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Proceedings Paper

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

Paper Details

Date Published: 13 May 2019
PDF: 9 pages
Proc. SPIE 10992, Geospatial Informatics IX, 1099203 (13 May 2019); doi: 10.1117/12.2518179
Show Author Affiliations
Marc Bosch, Johns Hopkins Univ. Applied Physics Lab., LLC (United States)
Rodrigo Rene Rai Munoz Abujder, Johns Hopkins Univ. Applied Physics Lab., LLC (United States)
Christopher Gifford, Johns Hopkins Univ. Applied Physics Lab., LLC (United States)

Published in SPIE Proceedings Vol. 10992:
Geospatial Informatics IX
Kannappan Palaniappan; Peter J. Doucette; Gunasekaran Seetharaman, Editor(s)

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