Application of convolutional neural networks for low vegetation filtering from data acquired by UAVs

Gruszczyński, Wojciech; Puniach, Edyta; Ćwiąkała, Paweł; Matwij, Wojciech

doi:10.1016/j.isprsjprs.2019.09.014

Cited by 13 publications

(10 citation statements)

References 23 publications

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“…For each of the six wavelength bands, one large orthoimage covering the study area was created. This orthorectification was performed in Agisoft Metashape using a similar processing workflow as many previous studies [ 45 , 46 ]. For the Micasense data, this produced only a few images that could not be aligned with no gaps in the study area.…”

Section: Methodsmentioning

confidence: 99%

Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors

Barber

Alvarado

Kane

et al. 2021

Sensors

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Predicting wildfire behavior is a complex task that has historically relied on empirical models. Physics-based fire models could improve predictions and have broad applicability, but these models require more detailed inputs, including spatially explicit estimates of fuel characteristics. One of the most critical of these characteristics is fuel moisture. Obtaining moisture measurements with traditional destructive sampling techniques can be prohibitively time-consuming and extremely limited in spatial resolution. This study seeks to assess how effectively moisture in grasses can be estimated using reflectance in six wavelengths in the visible and infrared ranges. One hundred twenty 1 m-square field samples were collected in a western Washington grassland as well as overhead imagery in six wavelengths for the same area. Predictive models of vegetation moisture using existing vegetation indices and components from principal component analysis of the wavelengths were generated and compared. The best model, a linear model based on principal components and biomass, showed modest predictive power (r² = 0.45). This model performed better for the plots with both dominant grass species pooled than it did for each species individually. The presence of this correlation, especially given the limited moisture range of this study, suggests that further research using samples across the entire fire season could potentially produce effective models for estimating moisture in this type of ecosystem using unmanned aerial vehicles, even when more than one major species of grass is present. This approach would be a fast and flexible approach compared to traditional moisture measurements.

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Section: Methodsmentioning

confidence: 99%

Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors

Barber

Alvarado

Kane

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, it is also demonstrated in [86] that the protocols which are used to create a link between the controller and the UAVs are insecure. To solve this problem, three additional mechanisms were used which are: watchdog timer, hardline input data filtering, and antispoofing mechanisms [101], [102]. The watchdog timer provides security against the DoS attack and it works in the domain of operating system (OS).…”

Section: Control System Vulnerabilities and Fuzzing Attacksmentioning

confidence: 99%

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

2021

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With the rapid growth in technology, the use of Unmanned Aerial Vehicles (UAVs) have increased in civil and military applications including rescue operations, disaster recovery, and military operations. Despite the utility and advantages of UAVs, they may lead to major security breaches in the context of hardware, software, and communication channel, due their ease of use and availability. UAVs are vulnerable to various types of attacks such as spoofing, false data injection, jamming, fuzzing, availability, confidentiality, and integrity attacks. To overcome these security threats, researchers have been investigating strong security protocols to keep UAVs safe from the attackers. Nevertheless, there are many flaws in the developed protocols which can be exploited by hackers. Therefore, it is becomes crucial to study and analyze the existing security protocols used in UAVs to discover and address their vulnerabilities and weaknesses. The purpose of this study is to explore the vulnerabilities in the security protocols and propose guidelines to improve the security and provide future research directions.

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“…Therefore, it is necessary to further filter the UAV point cloud to obtain the real digital terrain model (DTM). At present, the point cloud filtering generated by UAV optical cameras has always been a difficult problem in the world (Wang et al, 2015;Tan et al, 2018; Yilmaz et al, 2018;Gruszczyński et al, 2019;Zeybek anḑ Sanlıoglu, 2019). This research mainly refers to the calculation formula of VDVI (Visible-band Difference Vegetation Index) proposed by Wang et al (2015) in vegetation point cloud filtering:…”

Section: Uav Data Post-processingmentioning

confidence: 99%

Detection and Numerical Simulation of Potential Hazard in Oil Pipeline Areas Based on UAV Surveys

Yan

Yin

et al. 2021

Front. Earth Sci.

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Western China is rich in oil and gas resources, and many oil and gas pipelines are under construction or have been completed. However, many water-related natural hazards, such as landslides, collapses, rockfalls, and debris flows, have developed in the areas passed through by oil and gas pipelines and seriously threaten the operational safety of these pipelines. Therefore, it is urgent to carry out large-scale identification and assessment of pipeline geological hazards. At present, conventional on-site investigation, evaluation, monitoring, and early warning methods are difficult to apply for rapid identification and evaluation of pipeline geological hazards across large-scale areas. Based on this, this study takes the pipeline of Sinopec Marketing South China Branch in Yunnan Province as the research area. In this research, unmanned aerial vehicle (UAV) and photogrammetry technology were used to quickly and accurately obtain multi-phase images of an oil pipeline passing through the study area, and the images were post-processed to obtain multi-phase high-resolution, high-precision digital orthophoto maps and digital terrain models (DTMs) to identify landform changes and deformation. The focus of this research is to propose a set of technical methods for UAV point cloud filtering. The DTMs obtained based on this method can effectively identify unstable areas of oil pipelines. In addition, we have carried out numerical simulations under different motion scenarios in unstable regions, providing scientific support for future geological hazard prevention and mitigation and engineering practices in oil pipeline areas.

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Application of convolutional neural networks for low vegetation filtering from data acquired by UAVs

Cited by 13 publications

References 23 publications

Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors

Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

Detection and Numerical Simulation of Potential Hazard in Oil Pipeline Areas Based on UAV Surveys

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