Unmanned Aerial Vehicles (UAVs) provide a flexible method for acquiring high-resolution imagery with relative simple operation and cost-effectiveness. This technology emerged 30 years ago and it is widely used by commercial, scientific, and military communities due to its versatility. However, new technology brings new challenges. One of them is the radiometric accuracy of the UAV imagery. Radiometric accuracy is especially important when working with different illumination conditions, dates or sensors.
Radiometry is the science of measuring light in any portion of the electromagnetic (EM) spectrum. It is important to consider radiometry when working with optical sensors since the recorded images depend on the sunlight.
Radiometric calibration is specially needed when comparing datasets collected during multiple time periods, across sensors or when there are different illumination conditions over the same area. The last one is particularly important when working with UAVs, since the camera takes several images that will be overlapped to generate an orthomosaic
Focusing on UAV imagery, some factors that contribute to radiometric issues are: (1) the use of wide field of view (FOV) imaging equipment that creates an inherent radial variation in viewing angle, (2) the solar motion that creates a non-static illumination source, and (3) even though multispectral UAV imagery is cloud-free, clouds influence the incoming solar radiation at the surveyed area generating darker spots
It is important to mention that traditional RS has an established radiometric correction process while in UAV technology, a systematic, feasible, and convenient radiometric calibration method has not yet been developed
- To test, improve and validate a UAV radiometric calibration/correction protocol.
- To familiarize the candidate with the workflow related to UAV imagery acquisition.
- To develop a sound protocol for a market standard multispectral sensor.
- MicaSense Github Tutorial
- Study of radiometric variations in Unmanned Aerial Vehicle remote sensing imagery for vegetation mapping
- Programming in Python or R
- Agisoft Photoscan
Theme(s): Sensing & measuring