Towards image and video super-resolution for improved analytics from overhead imagery

Marc Bosch; Rodrigo Rene Rai Munoz Abujder; Christopher Gifford

doi:10.1117/12.2518179

13 May 2019 Towards image and video super-resolution for improved analytics from overhead imagery

Marc Bosch, Rodrigo Rene Rai Munoz Abujder, Christopher Gifford

Proceedings Volume 10992, Geospatial Informatics IX; 1099203 (2019) https://doi.org/10.1117/12.2518179
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

In this work, we address the problem of losing details in the overhead remote sensing image acquisition and generation process due to sensor resolution and distance to target by leveraging state-of-the-art deep neural network architectures. The goal is to recover such details by super-resolving the images acquired by overhead imaging sensors in order for human analysts to interpret data more accurately, and consequentially, for automated visual exploitation algorithms to be applied more effectively. We have developed a super-resolution framework operating on overhead full motion video (FMV) and still imagery (e.g. satellite images). Our framework consists of a neural network capable of learning the mapping between low and high resolution images in order to produce plausible details about the scene. Our framework combines Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs) to process low resolution signals both spatially and, in the case of FMV, temporally. We have applied the output of our system to several visual perception tasks, including object detection, object tracking, and semantic segmentation. We have also applied our methods to data from different geographical areas, sensors, and even modalities to demonstrate broad and generalized applicability.

Conference Presentation

Citation Download Citation

Marc Bosch, Rodrigo Rene Rai Munoz Abujder, and Christopher Gifford "Towards image and video super-resolution for improved analytics from overhead imagery", Proc. SPIE 10992, Geospatial Informatics IX, 1099203 (13 May 2019); https://doi.org/10.1117/12.2518179

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