Title
OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation
Abstract
The Sentinel-1 mission provides a freely accessible opportunity for urban image interpretation based on synthetic aperture radar (SAR) data with a specific resolution, which is of paramount importance for Earth observation. In parallel, with the rapid development of advanced technologies, especially deep learning, we urgently need a large-scale SAR dataset supporting urban image interpretation. This article presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OpenSARUrban</italic> : a Sentinel-1 dataset dedicated to the content-related interpretation of urban SAR images, including a well-defined hierarchical annotation scheme, data collection, well-established procedures for dataset compilation and organization as well as properties, visualizations, and applications of this dataset. Particularly, our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OpenSARUrban</italic> collection provides 33 358 image patches of urban SAR scenes, covering 21 major cities of China, including 10 different target area categories, 4 kinds of data formats, 2 kinds of polarization modes, and owning 5 essential properties: large-scale coverage, diversity, specificity, reliability, and sustainability. These properties guarantee the achievement of several goals for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OpenSARUrban</italic> . The first one is to support urban target characterization. The second one is to help develop well-applicable and advanced algorithms for Sentinel-1 urban target classification. The third one is to explore content-based image retrieval for these kinds of data. In addition, dataset visualization is implemented from the perspective of manifolds to give an intuitive understanding. Besides a detailed description and visualization of the dataset, we present results of some benchmarking algorithms, demonstrating that this dataset is practical and challenging. Notably, developing algorithms to enhance the classification performance on the whole dataset and considering the data imbalance are especially demanding.
Year
DOI
Venue
2020
10.1109/JSTARS.2019.2954850
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Keywords
DocType
Volume
OpenSARUrban,Sentinel-1 dataset,synthetic aperture radar (SAR),urban interpretation
Journal
13
ISSN
Citations 
PageRank 
1939-1404
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Juanping Zhao1181.50
Zenghui Zhang25010.29
Wei Yao300.34
Mihai Datcu4893111.62
Huilin Xiong584.16
Wenxian Yu632943.89