Spatial processing techniques for satellite altimetry applications in continental hydrology

2013 ◽  
Author(s):  
Philippe Maillard ◽  
Stéphane Calmant
Keyword(s):  
Satellite Altimetry ◽  
Spatial Processing ◽  
Continental Hydrology ◽  
Processing Techniques

scholarly journals Comparison of spatial processing techniques for sonar array data

1988 ◽  
Vol 84 (S1) ◽  
pp. S16-S16
Author(s):  
David V. Wyllie ◽  
Brian G. Ferguson ◽  
Garry C. Speechley
Keyword(s):  
Spatial Processing ◽  
Array Data ◽  
Processing Techniques

Self-validating deep learning of continental hydrology through satellite gravimetry and altimetry

2021 ◽  
Author(s):  
Christopher Irrgang ◽  
Jan Saynisch-Wagner ◽  
Robert Dill ◽  
Eva Boergens ◽  
Maik Thomas
Keyword(s):  
Neural Network ◽  
Satellite Altimetry ◽  
Water Cycle ◽  
Global Water Cycle ◽  
Network Training ◽  
The Neural Network ◽  
Continental Hydrology ◽  
Terrestrial Water ◽  
Combine Machine ◽  
Groundwater Basins

<p>Space-borne observations of terrestrial water storage (TWS) are an essential ingredient for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. However, the complex distribution of water masses in rivers, lakes, or groundwater basins remains elusive in coarse-resolution gravimetry observations. We combine machine learning, numerical modeling, and satellite altimetry to build and train a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. The neural network is designed to adapt and validate its training progress by considering independent satellite altimetry records. We show that the neural network can accurately derive TWS anomalies in 2019 after being trained over the years 2003 to 2018. Specifically for validated regions in the Amazonas, we highlight that the neural network can outperform the numerical hydrology model used in the network training.</p><p>https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL089258</p>

From satellite altimetry to ocean topography A survey of data processing techniques

1988 ◽  
Vol 9 (10-11) ◽  
pp. 1797-1818 ◽  
Author(s):  
K. F. WAKKER ◽  
R. C. A. ZANDBERGEN ◽  
G. H. M. VAN GELDORP ◽  
B. A. C. AMBROSIUS
Keyword(s):  
Data Processing ◽  
Satellite Altimetry ◽  
Ocean Topography ◽  
Processing Techniques

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

Digital phase analysis in interference electron microscopy

1988 ◽  
Vol 46 ◽  
pp. 842-843
Author(s):  
S. Hasegawa ◽  
T. Kawasaki ◽  
J. Endo ◽  
M. Futamoto ◽  
A. Tonomura
Keyword(s):  
Magnetic Field ◽  
Electron Microscopy ◽  
Phase Distribution ◽  
Magnetic Recording Media ◽  
Electron Wave ◽  
Vector Potentials ◽  
Weak Magnetic Fields ◽  
Leakage Magnetic Field ◽  
Optical Reconstruction ◽  
Processing Techniques

Interference electron microscopy enables us to record the phase distribution of an electron wave on a hologram. The distribution is visualized as a fringe pattern in a micrograph by optical reconstruction. The phase is affected by electromagnetic potentials; scalar and vector potentials. Therefore, the electric and magnetic field can be reduced from the recorded phase. This study analyzes a leakage magnetic field from CoCr perpendicular magnetic recording media. Since one contour fringe interval corresponds to a magnetic flux of Φo(=h/e=4x10-15Wb), we can quantitatively measure the field by counting the number of finges. Moreover, by using phase-difference amplification techniques, the sensitivity for magnetic field detection can be improved by a factor of 30, which allows the drawing of a Φo/30 fringe. This sensitivity, however, is insufficient for quantitative analysis of very weak magnetic fields such as high-density magnetic recordings. For this reason we have adopted “fringe scanning interferometry” using digital image processing techniques at the optical reconstruction stage. This method enables us to obtain subfringe information recorded in the interference pattern.

Specific Labeling of Protein Domains with Antibody Fragments

1981 ◽  
Vol 39 ◽  
pp. 416-417
Author(s):  
U. Aebi ◽  
L.E. Buhle ◽  
W.E. Fowler
Keyword(s):  
Image Processing ◽  
Specific Protein ◽  
Antibody Fragments ◽  
Supramolecular Organization ◽  
Two Dimensional ◽  
Ordered Structures ◽  
Virus Capsids ◽  
Processing Techniques

Many important supramolecular structures such as filaments, microtubules, virus capsids and certain membrane proteins and bacterial cell walls exist as ordered polymers or two-dimensional crystalline arrays in vivo. In several instances it has been possible to induce soluble proteins to form ordered polymers or two-dimensional crystalline arrays in vitro. In both cases a combination of electron microscopy of negatively stained specimens with analog or digital image processing techniques has proven extremely useful for elucidating the molecular and supramolecular organization of the constituent proteins. However from the reconstructed stain exclusion patterns it is often difficult to identify distinct stain excluding regions with specific protein subunits. To this end it has been demonstrated that in some cases this ambiguity can be resolved by a combination of stoichiometric labeling of the ordered structures with subunit-specific antibody fragments (e.g. Fab) and image processing of the electron micrographs recorded from labeled and unlabeled structures.

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