Volumetric Change Detection in Bedrock Coastal Cliffs Using Terrestrial Laser Scanning and UAS-Based SfM

Hayakawa, Yuichi S.; Obanawa, Hiroyuki

doi:10.3390/s20123403

Cited by 19 publications

(8 citation statements)

References 43 publications

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“…As for the relationship between the UAV and SfM algorithm, the UAV oblique photogrammetry can obtain both top and facade information of the target simultaneously, becoming a bridge connecting the traditional aerial photogrammetry and ground close-range photogrammetry. At present, it is very common to use the UAV images as data source for SfM sparse reconstruction of oblique images (Westoby et al, 2012;Ishiguro et al, 2016;Hayakawa, 2020).…”

Section: Data Processingmentioning

confidence: 99%

Method for UAV-based 3D topography reconstruction of tidal creeks

Zhang

Gong

et al. 2021

J. Geogr. Sci.

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It is common to obtain the topography of tidal flats by the Unmanned Aerial Vehicle (UAV) photogrammetry, but this method is not applicable in tidal creeks. The residual water will lead to inaccurate depth inversion results, and the topography of tidal creeks mainly depends on manual survey. The present study took the tidal creek of Chuandong port in Jiangsu Province, China, as the research area and used UAV oblique photogrammetry to reconstruct the topography of the exposed part above the water after the ebb tide. It also proposed a Trend Prediction Fitting (TPF) method for the topography of the unexposed part below the water to obtain a complete 3D topography. The topography above the water measured by UAV has the vertical precision of 12 cm. When the TPF method is used, the cross-section should be perpendicular the central axis of the tidal creek. A polynomial function can be adapted to most shape of sections, while a Fourier function obtains better results in asymmetrical sections. Compared with the two-order function, the three-order function lends itself to more complex sections. Generally, the TPF method is more suitable for small, straight tidal creeks with clear texture and no vegetation cover.

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Section: Data Processingmentioning

confidence: 99%

Method for UAV-based 3D topography reconstruction of tidal creeks

Zhang

Gong

et al. 2021

J. Geogr. Sci.

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“…SFM offers many powerful and effective techniques that help with terrain change monitoring (Cucchiaro et al, 2021). Several recent studies have highlighted that the use of SFM technology makes it possible to monitor geomorphology for many years in the same location (Hayakawa and Obanawa, 2020;Koukouvelas et al, 2020). Furthermore, SFM can also be applied to investigate coastal cliff stability, and historical UAV images can be analyzed to identify long-term geomorphic changes in retreating coastal dune areas (Mancini et al, 2017;Berquist et al, 2018;Grottoli et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling

Yan

Zhou

et al. 2022

Front. Plant Sci.

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The cliff ecosystem is one of the least human-disturbed ecosystems in nature, and its inaccessible and often extreme habitats are home to many ancient and unique plant species. Because of the harshness of cliff habitats, their high elevation, steepness of slopes, and inaccessibility to humans, surveying cliffs is incredibly challenging. Comprehensive and systematic information on cliff vegetation cover is not unavailable but obtaining such information on these cliffs is fundamentally important and of high priority for environmentalists. Traditional coverage survey methods—such as large-area normalized difference vegetation index (NDVI) statistics and small-area quadratic sampling surveys—are not suitable for cliffs that are close to vertical. This paper presents a semi-automatic systematic investigation and a three-dimensional reconstruction of karst cliffs for vegetation cover evaluation. High-resolution imagery with structure from motion (SFM) was captured by a smart unmanned aerial vehicle (UAV). Using approximately 13,000 records retrieved from high-resolution images of 16 cliffs in the karst region Guilin, China, 16 models of cliffs were reconstructed. The results show that this optimized UAV photogrammetry method greatly improves modeling efficiency and the vegetation cover from the bottom to the top of cliffs is high-low-high, and very few cliffs have high-low cover at the top. This study highlights the unique vegetation cover of karst cliffs, which warrants further research on the use of SFM to retrieve cliff vegetation cover at large and global scales.

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“…Another civilian application area in which reconstruction techniques are widely used regards the change detection task. The work presented in [47], for instance, shows how 3D reconstructed models can support the analysis of volumetric changes in generic environments. The combination of Unmanned Aerial Systems (UASs), Structure-from-Motion (SfM) multi-view stereophotogrammetry [48], and Terrestrial Laser Scanning (TLS) allows the authors to retrieve precise information about the morphology of structures.…”

Section: Introductionmentioning

confidence: 99%

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

et al. 2021

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In recent years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video surveillance applications, such as vehicle tracking, border control, dangerous object detection, and many others. Anomaly detection can represent a prerequisite of many of these applications thanks to its ability to identify areas and/or objects of interest without knowing them a priori. In this paper, a One-Class Support Vector Machine (OC-SVM) anomaly detector based on customized Haralick textural features for aerial video surveillance at low-altitude is presented. The use of a One-Class SVM, which is notoriously a lightweight and fast classifier, enables the implementation of real-time systems even when these are embedded in low-computational small-scale UAVs. At the same time, the use of textural features allows a vision-based system to detect micro and macro structures of an analyzed surface, thus allowing the identification of small and large anomalies, respectively. The latter aspect plays a key role in aerial video surveillance at low-altitude, i.e., 6 to 15 m, where the detection of common items, e.g., cars, is as important as the detection of little and undefined objects, e.g., Improvised Explosive Devices (IEDs). Experiments obtained on the UAV Mosaicking and Change Detection (UMCD) dataset show the effectiveness of the proposed system in terms of accuracy, precision, recall, and F1-score, where the model achieves a 100% precision, i.e., never misses an anomaly, but at the expense of a reasonable trade-off in its recall, which still manages to reach up to a 71.23% score. Moreover, when compared to classical Haralick textural features, the model obtains significantly higher performances, i.e., ≈20% on all metrics, further demonstrating the approach effectiveness.

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Volumetric Change Detection in Bedrock Coastal Cliffs Using Terrestrial Laser Scanning and UAS-Based SfM

Cited by 19 publications

References 43 publications

Method for UAV-based 3D topography reconstruction of tidal creeks

Method for UAV-based 3D topography reconstruction of tidal creeks

A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

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