Design and first test results of the Finnish airborne imaging spectrometer for different applications (AISA)

Braam, Bart M.; Okkonen, Jukka; Aikio, Mauri; Mäkisara, Kai; Bolton, John F.

doi:10.1117/12.157051

Cited by 11 publications

(3 citation statements)

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“…Airborne imagery was recorded with the Airborne Imaging Spectrometer for Applications (AISA) on July 16th, 1999 (Braam et al 1993) from approximately 330 m above ground level (AGL) under clear skies. This provided a ground instantaneous field of view (IFOV) of 1 m. Raw pixel values for the 10 nm wide spectral bands centered at 672 and 808 nm were calibrated to absolute at-sensor radiance using coefficients derived from laboratory based measurements.…”

Section: Methodsmentioning

confidence: 99%

Exploitation of spatial information in high resolution digital imagery to map leaf area index

Walthall

Pachepsky

Dulaney³

et al. 2007

Precision Agric

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Crop responses to management practices and the environment, as quantified by leaf area index (LAI), provide decision-making criteria for the delineation of crop management zones. The objective of this work was to investigate whether spatial correlations inferred from remotely sensed imagery can be used to interpolate and map LAI using a relatively small number of ground-based LAI measurements. Airborne imagery was recorded with the Airborne Imaging Spectrometer for Applications (AISA) radiometer over a 3.2 ha corn field. Spectral vegetation indexes (SVI) were derived from the image and aggregated to cells of 2 9 2 m 2 , 4 9 4 m 2 , and 8 9 8 m 2 resolution. The residual maximum likelihood method was used to estimate the LAI variogram parameters. A generalized least squares regression was used to relate ground truth LAI data and collocated image pixels. This regression result was then used to convert variograms from the imagery to LAI units as well as to interpolate and map LAI. The decrease in resolution by merging pixels led to an increase in the value of the r 2 and to a decrease in root meansquared error (RMSE) values. The accuracy of kriged estimates from the variogram of the measured LAI and that from the image derived variograms was estimated by cross-validation. There was no difference in the accuracy of the estimates using either variograms from measured LAI values or from those of converted SVIs. Maps of LAI from groundbased measurements made by kriging the data with image-derived variogram parameters were similar to those obtained by with kriging with the variogram of measured LAI. Similar coarse spatial trends of high, medium and low LAI were evident for both maps. 311-321 DOI 10.1007/s11119-007-9048-7 Variogram parameters from ground-based measurements of LAI compared favorably with those derived from remotely sensed imagery and could be used to provide reasonable results for the interpolation of LAI measurements.

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Section: Methodsmentioning

confidence: 99%

Exploitation of spatial information in high resolution digital imagery to map leaf area index

Walthall

Pachepsky

Dulaney³

et al. 2007

Precision Agric

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“…For our experiments we have used two cameras from the Finnish company Specim, in the VNIR and SWIR wavelength ranges respectively. Both cameras are derived from the first AISA camera, which was the first instrument to use the Prism-Grating-Prism (PGP) dispersive element invented in 1991 by Mauri Aikio [7,8]. The cameras are controlled by Specim's Lumo scanner software, which allows for: data acquisition, setting camera parameters, real-time image visualization, controlling the scanner systems.…”

Section: The Instrumentsmentioning

confidence: 99%

Determining Smile And Keystone Of Lab Hyperspectral Line Cameras

Bakker

Werff

Meer

2019

2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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Modern imaging spectrometers are compact, low weight, reasonably priced, and can be used in the laboratory. Accurate hyperspectral images require low noise, high calibration accuracy, and high response uniformity. Due to ageing of the optical and electronic components a periodic check is recommended. This paper presents a method for measuring the smile and keystone geometric distortions from test images. Measurements are obtained by using simple models for the smile and keystone, and existing SciPy functions for quadratic spline interpolation, normalized cross-correlation, and optimization to quickly find a solution for the models. Results show that the smile and keystone of a laboratory imaging spectrometer can be obtained with sub-pixel accuracy.

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“…Several hyperspectral sensors have been used to acquire data for a number of different applications, such as thematic mapping, geology, hydrology, agricultural assessment, mineral exploration, and study of global environmental and climate changes. Some important sensors include the hyperspectral digital imagery collection experiment, 17 airborne visible/infrared imaging spectrometer, 18 reflective optics system imaging spectrometer, 19 compact airborne spectrographic imager, 20 airborne imaging spectrometer, 21 airborne imaging spectrometer for different applications, 22 geophysical and environmental research imaging spectrometer, 23 hyperspectral mapper, 24 aerosol mass spectrometer, 25 airborne prism experiment, 26 compact high-resolution imaging spectrometer, 27 digital airborne imaging spectrometer, 28 and thermal infrared multispectral scanner. 29 The analysis of hyperspectral data has received growing interest from the community, since they have spectral attributes with high discriminative power.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral data classification improved by minimum spanning forests

Silva

Pedrini

2016

J. Appl. Remote Sens

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Remote sensing technology has applications in various knowledge domains, such as agriculture, meteorology, land use, environmental monitoring, military surveillance, and mineral exploration. The increasing advances in image acquisition techniques have allowed the generation of large volumes of data at high spectral resolution with several spectral bands representing images collected simultaneously. We propose and evaluate a supervised classification method composed of three stages. Initially, hyperspectral values and entropy information are employed by support vector machines to produce an initial classification. Then, the K-nearest neighbor technique searches for pixels with high probability of being correctly classified. Finally, minimum spanning forests are applied to these pixels to reclassify the image taking spatial restrictions into consideration. Experiments on several hyperspectral images are conducted to show the effectiveness of the proposed method.

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Design and first test results of the Finnish airborne imaging spectrometer for different applications (AISA)

Cited by 11 publications

References 0 publications

Exploitation of spatial information in high resolution digital imagery to map leaf area index

Exploitation of spatial information in high resolution digital imagery to map leaf area index

Determining Smile And Keystone Of Lab Hyperspectral Line Cameras

Hyperspectral data classification improved by minimum spanning forests

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