In this article, we will give a brief introduction to radiometric calibration, indicate signs that your imagery has not been calibrated properly, and offer a couple of ways to implement radiometric calibration in your image processing workflow.
What is radiometric calibration?
Radiometric calibration is a crucial part of processing multispectral imagery, such as the imagery produced by MicaSense RedEdge and Parrot Sequoia. Radiometric calibration takes into account directional measurements (such as the position of the sensor and the sun), irradiance measurements (using tools like light sensors or reflectance panels), as well as gain and exposure data from the camera. Considering these factors enables the conversion of raw digital numbers (from the raw imagery), to sensor reflectance or irradiance, and then to surface reflectance values.
Using a radiometric workflow enables the collection of repeatable reflectance data over different flights, dates, and weather conditions. However, for best results, you should follow the best practices for flying. For example, the best time to fly is around solar noon. This helps avoid shadows, which affect reflectance and prevent accurate analysis.
How to tell if your imagery is lacking a radiometric workflow:
Without radiometric calibration, you may see the following effects:
- Underexposed images, especially surrounding bright objects on the landscape
- Irregular coloration
- Index values, such as NDVI, that appear to change dramatically and unexpectedly near roads or buildings
- Extreme banding or patchiness in your mosaic
RedEdge and Sequoia adjust the gain and exposure time of each imager to ensure that the scene is properly exposed and to prevent over- and under-exposure. This allows the camera to cover a much larger dynamic range of input radiance than would be possible with fixed gains and exposures. Once the pixels saturate, all data in those pixels is lost. For this reason, the camera works to minimize saturated pixels. Due to this feature, certain landscapes that are often brighter than others, such as buildings, concrete, and water, can cause a decrease in the exposure time of the imagers in an attempt to prevent saturation. This can be accounted for when post-processing by ensuring the use of a radiometric workflow.
Collecting adequate data for radiometric calibration:
The collection of solar irradiance data for each flight is a key part of a radiometric workflow. This can be done by taking images of a calibrated reflectance panel pre- and post-flight, and/or using a light sensor during flight (such as the DLS or Sunshine sensor). We highly recommend always taking calibrated reflectance panel images, even when using a light sensor. You can read more about reflectance panels, light sensors, and best practices for data collection in our other articles (links below):
Implementing a radiometric workflow:
The RedEdge image processing tutorial is available on GitHub: Image Processing
Pix4D is one such third-party software that provides this function, using the reflectance map processing workflow (RedEdge: How to Process RedEdge Data in Pix4D | Sequoia: How to Process Sequoia Data in Pix4D). If using Pix4D, please make sure you are generating the reflectance map and providing a calibrated reference panel image as described in the articles mentioned above. To start a free trial of Pix4D, please visit this link: Pix4D Free Trial.
If you're using a different processing solution please contact that vendor to ensure their software is capable of radiometric calibrations and that you're using the appropriate radiometric workflow.
For more information about implementing the RedEdge Camera Radiometric Calibration Model in your own custom image processing workflow, please visit this link: RedEdge Radiometric Calibration Model.
For more information about Parrot Sequoia Radiometric Calibration, please see their technical documentation here and contact Sequoia@Parrot.com for further inquiries.