Machine Learning Based MTF Estimation System Evaluation Utilizing Slanted-Edge Targets in Suas Scenes

As the number of imaging systems continues to rapidly grow, so too does the need to perform spatial image quality control on captured data. While human assessment for general quality control may be utilized in some applications, target-based and scene-based automated spatial resolution methods may improve speed, accuracy, and efficiency. In certain applications, such as small unmanned-aircraft systems (sUAS) data collection, very large numbers of captured images may even make human assessment impractical, especially for multispectral and hyperspectral modalities. Imagery bands outside of the visible spectrum, such as infrared, may also prove more challenging for human assessment. Traditionally, scientific metrics utilized for spatial quality control such as MTF (modulation transfer function) were measured using in-situ targets such as the slanted-edge or Siemens star. However, newer advanced methods can now estimate these metrics without requiring dedicated targets. Automated machine vision methods utilizing techniques such as neural networks can provide these estimated measurement metrics for rapid image evaluation and quality control. MTF estimation without targets allows much more flexibility in assessment, enables a measurement map across each image, and eliminates the logistical burden of requiring in-scene fielded assets. We examine the performance of one such machine learning based MTF estimation system by placing checkerboard-type slanted-edge targets in multiple sUAS scenes including agricultural, urban, and forested subject matter. We then compare traditional target-based slanted-edge method measurements to the machine learning based estimated values and assess the overall accuracy.