Preprints

Filtering by Subject: Computer Engineering

Measuring Azimuth Deformation With L-Band ALOS-2 ScanSAR Interferometry

Cunren Liang, Eric Jameson Fielding

Published: 2020-04-06
Subjects: Aerospace Engineering, Civil and Environmental Engineering, Computational Engineering, Computer Engineering, Earth Sciences, Electrical and Computer Engineering, Engineering, Geology, Geomorphology, Geophysics and Seismology, Glaciology, Hydrology, Mining Engineering, Other Earth Sciences, Physical Sciences and Mathematics, Signal Processing, Tectonics and Structure, Volcanology

We analyze the methods for measuring azimuth deformation with the L-band Advanced Land Observing Satellite-2 (ALOS-2) scanning synthetic aperture radar (ScanSAR) interferometry. To implement the methods, we extract focused bursts from the ALOS-2 full-aperture product, which is the only product available for ScanSAR interferometry at present. The extracted bursts are properly processed to measure [...]

Evaluation of open-access global digital elevation models (AW3D30, SRTM and ASTER) for flood modelling purposes

Laurent Courty, Julio César Soriano-Monzalvo, Adrián Pedrozo-Acuña

Published: 2018-06-25
Subjects: Aerospace Engineering, Civil and Environmental Engineering, Computational Engineering, Computer Engineering, Earth Sciences, Engineering, Environmental Sciences, Hydraulic Engineering, Hydrology, Life Sciences, Physical Sciences and Mathematics, Water Resource Management

Digital Elevation Models (DEM) are a key piece of information for the accurate representation of topographic controls exerted in hydrologic and hydraulic models. Many practitioners rely on open-access global datasets usually obtained from space-borne survey due to the cost and sparse coverage of sources of higher resolution. In may 2016 the Japan Aerospace eXploration Agency publicly released an [...]

Landscape classification with deep neural networks.

Daniel David Buscombe

Published: 2018-06-18
Subjects: Computer and Systems Architecture, Computer Engineering, Earth Sciences, Engineering, Environmental Monitoring, Environmental Sciences, Geology, Geomorphology, Other Statistics and Probability, Physical Sciences and Mathematics, Statistics and Probability

The application of deep learning, specifically deep convolutional neural networks (DCNNs), to the classification of remotely sensed imagery of natural landscapes has the potential to greatly assist in the analysis and interpretation of geomorphic processes. However, the general usefulness of deep learning applied to conventional photographic imagery at a landscape scale is, at yet, largely [...]

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