Training set effect on super resolution for automated target recognition

Matthew Ciolino; David Noever; Josh Kalin

doi:10.1117/12.2557845

24 April 2020 Training set effect on super resolution for automated target recognition

Matthew Ciolino, David Noever, Josh Kalin

Proceedings Volume 11394, Automatic Target Recognition XXX; 113940P (2020) https://doi.org/10.1117/12.2557845
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Single Image Super Resolution (SISR) is the process of mapping a low-resolution image to a high-resolution image. This inherently has applications in remote sensing as a way to increase the spatial resolution in satellite imagery. This suggests a possible improvement to automated target recognition in image classification and object detection. We explore the effect that different training sets have on SISR with the network, Super Resolution Generative Adversarial Network (SRGAN). We train 5 SRGANs on different land-use classes (e.g. agriculture, cities, ports) and test them on the same unseen dataset. We attempt to find the qualitative and quantitative differences in SISR, binary classification, and object detection performance. We find that curated training sets that contain objects in the test ontology perform better on both computer vision tasks while having a complex distribution of images allows object detection models to perform better. However, Super Resolution (SR) might not be beneficial to certain problems and will see a diminishing amount of returns for datasets that are closer to being solved.

Conference Presentation

Citation Download Citation

Matthew Ciolino, David Noever, and Josh Kalin "Training set effect on super resolution for automated target recognition", Proc. SPIE 11394, Automatic Target Recognition XXX, 113940P (24 April 2020); https://doi.org/10.1117/12.2557845

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