Satellite images are taken for a variety of reasons, but the use of the images remains limited due to differences residing in different images from various satellites. The ability to use the images for impactful data collection in various fields including agriculture could provide value in an already existing technology.
SDSU researchers have developed a suite of algorithms and data originally designed for the cross-calibration of satellite imaging systems to “reverse-engineer” satellite imagery from multiple sensor so that the imagery is consistent between sensors. Additionally, the difference in the atmosphere as the imagery was recorded can be accounted for resulting in high temporal resolution image data sets of the Earth’s surface by combining observations from multiple sensors.
The algorithms include a spectral band adjustment and atmospheric correction. The use of this proprietary data allows a user to have a consistent image from satellite imagery regardless of the source of the image or the time of day taken.