scholarly journals Comparing C- and L-band SAR images for sea ice motion estimation

2015 ◽  
Vol 9 (1) ◽  
pp. 357-366 ◽  
Author(s):  
J. Lehtiranta ◽  
S. Siiriä ◽  
J. Karvonen
Keyword(s):  
Motion Estimation ◽  
Sea Ice ◽  
Cross Correlation ◽  
Correlation Technique ◽  
Sar Images ◽  
L Band ◽  
The Baltic ◽  
Cross Correlation Technique ◽  
Multiyear Ice ◽  
Band Image

Abstract. Pairs of consecutive C-band synthetic-aperture radar (SAR) images are routinely used for sea ice motion estimation. The L-band radar has a fundamentally different character, as its longer wavelength penetrates deeper into sea ice. L-band SAR provides information on the seasonal sea ice inner structure in addition to the surface roughness that dominates C-band images. This is especially useful in the Baltic Sea, which lacks multiyear ice and icebergs, known to be confusing targets for L-band sea ice classification. In this work, L-band SAR images are investigated for sea ice motion estimation using the well-established maximal cross-correlation (MCC) approach. This work provides the first comparison of L-band and C-band SAR images for the purpose of motion estimation. The cross-correlation calculations are hardware accelerated using new OpenCL-based source code, which is made available through the author's web site. It is found that L-band images are preferable for motion estimation over C-band images. It is also shown that motion estimation is possible between a C-band and an L-band image using the maximal cross-correlation technique.

scholarly journals Comparing C- and L-band SAR images for sea ice motion estimation

2014 ◽  
Vol 8 (3) ◽  
pp. 2721-2757
Author(s):  
J. Lehtiranta ◽  
S. Siiriä ◽  
J. Karvonen
Keyword(s):  
Motion Estimation ◽  
Sea Ice ◽  
Cross Correlation ◽  
Correlation Technique ◽  
Sar Images ◽  
L Band ◽  
Sar Imagery ◽  
The Baltic ◽  
Cross Correlation Technique ◽  
Multiyear Ice

Abstract. Pairs of consecutive C-band SAR images are routinely used for sea ice motion estimation. In addition to the surface roughness L-band SAR imagery provides information of the seasonal sea ice inner structure, which is especially useful in the Baltic Sea lacking multiyear ice and icebergs. In this work, L-band SAR images are investigated for sea ice motion estimation using the well-established maximal cross-correlation approach. This work provides the first comparison of L-band and C-band SAR images for the purpose of motion estimation. The cross-correlation calculations are hardware accelerated using new OpenCL-based source code, which is made available through the author's web site. It is found that L-band images are preferable for motion estimation over C-band images. It is also shown that motion estimation is possible between a C-band and an L-band image using the maximal cross-correlation technique.

scholarly journals SuperCLASS – I. The super cluster assisted shear survey: Project overview and data release 1

2020 ◽  
Vol 495 (2) ◽  
pp. 1706-1723 ◽  
Author(s):  
Richard A Battye ◽  
Michael L Brown ◽  
Caitlin M Casey ◽  
Ian Harrison ◽  
Neal J Jackson ◽  
...  
Keyword(s):  
Cross Correlation ◽  
Near Infrared ◽  
Correlation Technique ◽  
Large Array ◽  
Photometric Redshifts ◽  
Very Large Array ◽  
Infrared Observations ◽  
Data Release ◽  
L Band ◽  
Cross Correlation Technique

ABSTRACT The SuperCLuster Assisted Shear Survey (SuperCLASS) is a legacy programme using the e-MERLIN interferometric array. The aim is to observe the sky at L-band (1.4 GHz) to a r.m.s. of $7\, \mu {\rm Jy}\,$beam−1 over an area of $\sim 1\, {\rm deg}^2$ centred on the Abell 981 supercluster. The main scientific objectives of the project are: (i) to detect the effects of weak lensing in the radio in preparation for similar measurements with the Square Kilometre Array (SKA); (ii) an extinction free census of star formation and AGN activity out to z ∼ 1. In this paper we give an overview of the project including the science goals and multiwavelength coverage before presenting the first data release. We have analysed around 400 h of e-MERLIN data allowing us to create a Data Release 1 (DR1) mosaic of $\sim 0.26\, {\rm deg}^2$ to the full depth. These observations have been supplemented with complementary radio observations from the Karl G. Jansky Very Large Array (VLA) and optical/near infrared observations taken with the Subaru, Canada-France-Hawaii, and Spitzer Telescopes. The main data product is a catalogue of 887 sources detected by the VLA, of which 395 are detected by e-MERLIN and 197 of these are resolved. We have investigated the size, flux, and spectral index properties of these sources finding them compatible with previous studies. Preliminary photometric redshifts, and an assessment of galaxy shapes measured in the radio data, combined with a radio-optical cross-correlation technique probing cosmic shear in a supercluster environment, are presented in companion papers.

scholarly journals Masked Correlation for Improvement of 2D Glacier Motion Estimation Based on Terrasar-x Imagery

2014 ◽  
Vol II-7 ◽  
pp. 9-13
Author(s):  
L. Fang ◽  
U. Stilla
Keyword(s):  
High Resolution ◽  
Motion Estimation ◽  
Cross Correlation ◽  
Correlation Method ◽  
Correlation Technique ◽  
Binary Mask ◽  
Normalized Cross Correlation ◽  
The Law ◽  
Cross Correlation Technique ◽  
Glacier Motion

In this paper, the impacts of the non-glacier information in the template window used in cross-correlation calculation for 2D motion estimation of glaciers are discussed and illustrated by the example of the Taku glacier, which is the biggest glacier in the Juneau Icefield, Alaska. For this, the glacier motion maps are extracted by the traditional normalized cross-correlation technique and the masked cross-correlation method, which uses a manually generated binary mask to threshold the non-glacier pixels, based on geocoded high resolution TerraSAR-X images. Based on the comparison of the different results, it was found that without the disturbing information (e.g., mountain, water) the accuracy of the cross-correlation of sequential patches in masked cross-correlation method is improved and the estimation results are much more reasonable, which respect the law that glacier flows like a river with higher velocity in the middle and than that in the sides.

scholarly journals The emergence of the red sequence at z~2 seen through galaxy clustering in the UKIDSS UDS

2012 ◽  
Vol 8 (S295) ◽  
pp. 105-108
Author(s):  
William G. Hartley ◽  
Omar Almaini ◽  
Alice Mortlock ◽  
Chris Conselice ◽  
Keyword(s):  
Dark Matter ◽  
Cross Correlation ◽  
Near Infrared ◽  
Correlation Technique ◽  
Dark Matter Halos ◽  
Star Forming ◽  
Deep Survey ◽  
Cross Correlation Technique ◽  
Infrared Survey

AbstractWe use the UKIDSS Ultra-Deep Survey, the deepest degree-scale near-infrared survey to date, to investigate the clustering of star-forming and passive galaxies to z ~ 3.5. Our new measurements include the first determination of the clustering for passive galaxies at z > 2, which we achieve using a cross-correlation technique. We find that passive galaxies are the most strongly clustered, typically hosted by massive dark matter halos with Mhalo > 1013 M⊙ irrespective of redshift or stellar mass. Our findings are consistent with models in which a critical halo mass determines the transition from star-forming to passive galaxies.

scholarly journals Operational SAR-based sea ice drift monitoring over the Baltic Sea

Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 473-483 ◽  
Author(s):  
J. Karvonen
Keyword(s):  
Sea Ice ◽  
Baltic Sea ◽  
Vector Fields ◽  
Phase Correlation ◽  
The Baltic Sea ◽  
Sar Images ◽  
Envisat Asar ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
The Baltic

Abstract. An algorithm for computing ice drift from pairs of synthetic aperture radar (SAR) images covering a common area has been developed at FMI. The algorithm has been developed based on the C-band SAR data over the Baltic Sea. It is based on phase correlation in two scales (coarse and fine) with some additional constraints. The algorithm has been running operationally in the Baltic Sea from the beginning of 2011, using Radarsat-1 ScanSAR wide mode and Envisat ASAR wide swath mode data. The resulting ice drift fields are publicly available as part of the MyOcean EC project. The SAR-based ice drift vectors have been compared to the drift vectors from drifter buoys in the Baltic Sea during the first operational season, and also these validation results are shown in this paper. Also some navigationally useful sea ice quantities, which can be derived from ice drift vector fields, are presented.

Raman Chemical Imaging of Explosive-Contaminated Fingerprints

2009 ◽  
Vol 63 (11) ◽  
pp. 1197-1203 ◽  
Author(s):  
E. D. Emmons ◽  
A. Tripathi ◽  
J. A. Guicheteau ◽  
S. D. Christesen ◽  
A. W. Fountain
Keyword(s):  
Cross Correlation ◽  
Chemical Imaging ◽  
Correlation Technique ◽  
Bright Field ◽  
Characteristic Region ◽  
Biometric Analysis ◽  
Imaging Results ◽  
Raman Chemical Imaging ◽  
Cross Correlation Technique ◽  
Field Imaging

Raman chemical imaging (RCI) has been used to detect and identify explosives in contaminated fingerprints. Bright-field imaging is used to identify regions of interest within a fingerprint, which can then be examined to determine their chemical composition using RCI and fluorescence imaging. Results are presented where explosives in contaminated fingerprints are identified and their spatial distributions are obtained. Identification of explosives is obtained using Pearson's cosine cross-correlation technique using the characteristic region (500–1850 cm−1) of the spectrum. This study shows the ability to identify explosives nondestructively so that the fingerprint remains intact for further biometric analysis. Prospects for forensic examination of contaminated fingerprints are discussed.

Optoelectronic sensor applied to airflow velocity measurements through cross-correlation technique

2021 ◽  
Author(s):  
Alexandre Allil ◽  
Fábio Dutra ◽  
Cesar Cosenza Carvalho ◽  
Alex Dante ◽  
Regina Allil ◽  
...  
Keyword(s):  
Cross Correlation ◽  
Correlation Technique ◽  
Velocity Measurements ◽  
Airflow Velocity ◽  
Optoelectronic Sensor ◽  
Cross Correlation Technique
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