Water detection for the SWOT mission (2014 - 2017)
Description
To obtain a better coverage both spatially and temporally, hydrologists use spaceborne data in addition to data acquired in situ. Resulting from a collaboration between NASA’s Jet Propulsion Laboratory (JPL) and the French Space Agency (CNES), the upcoming SWOT mission will provide global continental water elevation measures using Synthetic Aperture Radar (SAR) interferometry. In this project, we addressed the problem of water detection in SWOT amplitude images, which is to be performed before the interferometric processing.
To this end, we propose to use a method dedicated to the detection of large water bodies and a specific algorithm for the detection of narrow rivers. The first method is based on Markov Random Fields (MRF). The classification is regularized and the class parameters, which cannot be assumed constant in the case of SWOT, are jointly estimated. The second method is based on segment detection at the pixel level, completed by a connection step.
One of the main objectives of the surface water and ocean topography (SWOT) mission, scheduled for launch in 2021, is to measure inland water levels using synthetic aperture radar (SAR) interferometry. A key step toward this objective is to precisely detect water areas. In this article, we present a method to detect water in SWOT images. Water is detected based on the relative brightness of the water and nonwater surfaces. Water brightness varies throughout the swath because of system parameters (i.e., the antenna pattern), as well as the phenomenology such as wind speed and surface roughness. To handle the effects of brightness variability, we propose to model the problem with one Markov random field (MRF) on the binary classification map, and two other MRFs to regularize the estimation of the class parameters (i.e., the land and water background power images). Our experiments show that the proposed method is more robust to the expected variations in SWOT images than traditional approaches.
@article{8897698,author={Lobry, Sylvain and Denis, Loïc and Williams, Brent and Fjørtoft, Roger and Tupin, Florence},journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},title={Water Detection in SWOT HR Images Based on Multiple Markov Random Fields},year={2019},volume={12},number={11},pages={4315-4326},url={https://ieeexplore.ieee.org/document/8897698},doi={10.1109/JSTARS.2019.2948788},project={SWOT}}
International Conferences
Double MRF for water classification in SAR images by joint detection and reflectivity estimation
Sylvain Lobry, Loïc Denis, Florence Tupin, and
1 more author
In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
Classification of SAR images is a challenging task as the radiometric properties of a class may not be constant throughout the image. The assumption made in most classification algorithms that a class can be modeled by constant parameters is then not valid. In this paper, we propose a classification algorithm based on two Markov random fields that accounts for local and global variations of the parameters inside the image and produces a regularized classification. This algorithm is applied on airborne TropiSAR and simulated SWOT HR data. Both quantitative and visual results are provided, demonstrating the effectiveness of the proposed method.
@inproceedings{8127445,author={Lobry, Sylvain and Denis, Loïc and Tupin, Florence and Fjortoft, Roger},booktitle={2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},title={Double MRF for water classification in SAR images by joint detection and reflectivity estimation},year={2017},volume={},number={},pages={2283-2286},doi={10.1109/IGARSS.2017.8127445},url={https://ieeexplore.ieee.org/document/8127445},project={SWOT}}
Unsupervised detection of thin water surfaces in SWOT images based on segment detection and connection
Sylvain Lobry, Florence Tupin, and Roger Fjortoft
In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
The objective of the Surface Water and Ocean Topography (SWOT) mission is to regularly monitor the height of the earth’s water surfaces. One of the challenges toward obtaining global measurements of these surfaces is to detect small water areas. In this article we introduce a method for the detection of thin water surfaces, such as rivers, in SWOT images. It combines a low-level step (segment detection) with a high-level regularization of these features. The method is then tested on a simulated SWOT image.
@inproceedings{8127807,author={Lobry, Sylvain and Tupin, Florence and Fjortoft, Roger},booktitle={2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},title={Unsupervised detection of thin water surfaces in SWOT images based on segment detection and connection},year={2017},volume={},number={},pages={3720-3723},doi={10.1109/IGARSS.2017.8127807},url={https://ieeexplore.ieee.org/document/8127807},project={SWOT}}
Non-Uniform Markov Random Fields for Classification of SAR Images
Sylvain Lobry, Florence Tupin, and Roger Fjortoft
In Proceedings of EUSAR 2016: 11th European Conference on Synthetic Aperture Radar, 2016
When dealing with SAR image classification, the class parameters may vary along the swath for several reasons. Traditional classification algorithms are then not well adapted, as they assume constant class parameters. In this paper, we propose a binary classification algorithm based on Markov Random Fields that take into account the parameters variations in the swath, and we present results obtained on airborne TropiSAR and simulated SWOT HR data.
@inproceedings{7559390,author={Lobry, Sylvain and Tupin, Florence and Fjortoft, Roger},booktitle={Proceedings of EUSAR 2016: 11th European Conference on Synthetic Aperture Radar},title={Non-Uniform Markov Random Fields for Classification of SAR Images},year={2016},volume={},number={},pages={1-4},doi={},url={https://ieeexplore.ieee.org/document/7559390},project={SWOT}}
National Conferences
Détection de l’eau dans les images radar du futur satellite SWOT
Sylvain Lobry, Roger Fjortoft, Loı̈c Denis, and
1 more author
One of the objectives of the SWOT mission conducted by CNES and JPL is to obtain a global measurement of water heights. In order to apply an interferometric processing on SWOT images over continents, a first step is to obtain a classification indicating the presence of water. We introduce two methods adapted to the unusual acquisition parameters of the sensor for the detection of compact areas (i.e. lakes) and linear networks (i.e. rivers).
@inproceedings{lobry2017detection,title={D{\'e}tection de l’eau dans les images radar du futur satellite SWOT},author={Lobry, Sylvain and Fjortoft, Roger and Denis, Lo{\"\i}c and Tupin, Florence},booktitle={GRETSI},year={2017},project={SWOT}}