Abstract
The georeferencing accuracy of a ground-based mobile mapping system designated for agricultural applications is tested. The system integrates a hyperspectral sensor, digital camera, global navigation satellite system receivers, and an inertial navigation system. Acquired imagery was synchronized with GPS time using custom hardware and software solutions developed in-house. The imaging platform was mounted on a forklift and used to conduct three imaging missions along a paved road segment and agricultural beds. Sixteen ground control points were established in each site and used to calibrate the system and test the positional accuracy. The control point coordinates were determined using GNSS and total station observations independent from the imaging data. The navigation data were postprocessed to extract sensor positions and attitude along the imaging trajectories. The pushbroom hyperspectral images were georeferenced using ReSe Parge software, while the digital camera images were analyzed using Agisoft PhotoScan software. Control point coordinates extracted from the georeferenced imagery were compared to corresponding ground-surveyed coordinates. The maximum root mean square errors obtained for the hyperspectral images in all experiments were 2.4 and 3.1 cm in the easting and northing directions, respectively. These results were achieved using only two control points at both ends of the scan line to estimate the boresight offsets. The RMSE values of the orthorectified image constructed using the digital camera images and two control points at each end of the agricultural site were 1.6 and 2.6 cm in the easting and northing directions.
Highlights
Remote sensing imagery has been used extensively in agricultural applications such as plant stress detection, yield estimation, and field management
Failing to account for the pitch and roll boresight offsets in the hyperspectral image, georeferencing process has a great effect on the image positional accuracy
Abd-Elrahman et al.: Georeferencing of mobile ground-based hyperspectral digital single-lens reflex imagery the difference between the global navigation satellite system (GNSS)/total-station surveyed control point coordinates and the ones extracted from the georeferenced images
Summary
Remote sensing imagery has been used extensively in agricultural applications such as plant stress detection, yield estimation, and field management. Foliage changes are often identified and modeled using spectral measurements.[1,2,3] Stressed vegetation absorbs and reflects radiation differently along the electromagnetic spectrum causing symptoms that can be detected using spectral analysis.[3,4,5] changes in plant foliage due to different stress sources, such as diseases, nutrient deficiency, and drought, can be detected and quantified using spectral imaging. Using hyperspectral and multispectral imagery in agricultural field management is expected to continue to gain momentum as the sensors become more available and easier to deploy. Most hyperspectral imaging is performed using spaceborne and airborne platforms, which provide submeter and greater ground pixel size. The resolution range from these platforms is adequate for large area studies, it does not enable studies at the single-leaf or subcanopy
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