Abstract
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.
Highlights
Pushbroom cameras prevail in hyperspectral remote sensing because they usually offer much better spectral resolution and much higher number of bands than frame cameras
The usual method to collect and georeference pushbroom data is to equip the aircraft with navigation sensors like GPS and Inertial Measurement Units (IMU), fuse the data from both of these sensors via Kalman Filtering, and perform direct georeferencing [1]—knowing the position and attitude of the vehicle at the time of acquisition, data can be projected on a constant height field or a Digital Elevation Model (DEM) using the collinearity equation, which relates the pixel coordinates of the data to the ground coordinates of the points where they were acquired, in a geographic or projected reference system
Two Ground Sampling Distance (GSD) are given: the across-track GSD, which mainly depends on the geometry of the sensor, the flight altitude and attitude, and the along-track GSD, which depends on the speed of the aircraft, the acquisition frequency and the attitude
Summary
Pushbroom cameras prevail in hyperspectral remote sensing because they usually offer much better spectral resolution and much higher number of bands than frame cameras. The bundle adjustment theory is, of little help in the case of pushbroom imagery: three GCPs per scan line would be required to georeference them properly [8]; the automatic tie points matching between scan lines is extremely difficult because it usually relies on variations of the SIFT algorithm [9] and the 2D neighbourhoods of the points, which are not necessarily available in a 1D acquisition. To bypass these constraints, extra assumptions can be made about the trajectory of the vehicle.
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