Radiometric block adjustment of hyperspectral image blocks in the Brazilian environment

Miyoshi, Gabriela Takahashi; Imai, Nilton Nobuhiro; Tommaselli, Antônio Maria Garcia; Honkavaara, Eija; Näsi, Roope; Moriya, Érika Akemi Saito

doi:10.1080/01431161.2018.1425570

Cited by 37 publications

(44 citation statements)

References 30 publications

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“…The performances of approaches based on the irradiance radiometer on-board the UAV, irradiance spectrometer on the ground, and the radiometric block adjustment were compared, and the radiometric block adjustment provided the best results. Similar conclusions were drawn by Miyoshi et al [201]. The radiometric block adjustment also provides the uniform mosaics of a dataset with different flights with varying solar azimuth and elevation [163].…”

Section: Radiometric Processingsupporting

confidence: 81%

“…Radiometric block adjustment has been applied in studies with different settings to produce uniform image mosaics [163,[177][178][179][180]201]. Principally, the method can compensate for illumination changes during the flight campaign based on the information contained in the images.…”

Section: Radiometric Processingmentioning

confidence: 99%

“…Thus, no further equipment is needed on-board the UAV for the irradiance measurement, but the irradiance recordings can also be integrated to the same adjustment process. In several studies, the method has provided the best uniformity over the entire image dataset when compared with approaches based on ground irradiance spectra measurement and on-board irradiance measurement [44,201]. In Honkavaara et al [44] and Hakala et al [202], datasets were captured in illumination conditions varying from cloudy to sunny.…”

Section: Radiometric Processingmentioning

confidence: 99%

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Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

et al. 2018

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In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial data collection in which not only few specialist data providers collect and deliver remotely sensed data, but a whole diverse community is potentially able to gather geospatial data that fit their needs. However, the diversification of sensing systems and user applications challenges the common application of good practice procedures that ensure the quality of the data. This challenge can only be met by establishing and communicating common procedures that have had demonstrated success in scientific experiments and operational demonstrations. In this review, we evaluate the state-of-the-art methods in UAV spectral remote sensing and discuss sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation. We follow the 'journey' of the reflected energy from the particle in the environment to its representation as a pixel in a 2D or 2.5D map, or 3D spectral point cloud. Additionally, we reflect on the current revolution in remote sensing, and identify trends, potential opportunities, and limitations.

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Section: Radiometric Processingsupporting

confidence: 81%

Section: Radiometric Processingmentioning

confidence: 99%

Section: Radiometric Processingmentioning

confidence: 99%

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Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

et al. 2018

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“…The effect was the most noticeable in the 'Barley UAV 140 m' dataset, which was collected during 4.5 h, when illumination changed significantly, and in the 'Grass UAV 50 m' dataset, which was collected during sunny conditions at a low flying height that caused remarkable anisotropy effects ( Figure 5). Multiple studies have shown that radiometric correction using the RBA method improved the uniformity of image orthomosaics [7,11,12,63,77]. Our results showed that the corrections also improved the accuracy of the crop parameter estimations.…”

Section: Discussionsupporting

confidence: 65%

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

et al. 2018

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Abstract:The timely estimation of crop biomass and nitrogen content is a crucial step in various tasks in precision agriculture, for example in fertilization optimization. Remote sensing using drones and aircrafts offers a feasible tool to carry out this task. Our objective was to develop and assess a methodology for crop biomass and nitrogen estimation, integrating spectral and 3D features that can be extracted using airborne miniaturized multispectral, hyperspectral and colour (RGB) cameras. We used the Random Forest (RF) as the estimator, and in addition Simple Linear Regression (SLR) was used to validate the consistency of the RF results. The method was assessed with empirical datasets captured of a barley field and a grass silage trial site using a hyperspectral camera based on the Fabry-Pérot interferometer (FPI) and a regular RGB camera onboard a drone and an aircraft. Agricultural reference measurements included fresh yield (FY), dry matter yield (DMY) and amount of nitrogen. In DMY estimation of barley, the Pearson Correlation Coefficient (PCC) and the normalized Root Mean Square Error (RMSE%) were at best 0.95% and 33.2%, respectively; and in the grass DMY estimation, the best results were 0.79% and 1.9%, respectively. In the nitrogen amount estimations of barley, the PCC and RMSE% were at best 0.97% and 21.6%, respectively. In the biomass estimation, the best results were obtained when integrating hyperspectral and 3D features, but the integration of RGB images and 3D features also provided results that were almost as good. In nitrogen content estimation, the hyperspectral camera gave the best results. We concluded that the integration of spectral and high spatial resolution 3D features and radiometric calibration was necessary to optimize the accuracy.

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“…Furthermore, disturbances caused by the object-reflectance anisotropy (i.e., bidirectional reflectance distribution function (BRDF)) were determined for the datasets that were collected during sunny weather ( Table 5). The BRDF-correction was determined from the image data and it was based on the BRDF-model that was estimated using intensity information of radiometric tie points appearing in the overlapping images; the procedure is described in detail in [55,58]. Finally, the reflectance orthophoto mosaics were calculated to the nadir view geometry with a GSD of 50 cm using the image orientations, DSM, and the radiometric model.…”

Section: Calculation Of Dense Point Clouds and Image Mosaicsmentioning

confidence: 99%

Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

et al. 2018

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Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring.

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Radiometric block adjustment of hyperspectral image blocks in the Brazilian environment

Cited by 37 publications

References 30 publications

Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

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