Hyperspektrale Fernerkundung mit dem UAS "Stuttgarter Adler" - Systemübersicht, Kalibrierung und erste Ergebnisse

Büttner, Axel; Röser, Hans‐Peter

doi:10.1127/1432-8364/2014/0217

Cited by 15 publications

(25 citation statements)

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“…The corrections of the sensor artifacts, such as the vignetting corrections, have been common in UAV remote sensing research literature [4,7,[13][14][15]23,24]. In most studies, single or multiple reflectance panels have been used to carry out the reflectance transformation [6,[13][14][15].…”

Section: Discussionmentioning

confidence: 99%

“…In the following sections, the geometric (2-3) and radiometric (1,4,5) processing steps used in this investigation are described.…”

Section: Data Processing Chainmentioning

confidence: 99%

“…Using these technologies, spectral remote sensing measurements can be made cost-efficiently, with greater accuracy and detail than ever before. Several pushbroom hyperspectral imaging sensors [2][3][4][5][6] and point spectrometers [7,8] have recently been implemented in UAVs. Hyperspectral cameras based on the 2D frame format sensors have entered the market in recent years, offering an interesting alternative for hyperspectral data capture [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…In recent studies, the radiometric correction of UAV imagery and spectrometry has mostly emphasized the atmospheric correction using reflectance panels [6,[12][13][14][15][31][32][33][34][35][36][37], atmospheric radiative transfer modeling [2,4] or normalization of UAV based radiance observations using field based radiance observations of a reference panel or irradiance [7,8,31]. Aasen et al [13] and Yang et al [15] calibrated the Cubert UHD 185-Firefly hyperspectral imagery; they emphasized the sensor calibration and performed reflectance calibration using single or multiple reflectance panels.…”

Section: Introductionmentioning

confidence: 99%

“…a radiometrically-calibrated sensor is the prerequisite [23][24][25]. The fundamental calibration parameters include the photon response nonuniformity correction (PRNU) to compensate for the vignetting effects and the nonuniformity of the detector [4,[13][14][15]23,24]; the sensor calibration parameters may also include the absolute calibration model from DNs to physical units of radiance. For absolutely calibrated sensors, the physically based atmospheric correction can be utilized; corrections for the path radiance, impacts of the adjacent objects and irradiance levels can be estimated utilizing the atmospheric radiative transfer codes, such as Modtran or 6S and the insitu observations of weather conditions [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment

Honkavaara

Khoramshahi

2018

Remote Sensing

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Unmanned airborne vehicles (UAV) equipped with novel, miniaturized, 2D frame format hyper-and multispectral cameras make it possible to conduct remote sensing measurements cost-efficiently, with greater accuracy and detail. In the mapping process, the area of interest is covered by multiple, overlapping, small-format 2D images, which provide redundant information about the object. Radiometric correction of spectral image data is important for eliminating any external disturbance from the captured data. Corrections should include sensor, atmosphere and view/illumination geometry (bidirectional reflectance distribution function-BRDF) related disturbances. An additional complication is that UAV remote sensing campaigns are often carried out under difficult conditions, with varying illumination conditions and cloudiness. We have developed a global optimization approach for the radiometric correction of UAV image blocks, a radiometric block adjustment. The objective of this study was to implement and assess a combined adjustment approach, including comprehensive consideration of weighting of various observations. An empirical study was carried out using imagery captured using a hyperspectral 2D frame format camera of winter wheat crops. The dataset included four separate flights captured during a 2.5 h time period under sunny weather conditions. As outputs, we calculated orthophoto mosaics using the most nadir images and sampled multiple-view hyperspectral spectra for vegetation sample points utilizing multiple images in the dataset. The method provided an automated tool for radiometric correction, compensating for efficiently radiometric disturbances in the images. The global homogeneity factor improved from 12-16% to 4-6% with the corrections, and a reduction in disturbances could be observed in the spectra of the object points sampled from multiple overlapping images. Residuals in the grey and white reflectance panels were less than 5% of the reflectance for most of the spectral bands.

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