Sea Wind Vector Estimation Using C-band Full-polarimetric SAR Data

Simple item page
dc.contributor.advisorCollins, Michael
dc.contributor.authorYekkehkhany, Bahareh
dc.contributor.committeememberSkone, Susan
dc.contributor.committeememberO'Keefe, kyle
dc.contributor.committeememberJohn, Yackel
dc.contributor.committeememberBenjamin, Holt
dc.date2022-11
dc.date.accessioned2022-10-04T19:08:03Z
dc.date.available2022-10-04T19:08:03Z
dc.date.issued2022-09-19
dc.description.abstractThis research used neural network (NN) and random forests (RF) models to estimate sea wind speed and direction using synthetic aperture radar (SAR) data. We used RADARSAT (RS)-2 C-band single look complex (SLC) fine quad-polarimetric data and buoy measurements. After data preparation, SAR data are paired with their relevant buoy observation. Then, SAR data parameters expected to be impacted by sea wind are generated. While the spatial resolution of our RS-2 data is 4.7 × 5.1 m, we cropped each image to a chip of 512 × 512 pixels centred by its related buoy and averaged the parameters over the image chip. Therefore, this study’s estimated wind speed and direction resolution is approximately 2.45 × 2.65km. Then, parameters are separated to train and test data by repeated k-fold cross-validation (CV). Also, successive halving random search CV is used to tune NN and RF hyperparameters. To estimate wind speed, least absolute shrinkage and selection operator (LASSO) feature selection determined the HV polarization normalized radar cross section (NRCS) (σ0H V ) and the real part of the correlation coefficient between HV and VH polarization channels (ℜ(ρHV V H)) as models inputs. The bias, root mean square error (RMSE), and correlation coefficient (CC) between the buoy measured and estimated wind speed by NN are 0.08 m/s, 1.96 m/s, and 0.81, and by RF are 0.01 m/s, 1.94 m/s, and 0.82, respectively. The machine learning models are given all SAR parameters as their inputs in estimating wind direction. First, some data bucketing of wind speed bins of 5 m/s and incidence angle bins of 11◦ is done. Finally, the models’ evaluations are based on their performance on each data bucket and the whole dataset by calculating the weighted average of all the data buckets. Then, the bias, RMSE, and CC between the buoy measured and estimated wind direction by NN are −0.69◦, 31.25◦, and 0.58, and by RF are −0.03◦, 25.73◦, and 0.77, respectively. Finally, a permutation feature importance is applied to the trained wind direction models. The imaginary parts of the correlation coefficient between cross- and co-polarization channels, ℑ(ρHHHV ), ℑ(ρHHV H), ℑ(ρV V HV ), and ℑ(ρV V V H), play significant roles in building both NN and RF models.en_US
dc.identifier.citationYekkehkhany, B. (2022). Sea wind vector estimation using C-band full-polarimetric SAR data (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.urihttp://hdl.handle.net/1880/115344
dc.identifier.urihttps://dx.doi.org/10.11575/PRISM/40344
dc.language.isoengen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectEarth Observationen_US
dc.subjectSynthetic Aperture Radaren_US
dc.subjectMachine Learningen_US
dc.subjectNeural Networken_US
dc.subjectRandom Forestsen_US
dc.subjectPolarimetric Parametersen_US
dc.subjectSatellite Imageryen_US
dc.subjectWind Estimationen_US
dc.subjectMarine Applicationen_US
dc.subject.classificationOceanographyen_US
dc.subject.classificationRemote Sensingen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.subject.classificationEngineering--Environmentalen_US
dc.subject.classificationEngineering--Marine and Oceanen_US
dc.titleSea Wind Vector Estimation Using C-band Full-polarimetric SAR Dataen_US
dc.typedoctoral thesisen_US
thesis.degree.disciplineEngineering – Geomaticsen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameDoctor of Philosophy (PhD)en_US
ucalgary.item.requestcopytrueen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ucalgary_2022_yekkehkhany_bahareh.pdf
Size:
4.51 MB
Format:
Adobe Portable Document Format
Description:
PhD Dissertation
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Open Theses and Dissertations