Leveraging mixed data sources for enhanced road segmentation in synthetic aperture radar images
In this study, we tackle the task of road segmentation from Synthetic Aperture Radar (SAR) imagery, which is vital for remote sensing applications including urban planning and disaster management. Despite its significance, SAR-based road segmentation is hindered by the scarcity of high-resolution, a...
Saved in:
Main Authors: | , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/180516 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-180516 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1805162024-10-11T15:40:51Z Leveraging mixed data sources for enhanced road segmentation in synthetic aperture radar images Lan, Tian He, Shuting Qing, Yuanyuan Wen, Bihan School of Electrical and Electronic Engineering Engineering Geographic information Transportation In this study, we tackle the task of road segmentation from Synthetic Aperture Radar (SAR) imagery, which is vital for remote sensing applications including urban planning and disaster management. Despite its significance, SAR-based road segmentation is hindered by the scarcity of high-resolution, annotated SAR datasets and the distinct characteristics of SAR imagery, which differ significantly from more commonly used electro-optical (EO) imagery. To overcome these challenges, we introduce a multi-source data approach, creating the HybridSAR Road Dataset (HSRD). This dataset includes the SpaceNet 6 Road (SN6R) dataset, derived from high-resolution SAR images and OSM road data, as well as the DG-SAR and SN3-SAR datasets, synthesized from existing EO datasets. We adapt an off-the-shelf road segmentation network from the optical to the SAR domain through an enhanced training framework that integrates both real and synthetic data. Our results demonstrate that the HybridSAR Road Dataset and the adapted network significantly enhance the accuracy and robustness of SAR road segmentation, paving the way for future advancements in remote sensing. Published version 2024-10-10T01:05:22Z 2024-10-10T01:05:22Z 2024 Journal Article Lan, T., He, S., Qing, Y. & Wen, B. (2024). Leveraging mixed data sources for enhanced road segmentation in synthetic aperture radar images. Remote Sensing, 16(16), 3024-. https://dx.doi.org/10.3390/rs16163024 2072-4292 https://hdl.handle.net/10356/180516 10.3390/rs16163024 2-s2.0-85202441082 16 16 3024 en Remote Sensing © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering Geographic information Transportation |
spellingShingle |
Engineering Geographic information Transportation Lan, Tian He, Shuting Qing, Yuanyuan Wen, Bihan Leveraging mixed data sources for enhanced road segmentation in synthetic aperture radar images |
description |
In this study, we tackle the task of road segmentation from Synthetic Aperture Radar (SAR) imagery, which is vital for remote sensing applications including urban planning and disaster management. Despite its significance, SAR-based road segmentation is hindered by the scarcity of high-resolution, annotated SAR datasets and the distinct characteristics of SAR imagery, which differ significantly from more commonly used electro-optical (EO) imagery. To overcome these challenges, we introduce a multi-source data approach, creating the HybridSAR Road Dataset (HSRD). This dataset includes the SpaceNet 6 Road (SN6R) dataset, derived from high-resolution SAR images and OSM road data, as well as the DG-SAR and SN3-SAR datasets, synthesized from existing EO datasets. We adapt an off-the-shelf road segmentation network from the optical to the SAR domain through an enhanced training framework that integrates both real and synthetic data. Our results demonstrate that the HybridSAR Road Dataset and the adapted network significantly enhance the accuracy and robustness of SAR road segmentation, paving the way for future advancements in remote sensing. |
author2 |
School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Lan, Tian He, Shuting Qing, Yuanyuan Wen, Bihan |
format |
Article |
author |
Lan, Tian He, Shuting Qing, Yuanyuan Wen, Bihan |
author_sort |
Lan, Tian |
title |
Leveraging mixed data sources for enhanced road segmentation in synthetic aperture radar images |
title_short |
Leveraging mixed data sources for enhanced road segmentation in synthetic aperture radar images |
title_full |
Leveraging mixed data sources for enhanced road segmentation in synthetic aperture radar images |
title_fullStr |
Leveraging mixed data sources for enhanced road segmentation in synthetic aperture radar images |
title_full_unstemmed |
Leveraging mixed data sources for enhanced road segmentation in synthetic aperture radar images |
title_sort |
leveraging mixed data sources for enhanced road segmentation in synthetic aperture radar images |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/180516 |
_version_ |
1814047357367484416 |