[HTML][HTML] Remote sensing image fusion on 3D scenarios: A review of applications for agriculture and forestry
Abstract Three-dimensional (3D) image mapping of real-world scenarios has a great
potential to provide the user with a more accurate scene understanding. This will enable …
potential to provide the user with a more accurate scene understanding. This will enable …
Are unmanned aerial vehicle-based hyperspectral imaging and machine learning advancing crop science?
The past few years have seen increased interest in unmanned aerial vehicle (UAV)-based
hyperspectral imaging (HSI) and machine learning (ML) in agricultural research …
hyperspectral imaging (HSI) and machine learning (ML) in agricultural research …
Multi-sensor and multi-platform consistency and interoperability between UAV, Planet CubeSat, Sentinel-2, and Landsat reflectance data
Unmanned aerial vehicle (UAV) and satellite data have considerable complementarity for
platform inter-operability, data fusion studies, calibration and validation efforts, and various …
platform inter-operability, data fusion studies, calibration and validation efforts, and various …
Accuracy of 3d landscape reconstruction without ground control points using different uas platforms
The rapid increase of low-cost consumer-grade to enterprise-level unmanned aerial systems
(UASs) has resulted in the exponential use of these systems in many applications. Structure …
(UASs) has resulted in the exponential use of these systems in many applications. Structure …
Extending geometallurgy to the mine scale with hyperspectral imaging: A pilot study using drone-and ground-based scanning
Geometallurgical assessment of orebodies in the mining industry typically relies on bench-
scale or lab-based characterization techniques. In this study, we investigate drone-and …
scale or lab-based characterization techniques. In this study, we investigate drone-and …
Machine learning strategies for the retrieval of leaf-chlorophyll dynamics: model choice, sequential versus retraining learning, and hyperspectral predictors
Monitoring leaf Chlorophyll (Chl) in-situ is labor-intensive, limiting representative sampling
for detailed mapping of Chl variability at field scales across time. Unmanned aeria-l vehicles …
for detailed mapping of Chl variability at field scales across time. Unmanned aeria-l vehicles …
Prediction of the severity of Dothistroma needle blight in radiata pine using plant based traits and narrow band indices derived from UAV hyperspectral imagery
Dothistroma needle blight, caused by the fungi Dothistroma septosporum and D. pini is
globally one of the most damaging diseases of pine species. Infection from the pathogen …
globally one of the most damaging diseases of pine species. Infection from the pathogen …
Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles
Coastal water flows facilitate important nutrient exchanges between mangroves, seagrasses
and coral reefs. However, due to the complex nature of tidal interactions, their …
and coral reefs. However, due to the complex nature of tidal interactions, their …
Underwater hyperspectral imaging (UHI): A review of systems and applications for proximal seafloor ecosystem studies
Marine ecosystem monitoring requires observations of its attributes at different spatial and
temporal scales that traditional sampling methods (eg, RGB imaging, sediment cores) …
temporal scales that traditional sampling methods (eg, RGB imaging, sediment cores) …
Mineralogical mapping with accurately corrected shortwave infrared hyperspectral data acquired obliquely from UAVs
While uncrewed aerial vehicles are routinely used as platforms for hyperspectral sensors,
their application is mostly confined to nadir imaging orientations. Oblique hyperspectral …
their application is mostly confined to nadir imaging orientations. Oblique hyperspectral …