Preprints

Filtering by Subject: Computer Engineering

River Planform Extraction From High-Resolution SAR Images Via Generalised Gamma Distribution Superpixel Classification

Odysseas Pappas, Nantheera Anantrasirichai, Alin Achim, Byron Adams

Published: 2020-10-21
Subjects: Computer Engineering, Electrical and Computer Engineering, Engineering, Geology, Geomorphology, Hydrology

The extraction of river planforms from remotely sensed satellite images is a task of crucial importance to many applications such as land planning, water resource monitoring or flood prediction. In this paper we present a novel framework for the extraction of rivers from Synthetic Aperture Radar (SAR) images, based on superpixel segmentation and subsequent classification. Superpixel segmentation [...]

A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources

Muhammed Ali Sit, Bekir Zahit Demiray, Zhongrun Xiang, Gregory Ewing, Yusuf Sermet, Ibrahim Demir

Published: 2020-06-19
Subjects: Civil and Environmental Engineering, Computer Engineering, Engineering, Environmental Engineering

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety, and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these [...]

D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

Bekir Zahit Demiray, Muhammed Ali Sit, Ibrahim Demir

Published: 2020-04-18
Subjects: Civil and Environmental Engineering, Computer Engineering, Engineering

LIDAR (light detection and ranging) is an optical remote-sensing technique that measures the distance between sensor and object, and the reflected energy from the object. Over the years, LIDAR data has been used as the primary source of Digital Elevation Models (DEMs). DEMs have been used in a variety of applications like road extraction, hydrological modeling, flood mapping, and surface [...]

Interferometric Processing of ScanSAR Data Using Stripmap Processor: New Insights from Coregistration

Cunren Liang, Eric Jameson Fielding

Published: 2020-04-14
Subjects: Aerospace Engineering, Civil and Environmental Engineering, Computer Engineering, Earth Sciences, Electrical and Computer Engineering, Engineering, Geology, Geophysics and Seismology, Glaciology, Hydrology, Mining Engineering, Other Earth Sciences, Physical Sciences and Mathematics, Tectonics and Structure, Volcanology

Processing scanning synthetic aperture radar (ScanSAR) data using a stripmap processor, which is called full-aperture processing, has been the choice of many researchers. ScanSAR data are known to require very high azimuth coregistration precision which is usually achieved by a geometrical coregistration followed by a spectral diversity coregistration on the ScanSAR burst. However, for [...]

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 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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