The use of small unmanned aircraft systems (sUAS) for applications in the field of precision agriculture has demonstrated the need to produce temporally consistent imagery to allow for quantitative comparisons. In order for these aerial images to be used to identify actual changes on the ground, conversion of raw digital count to reflectance, or to an atmospherically normalized space, needs to be carried out. This paper will describe an experiment that compares the use of reflectance calibration panels, for use with the empirical line method (ELM), against a newly proposed ratio of the target radiance and the downwelling radiance, to predict the reflectance of known targets in the scene. We propose that the use of an on-board downwelling light sensor (DLS) may provide the sUAS remote sensing practitioner with an approach that does not require the expensive and time consuming task of placing known reflectance standards in the scene. Three calibration methods were tested in this study: 2-Point ELM, 1-Point ELM, and At-altitude Radiance Ratio (AARR). Our study indicates that the traditional 2-Point ELM produces the lowest mean error in band effective reflectance factor, 0.0165. The 1-Point ELM and AARR produce mean errors of 0.0343 and 0.0287 respectively. A modeling of the proposed AARR approach indicates that the technique has the potential to perform better than the 2-Point ELM method, with a 0.0026 mean error in band effective reflectance factor, indicating that this newly proposed technique may prove to be a viable alternative with suitable on-board sensors.
Stray light (also called flare) can adversely affect the image quality or application performance of a camera system. Testing for stray light is critical for understanding limitations of camera system performance. Stray light is any light that reaches the detector (i.e., the image sensor) other than through the designed optical path. Depending on the mechanism causing stray light, it can introduce false colors and phantom objects (ghosts) within the scene, reduce contrast over portions of the image (veiling glare), and effectively reduce system dynamic range.In this paper, we present an overview of stray light testing for digital camera systems, as well as lessons learned and various technical elements to consider. These elements include the radiometric (e.g., brightness) and geometric (e.g., size) properties of the light source and test setup. We focus on a test approach that involves illuminating the camera with a small, bright light source and describe how certain elements of the test can impact a measurement.
Students are prepared to care for Latino patients through the cultural immersion program and are also introduced to working in "team-based care" multidisciplinary groups to improve health care outcomes.
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