Georegistration of airborne hyperspectral image data

Lee, C.; Bethel, James

doi:10.1109/36.934067

Cited by 39 publications

(15 citation statements)

References 2 publications

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“…The first is a scan-line to scan-line error, which is the highest frequency error. It most likely results from differential movement or vibration of the airframe [36]. The second, lower-frequency error, which is S-shaped, is likely to be a result of the Kalman filters used to predict aircraft position and orientation from the 25 Hz telemetry stream acquired from the Piccolo II autopilot.…”

Section: Geometric Correctionmentioning

confidence: 99%

Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle

et al. 2012

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Abstract:In the summer of 2010, an Unmanned Aerial Vehicle (UAV) hyperspectral calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the US Department of Energy's Idaho National Laboratory (INL) UAV Research Park. The purpose of the experiment was to validate the radiometric calibration of the spectrometer and determine the georegistration accuracy achievable from the on-board global positioning system (GPS) and inertial navigation sensors (INS) under operational conditions. In order for low-cost hyperspectral systems to compete with larger systems flown on manned aircraft, they must be able to collect data suitable for quantitative scientific analysis. The results of the in-flight calibration experiment indicate an absolute average agreement of 96.3%, 93.7% and 85.7% for calibration tarps of 56%, 24%, and 2.5% reflectivity, respectively. The achieved planimetric accuracy was 4.6 m (based on RMSE) with a flying height of 344 m above ground level (AGL).

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Section: Geometric Correctionmentioning

confidence: 99%

Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle

et al. 2012

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“…Most ortho-rectification software claim theoretical accuracies smaller (or in the order of magnitude) of one single pixel (Lee and Bethel, 2001;Müller et al, 2005Müller et al, , 2008Schläpfer and Richter, 2002). In practice, due to limitations of the available data, in particular of the underlying digital elevation model, much larger discrepancies (i.e.…”

Section: Motivationsmentioning

confidence: 97%

A data-driven approach to quality assessment for hyperspectral systems

Kerr

Fischer

Reulke

2015

Computers & Geosciences

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“…Using this information, we can perform a bundle adjustment to retrieve the corrected exterior orientation parameters for each scan line. Considering N L and N P to be respectively the number of scan lines and the number of pixels per line, X oC i , Y oC i , Z oC i and (r i , p i , y i ) the EOP for line i, (X G i,j , Y G i,j , Z G i,j ) the real ground point for pixel j of line i and µ i,j the collinearity coefficient for pixel j of line i, we wish to minimise the squares of the set of Equation (11).…”

Section: Estimation Of the Orientation Parametersmentioning

confidence: 99%

Pushbroom Hyperspectral Data Orientation by Combining Feature-Based and Area-Based Co-Registration Techniques

Barbieux

2018

Remote Sensing

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Direct georeferencing of airborne pushbroom scanner data usually suffers from the limited precision of navigation sensors onboard of the aircraft. The bundle adjustment of images and orientation parameters, used to perform geocorrection of frame images during the post-processing phase, cannot be used for pushbroom cameras without difficulties-it relies on matching corresponding points between scan lines, which is not feasible in the absence of sufficient overlap and texture information. We address this georeferencing problem by equipping our aircraft with both a frame camera and a pushbroom scanner: the frame images and the navigation parameters measured by a couple GPS/Inertial Measurement Unit (IMU) are input to a bundle adjustment algorithm; the output orientation parameters are used to project the scan lines on a Digital Elevation Model (DEM) and on an orthophoto generated during the bundle adjustment step; using the image feature matching algorithm Speeded Up Robust Features (SURF), corresponding points between the image formed by the projected scan lines and the orthophoto are matched, and through a least-squares method, the boresight between the two cameras is estimated and included in the calculation of the projection. Finally, using Particle Image Velocimetry (PIV) on the gradient image, the projection is deformed into a final image that fits the geometry of the orthophoto. We apply this algorithm to five test acquisitions over Lake Geneva region (Switzerland) and Lake Baikal region (Russia). The results are quantified in terms of Root Mean Square Error (RMSE) between matching points of the RGB orthophoto and the pushbroom projection. From a first projection where the Interior Orientation Parameters (IOP) are known with limited precision and the RMSE goes up to 41 pixels, our geocorrection estimates IOP, boresight and Exterior Orientation Parameters (EOP) and produces a new projection with an RMSE, with the reference orthophoto, around two pixels.

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Georegistration of airborne hyperspectral image data

Cited by 39 publications

References 2 publications

Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle

Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle

A data-driven approach to quality assessment for hyperspectral systems

Pushbroom Hyperspectral Data Orientation by Combining Feature-Based and Area-Based Co-Registration Techniques

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