Validation of Airborne Hyperspectral Imagery from Laboratory Panel Characterization to Image Quality Assessment: Implications for an Arctic Peatland Surrogate Simulation Site

Soffer, Raymond; Ifimov, Gabriela; Arroyo‐Mora, J. Pablo; Kalácska, Margaret

doi:10.1080/07038992.2019.1650334

Cited by 24 publications

(45 citation statements)

References 48 publications

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“…This approach generally involves up-scaling data from the ground to the scale of the UAS sensing, and requires detailed quality assessment, often via standard statistical metrics. These might include Euclidean distance, spectral angle distance, comparing absolute values [149], multiple linear regression and the average sum of deviation square score [150]. QA is mainly performed by correlation coefficients and RMSE.…”

Section: Quality Assurance Metrics For Radiometric Datamentioning

confidence: 99%

Current Practices in UAS-based Environmental Monitoring

et al. 2020

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With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.

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Section: Quality Assurance Metrics For Radiometric Datamentioning

confidence: 99%

Current Practices in UAS-based Environmental Monitoring

et al. 2020

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“…The device has a 0.484 mrad instantaneous field of view at nadir with a variable Remote Sens. 2020, 12, 641 5 of 27 f-number aperture that is configurable between 3.5 and 18.0 [47]. Table 1 records the parameters (heading, speed, altitude, integration time, frame time, time and date) associated with the flight lines.…”

Section: Airborne Hsi Datamentioning

confidence: 99%

“…The third step removed the laboratory-measured spectral smile by resampling the data from each spatial pixel to a uniform wavelength array. In the final processing stage, the imaging data were atmospherically corrected with ATCOR4 (ReSe, Wil, Switzerland), converting the measured radiance to units of surface reflectance (%) [47]. To preserve the original sensor geometry, the images were not geocorrected.…”

Section: Airborne Hsi Datamentioning

confidence: 99%

“…To preserve the original sensor geometry, the images were not geocorrected. The Macdonald-Cartier airport and the surrounding area is primarily composed of man-made materials that have defined edges between spectrally homogenous matter such as asphalt and concrete [47,55]. The area surrounding the Macdonald-Cartier airport contains the Flight Research Laboratory's calibration site, which is composed of asphalt and concrete that have been spectrally monitored over the past decade.…”

Section: Airborne Hsi Datamentioning

confidence: 99%

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Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data

et al. 2020

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In hyperspectral imaging (HSI), the spatial contribution to each pixel is non-uniform and extends past the traditionally square spatial boundaries designated by the pixel resolution, resulting in sensor-generated blurring effects. The spatial contribution to each pixel can be characterized by the net point spread function, which is overlooked in many airborne HSI applications. The objective of this study was to characterize and mitigate sensor blurring effects in airborne HSI data with simple tools, emphasizing the importance of point spread functions. Two algorithms were developed to (1) quantify spatial correlations and (2) use a theoretically derived point spread function to perform deconvolution. Both algorithms were used to characterize and mitigate sensor blurring effects on a simulated scene with known spectral and spatial variability. The first algorithm showed that sensor blurring modified the spatial correlation structure in the simulated scene, removing 54.0%–75.4% of the known spatial variability. Sensor blurring effects were also shown to remove 31.1%–38.9% of the known spectral variability. The second algorithm mitigated sensor-generated spatial correlations. After deconvolution, the spatial variability of the image was within 23.3% of the known value. Similarly, the deconvolved image was within 6.8% of the known spectral variability. When tested on real-world HSI data, the algorithms sharpened the imagery while characterizing the spatial correlation structure of the dataset, showing the implications of sensor blurring. This study substantiates the importance of point spread functions in the assessment and application of airborne HSI data, providing simple tools that are approachable for all end-users.

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“…Field spectroscopy has long played a key role in the collection of spectral information used for a broad range of remote sensing studies [1][2][3]. Surface reflectance data have applications in multiple disciplines, such as forestry [4], agriculture [5], mining [6], calibration and validation [3,7], and many others. Solar-reflective spectroscopy data (i.e., near ultraviolet to shortwave infrared) from portable field spectrometers contain detailed information regarding the physical properties and chemical composition of materials.…”

Section: Introductionmentioning

confidence: 99%

ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data

Elmer

Soffer

Arroyo‐Mora

et al. 2020

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Over the past 30 years, the use of field spectroscopy has risen in importance in remote sensing studies for the characterization of the surface reflectance of materials in situ within a broad range of applications. Potential uses range from measurements of individual targets of interest (e.g., vegetation, soils, validation targets) to characterizing the contributions of different materials within larger spatially mixed areas as would be representative of the spatial resolution captured by a sensor pixel (UAV to satellite scale). As such, it is essential that a complete and rigorous assessment of both the data acquisition procedures and the suitability of the derived data product be carried out. The measured energy from solar-reflective range spectroradiometers is influenced by the viewing and illumination geometries and the illumination conditions, which vary due to changes in solar position and atmospheric conditions. By applying corrections, the estimated absolute reflectance (Rabs) of targets can be calculated. This property is independent of illumination intensity or conditions, and is the metric commonly suggested to be used to compare spectra even when data are collected by different sensors or acquired under different conditions. By standardizing the process of estimated Rabs, as is provided in the described toolkit, consistency and repeatability in processing are ensured and the otherwise labor-intensive and error-prone processing steps are streamlined. The resultant end data product (Rabs) represents our current best effort to generate consistent and comparable ground spectra that have been corrected for viewing and illumination geometries as well as other factors such as the individual characteristics of the reference panel used during acquisition.

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Validation of Airborne Hyperspectral Imagery from Laboratory Panel Characterization to Image Quality Assessment: Implications for an Arctic Peatland Surrogate Simulation Site

Cited by 24 publications

References 48 publications

Current Practices in UAS-based Environmental Monitoring

Current Practices in UAS-based Environmental Monitoring

Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data

ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data

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