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Deep Learning Enhanced Fidelity InSAR Toolkit (DEFIT), Phase I

Completed Technology Project

Project Introduction

Lynntech, in collaboration with Southern Methodist University (SMU) Earth Sciences, proposes to develop a new deep learning-based toolkit that is useful for enhancing the fidelity of results derived from Interferometric Synthetic Aperture Radar (InSAR) interferograms. The automated deep learning tool performs a spatial–temporal analysis of multiple InSAR images, to yield a high fidelity estimate of the deformation of the topography and estimate of atmospheric water vaper when a recent Digital Elevation Model is also known. There are existing methods used by earth science experts to detect and mitigate the atmospheric anomaly that effects the time of flight of backscattered radar, either from multiple InSAR images or when integrating other sources of elevation or meteorological observations or models.  The automated image reconstruction algorithm will minimize a loss function, an inferred empirical error based on a large sample set, rather than the heuristic or incompletely modeled statistical algorithms currently employed, through a three-step process: first detect the regions affected by the atmospheric anomaly, and then second without a-priori knowledge use a generative network to reconstruct the interferogram or deformation map without the atmospheric effect, and use another network to train the loss function to evaluate the generator's result and adjust its internal parameters. This type of approach has not been implemented for InSAR imagery, but has been applied to similar image processing problems and generalized to other tasks. This tool is meant for big data analysis of very fast revisit InSAR that covers the entire globe. Lynntech and SMU-Earth Science propose to develop and validate this approach for developing a new image processing tool in Phase I , while developing the deep learning enhanced fidelity InSAR toolkit in Phase II and III, raising the TRL from 2 to 4 within the Phase I work plan and planning for testing on relevant datasets in Phase II.

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