There is an increasing interest in the use of machine learning deep networks to automatically analyze satellite imagery. However, there are limited annotated satellite imagery datasets available for training these networks. Synthetic image generation offers a solution to this need, but only if the simulated images have comparable characteristics to the real data. This work deals with analysis of commercial satellite imagery to characterize their imaging systems for the purpose of increasing the realism of the synthetic imagery generated by RIT’s Digital Imaging and Remote Sensing Image Generation (DIRSIG) model.
The analysis was applied to satellite imagery from Planet Labs and Digital Globe. Local spatial correlation was leveraged for noise estimation and the EMVA1288 standard was used for noise modeling. Real world calibration targets across the world were used together with the slanted edge method based on the ISO 12233 standard for estimation of the sensor optical systems’ point spread function (PSF). The estimated camera models were then used to generate synthetic imagery using DIRSIG. The PSF was applied within DIRSIG using its in-built functionality while noise was added in post processing. Analysis similar to real imagery was performed on the simulated scenes to verify the application of the model on synthetic scenes. Future work is recommended to further characterize the various imagery products produced by the satellite companies to better represent artifacts present in these processed images.
Library of Congress Subject Headings
Remote-sensing images--Data processing; Machine learning; Neural networks (Computer science); Artificial satellites in remote sensing
Imaging Science (MS)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
John P. Kerekes
Mokashi, Nilay Vijay, "Empirical Satellite Characterization for Realistic Imagery Generation" (2018). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus