stockpile Volume Monitoring Using Multi-View Stereo from Skysat Imagery

Marí, Roger; Franchis, Carlo de; Meinhardt-Llopis, Enric; Facciolo, Gabriele

doi:10.1109/igarss47720.2021.9554482

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Satellite imagery (or Earth-observation imagery, spaceborne photography, or simply satellite photo) can be used for earthwork monitoring and stockpile volume estimation [24][25][26][27][28]. This method is useful for monitoring changes in landfills over a long period of time, particularly for large areas.…”

Section: Satellite Imagerymentioning

confidence: 99%

Stockpile Volume Estimation in Open and Confined Environments: A Review

Alsayed,

Nabawy

2023

Drones

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This paper offers a comprehensive review of traditional and advanced stockpile volume-estimation techniques employed within both outdoor and indoor confined spaces, whether that be a terrestrial- or an aerial-based technique. Traditional methods, such as manual measurement and satellite imagery, exhibit limitations in handling irregular or constantly changing stockpiles. On the other hand, more advanced techniques, such as global navigation satellite system (GNSS), terrestrial laser scanning (TLS), drone photogrammetry, and airborne light detection and ranging (LiDAR), have emerged to address these challenges, providing enhanced accuracy and efficiency. Terrestrial techniques relying on GNSS, TLS, and LiDAR offer accurate solutions; however, to minimize or eliminate occlusions, surveyors must access geometrically constrained places, representing a serious safety hazard. With the speedy rise of drone technologies, it was not unexpected that they found their way to the stockpile volume-estimation application, offering advantages such as ease of use, speed, safety, occlusion elimination, and acceptable accuracy compared to current standard methods, such as TLS and GNSS. For outdoor drone missions, image-based approaches, like drone photogrammetry, surpass airborne LiDAR in cost-effectiveness, ease of deployment, and color information, whereas airborne LiDAR becomes advantageous when mapping complex terrain with vegetation cover, mapping during low-light or dusty conditions, and/or detecting small or narrow objects. Indoor missions, on the other hand, face challenges such as low lighting, obstacles, dust, and limited space. For such applications, most studies applied LiDAR sensors mounted on tripods or integrated on rail platforms, whereas very few utilized drone solutions. In fact, the choice of the most suitable technique/approach depends on factors such as site complexity, required accuracy, project cost, and safety considerations. However, this review puts more focus on the potential of drones for stockpile volume estimation in confined spaces, and explores emerging technologies, such as solid-state LiDAR and indoor localization systems, which hold significant promise for the future. Notably, further research and real-world applications of these technologies will be essential for realizing their full potential and overcoming the challenges of operating robots in confined spaces.

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Section: Satellite Imagerymentioning

confidence: 99%

Stockpile Volume Estimation in Open and Confined Environments: A Review

Alsayed,

Nabawy

2023

Drones

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“…SmallSat push-frame imagery is used in a wide range of remote sensing applications, including topography extraction (Aati and Avouac, 2020, Bhushan et al, 2021, d'Angelo and Reinartz, 2021, super-resolution products (Nguyen et al, 2021, Anger et al, 2020 and various tasks demanding short revisit times, such as monitoring of natural phenomena (Cannistra et al, 2021) or commercial assets (Marí et al, 2021b, d'Autume et al, 2020. This paper focuses on the task of 3D reconstruction, but the proposed method can be beneficial for any of these applications.…”

Section: Related Workmentioning

confidence: 99%

“…As explained in Section 2, for the case of the L1B scenes the 3D reconstruction is a multi-view problem. We use the MVS methodology described in (Marí et al, 2021b) to solve it. Following the selection of P suitable pairs of scenes, S2P is employed to reconstruct P independent local DSMs, using the corrected RPCs of the L1B scenes (Section 3.1).…”

Section: Stereo Reconstruction Based Evaluationmentioning

confidence: 99%

L1b+: A Perfect Sensor Localization Model for Simple Satellite Stereo Reconstruction From Push-Frame Image Strips

Marí

Ehret²,

Anger³

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. We propose a novel method to generate a single image product from a multi-image strip acquired by a push-frame satellite imaging system. The images of the push-frame strips are combined into a large scale mosaic simulating a perfect sensor geometry. The local camera models of the input images are leveraged to produce a new localization model that covers the output mosaic entirely. Among other applications, this simplifies the task of stereo reconstruction enormously: instead of treating multiple stereo pairs of small images, it is possible to reconstruct the entire area covered by the push-frame acquisition using a single pair of mosaics incorporating all the images. We test our method using strips of SkySat L1B scenes and denote the output images as L1B+. To evaluate the quality of the L1B+ images and their localization models, the stereo reconstructions obtained with L1B+ are compared with those obtained with L1B and with a lidar reference model.

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HRTracker: Multi-Object Tracking in Satellite Video Enhanced by High-Resolution Feature Fusion and an Adaptive Data Association

Wu,

Liu,

Sun

et al. 2024

Remote Sensing

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Multi-object tracking in satellite videos (SV-MOT) is an important task with many applications, such as traffic monitoring and disaster response. However, the widely studied multi-object tracking (MOT) approaches for general images can rarely be directly introduced into remote sensing scenarios. The main reasons for this can be attributed to the following: (1) the existing MOT approaches would cause a significant rate of missed detection of the small targets in satellite videos; (2) it is difficult for the general MOT approaches to generate complete trajectories in complex satellite scenarios. To address these problems, a novel SV-MOT approach enhanced by high-resolution feature fusion and a two-step association method is proposed. In the high-resolution detection network, a high-resolution feature fusion module is designed to assist detection by maintaining small object features in forward propagation. By utilizing features of different resolutions, the performance of the detection of small targets in satellite videos is improved. Through high-quality detection and the use of an adaptive Kalman filter, the densely packed weak objects can be effectively tracked by associating almost every detection box instead of only the high-score ones. The comprehensive experimental results using the representative satellite video datasets (VISO) demonstrate that the proposed HRTracker with the state-of-the-art (SOTA) methods can achieve competitive performance in terms of the tracking accuracy and the frequency of ID conversion, obtaining a tracking accuracy score of 74.6% and an ID F1 score of 78.9%.

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Automatic Stockpile Volume Monitoring Using Multi-View Stereo from Skysat Imagery

Cited by 3 publications

References 15 publications

Stockpile Volume Estimation in Open and Confined Environments: A Review

Stockpile Volume Estimation in Open and Confined Environments: A Review

L1b+: A Perfect Sensor Localization Model for Simple Satellite Stereo Reconstruction From Push-Frame Image Strips

HRTracker: Multi-Object Tracking in Satellite Video Enhanced by High-Resolution Feature Fusion and an Adaptive Data Association

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