Monitoring of rivers is of major scientific and societal importance, due to the crucial resource they provide to human activities and the threats caused by flood events. Rapid revisit Synthetic Aperture Radar (SAR) sensors such as Sentinel-1 or the future Surface Water and Ocean Topography (SWOT) mission are indispensable tools to achieve all-weather monitoring of water bodies at the global scale. Unfortunately, at the spatial resolution of these sensors, the extraction of narrow rivers is extremely difficult without resorting to exogenous knowledge. This paper introduces an innovative river segmentation method from SAR images using a priori databases such as the Global River Widths from Landsat (GRWL). First, a recently proposed linear structure detector is used to produce a map of likely line structures. Then, a limited number of nodes along the prior river centerline are extracted from the exogenous database, and used to reconstruct the full river centerline from the detection map. Finally, an innovative conditional random field approach is used to delineate accurately the river extent around its centerline. The proposed method has been tested on several Sentinel-1 images and on simulated SWOT data. Both visual and qualitative evaluations demonstrate its efficiency.
The increasing availability of SAR time series creates many opportunities for remote sensing applications, but it can be challenging in terms of amount of data to process. This letter discusses the interest of the geometric mean to average SAR time series. First, the properties of the geometric mean and of the arithmetic mean are compared. Then, a speckle-reduction method specifically designed to improve images obtained with the geometric mean is presented. This method is based on an adaptation of the MuLoG framework to take into account the specific distribution of the geometric mean. Finally, applications of this denoised geometric-mean image are presented.
This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy. Training the deep neural network on collections of Sentinel 1 GRD images leads to a despeckling algorithm that is robust to space-variant spatial correlations of speckle. Despeckled images improve the detection of structures like narrow rivers. We apply a detector based on exogenous information and a linear features detector and show that rivers are better segmented when the processing chain is applied to images pre-processed by our despeckling neural network.
<p>The hydroweb.next platform is an open-data thematic hub for hydrology. It aims to foster new uses of remote sensing data for water applications by removing the main barriers: data formatting issues, dispersal of access points, and data processing costs,&#8230;</p> <p><br />Hydroweb.next has been funded by the French government in the frame of Theia (Data and Services center for continental surfaces) and SWOT downstream (Surface Water and Ocean Topography satellite) programs. The hub brings together products from various providers such as Copernicus Land Services along with products from its own production centers. The production centers operate state-of-the-art algorithms that have been developed with scientists from Theia&#8217;s Scientific Expertise Centers: SurfWater for Surface Water Extent (SWE) from Sentinel-1 and Sentinel-2 images, Let It Snow for fractional snow cover and OBS2CO for water quality from Sentinel-2 images. As of June 2023, these 3 products will be made available with a 5 million square kilometer coverage. Products from SWOT and Trishna missions will also be distributed by hydroweb.next as they become available.&#160;<br />In late 2023, SWOT data will include high-level user-oriented products such as river discharges and lake storage changes with global coverage. In 2025, Trishna products will include water quality, water skin temperature, and evapotranspiration. In situ data are also available to allow comparison with satellite data.</p> <p>The products are distributed using STAC (Spatio-Temporal Asset Catalog) and WMS/WMTS (Web Mapping Services) protocols that follow the FAIR principles. This enables the direct reuse of the data by other services (e.g. UNESCO&#8217;s water quality portals).</p> <p>The WebGIS interface is designed following a User-Centered Development approach. By involving users from various backgrounds such as Water Agencies, NGOs, industry, or academic research in stages of the project: surveys of user needs during interviews, features design involving users, ergonomics improvement through alpha testing, and quick consideration of user feedbacks through continuous integration and deployment. The interface allows searching relevant data using keywords, geophysical variables, and space-time restrictions. It also allows visualizing the products, their temporal evolution, and multitemporal synthesis. Finally, it allows downloading, harvesting, or streaming data, either through the interface or python APIs.</p>
This paper presents a semi-guided method to detect lakes in Sentinel-1 SAR data. The proposed approach is an adaptation of the grab-cut framework developed in [1]. Starting from a coarse bounding box around the lake, an accurate segmentation is extracted using a Conditional Random Field formalism and a graph-cut based optimization. Then an extension of this approach to process jointly a stack of multi-temporal data is presented. A temporal regularization term is introduced to control the joint segmentation.The proposed approach is evaluated on Sentinel-1 datasets. Qualitative and quantitative results demonstrate the interest of the proposed framework and its robustness to the initialization polygon of the lake.
Synthetic aperture radar (SAR) and optical satellite image registration is a field that developed in the last decades and gave rise to a great number of approaches. The registration process is composed of several steps: feature definition, feature comparison and optimization of a geometric transformation between the images. Feature definition can be done using simple traditional filtering or more complex deep learning (DL) methods. In this paper, two traditional approaches and a DL approach are compared. One can then wonder if the complexity of DL is worth to address the registration task. The aim of this paper is to quantitatively compare approaches rooted in distinct methodological areas on two common datasets with different resolutions. The comparison suggests that, although more complex, the DL approach is more precise than traditional methods.
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