Abstract
Abstract. The increased availability of unmanned aerial vehicles (UAVs) has resulted in their frequent adoption for a growing range of remote sensing tasks which include precision agriculture, vegetation surveying and fine-scale topographic mapping. The development and utilisation of UAV platforms requires broad technical skills covering the three major facets of remote sensing: data acquisition, data post-processing, and image analysis. In this study, UAV image data acquired by a miniature 6-band multispectral imaging sensor was corrected and calibrated using practical image-based data post-processing techniques. Data correction techniques included dark offset subtraction to reduce sensor noise, flat-field derived per-pixel look-up-tables to correct vignetting, and implementation of the Brown- Conrady model to correct lens distortion. Radiometric calibration was conducted with an image-based empirical line model using pseudo-invariant features (PIFs). Sensor corrections and radiometric calibration improve the quality of the data, aiding quantitative analysis and generating consistency with other calibrated datasets.
Highlights
The increased availability of unmanned aerial vehicles (UAVs) has resulted in their frequent adoption for a growing range of remote sensing tasks which include precision agriculture, vegetation surveying and fine-scale topographic mapping
A six band multispectral UAV acquired salt marsh image was selected to serve as a test case for image based data post-processing
The mini-MCA is a low-cost, lightweight 6-channel multispectral sensor suited to UAV remote sensing research
Summary
The increased availability of unmanned aerial vehicles (UAVs) has resulted in their frequent adoption for a growing range of remote sensing tasks which include precision agriculture, vegetation surveying and fine-scale topographic mapping. The development and utilisation of UAV platforms requires broad technical skills covering the three major facets of remote sensing: data acquisition, data post-processing, and image analysis. UAV image data acquired by a miniature 6-band multispectral imaging sensor was corrected and calibrated using practical image-based data post-processing techniques. Sensor corrections and radiometric calibration improve the quality of the data, aiding quantitative analysis and generating consistency with other calibrated datasets. Vignetting is Unmanned aerial vehicles occupy a previously unfilled niche through a radial falloff in illumination strength caused primarily by intheir capability to generate ultra high spatial resolution image creased occlusion of the detector plane by the sensor (Goldman, data at highly flexible temporal scales (Dunford et al, 2009). UAV development and operation require a range of technical skills covering platform development, image data post-processing and image analysis techniques. Post-processing techniques improve data quality and generates consistency with other calibrated image sets.
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