Estimation and analysis of geometric distortions in SAR images
Synthetic Aperture Radar (SAR) is a kind of high-resolution imaging sensor with the advantages of all-day, all-weather. SAR-based analysis is used in many fields and plays an important role because of its characteristic of not being disturbed by weather and climate factors, among which the target...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/174900 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Synthetic Aperture Radar (SAR) is a kind of high-resolution imaging sensor
with the advantages of all-day, all-weather. SAR-based analysis is used in many
fields and plays an important role because of its characteristic of not being disturbed
by weather and climate factors, among which the target detection task
based on SAR is the key to many technologies. However, deep learning requires
a large amount of sample data support, the high cost of obtaining highresolution
SAR data sets brought certain difficulties to the research works in
related fields. Image-to-image (I2I) translation has been extensively applied to
natural images and the great success has attracted researchers in various research
backgrounds. Generative Adversarial Network (GAN), one of the most
successful I2I algorithms, has also brought changes to the data acquisition in
remote sensing domain. By feeding an EO image into a well-trained model, a
synthetic but realistic SAR images can be obtained. But one of the most critical
problems between EO and SAR: geometric distortion, is still a big challenge
for the EO-SAR translation task. This project aims to utilize the prior EO-SAR
translation works to estimate and analyze the geometric distortions between EO
and SAR images. |
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