Abstract
Unmanned aerial vehicles (UAVs) represent a quickly evolving technology, broadening the availability of remote sensing tools to small-scale research groups across a variety of scientific fields. Development of UAV platforms requires broad technical skills covering platform development, data post-processing, and image analysis. UAV development is constrained by a need to balance technological accessibility, flexibility in application and quality in image data. In this study, the quality of UAV imagery acquired by a miniature 6-band multispectral imaging sensor was improved through the application of practical image-based sensor correction techniques. Three major components of sensor correction were focused upon: noise reduction, sensor-based modification of incoming radiance, and lens distortion. Sensor noise was reduced through the use of dark offset imagery. Sensor modifications through the effects of filter transmission rates, the relative monochromatic efficiency of the sensor and the effects of vignetting were removed through a combination of spatially/spectrally dependent correction factors. Lens distortion was reduced through the implementation of the Brown–Conrady model. Data post-processing serves dual roles in data quality improvement, and the identification of platform limitations and sensor idiosyncrasies. The proposed corrections improve the quality of the raw multispectral imagery, facilitating subsequent quantitative image analysis.
Highlights
Unmanned aerial vehicles (UAVs) are gaining attention from the scientific community as novel tools for remote sensing applications [1]
The objective of this study is to provide a primarily image-based, linear workflow of the sensor correction of a low-cost consumer grade multispectral sensor
Sensor correction encompasses the suite of techniques for correcting these sensor based processes, allowing the extraction of arbitrary digital numbers (DN)
Summary
Unmanned aerial vehicles (UAVs) are gaining attention from the scientific community as novel tools for remote sensing applications [1]. Compared with more traditional aircraft or satellite based platforms, the UAV fills a previously unoccupied niche due to the unique characteristics of data it is able to capture. Its low operating altitude allows for the generation of ultra-high spatial resolution data over relatively small spatial extents [2] (see Figure 1). The greatly reduced preparation time of UAVs relative to large scale platforms aids in the acquisition of multi-temporal datasets or in exploiting limited windows of opportunity [3]. UAVs may serve to bridge the scale gap between satellite imagery, full-scale aerial photography, and field samples
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