1,720,965 research outputs found
Along Track Slope Compensation In A SIMO Formation
Full text linkCompact single-input-multiple-output (SIMO) formations are a promising option for performance improvement in future Synthetic Aperture Radar (SAR) missions. A high-resolution unambiguous imaging is obtained by utilizing several satellites with small antennas. The across-track baseline between the satellites poses a significant issue for known signal reconstruction algorithms due to the dependency on the topography, which may vary along-track. Proposed here is a method to compensate for height variations along the azimuth direction using a simplified but powerful 1-D model. A theoretical derivation of a forward model is provided, and performance analysis in terms of signal to noise is demonstrated
Calibration of SIMO Formations with Azimuth Ambiguities
No full textFormations of small satellites are gaining momentum as an alternative to single synthetic aperture radar (SAR) systems, allowing them to improve performance and reduce costs. Each sensor operates with a low pulse repetition frequency (PRF), so swath size can be kept large, and the strong azimuth ambiguities are later resolved by combining acquisitions from multiple satellites. For proper imaging to be successful, careful calibration of the system and geometric parameters is performed based on the single-channel images. However, ambiguities jeopardize the retrieval of parameters from data; therefore, it is crucial to identify those areas that are inherently ambiguity-free. This article handles acquisitions that are highly affected by azimuth ambiguities, as in the case of the formation. First, we model the replica's structure in the focused image and then propose an effective yet simple method to detect such disturbances. Finally, we demonstrate an improved approach for estimating the along-track baseline by incorporating the aforementioned ambiguity detection scheme. The proposed analysis is validated with simulated data, considering a realistic reflectivity map derived from a COSMO-SkyMed stack
Fast urban land cover mapping exploiting sentinel-1 and sentinel-2 data
Full text linkThe rapid change and expansion of human settlements raise the need for precise remote-sensing monitoring tools. While some Land Cover (LC) maps are publicly available, the knowledge of the up-to-date urban extent for a specific instance in time is often missing. The lack of a relevant urban mask, especially in developing countries, increases the burden on Earth Observation (EO) data users or requires them to rely on time-consuming manual classification. This paper explores fast and effective exploitation of Sentinel-1 (S1) and Sentinel-2 (S2) data for the generation of urban LC, which can be frequently updated. The method is based on an Object-Based Image Analysis (OBIA), where one Multi-Spectral (MS) image is used to define clusters of similar pixels through super-pixel segmentation. A short stack (<2 months) of Synthetic Aperture Radar (SAR) data is then employed to classify the clusters, exploiting the unique characteristics of the radio backscatter from human-made targets. The repeated illumination and acquisition geometry allows defining robust features based on amplitude, coherence, and polarimetry. Data from ascending and descending orbits are combined to overcome distortions and decrease sensitivity to the orientation of structures. Finally, an unsupervised Machine Learning (ML) model is used to separate the signature of urban targets in a mixed environment. The method was validated in two sites in Portugal, with diverse types of LC and complex topography. Comparative analysis was performed with two state-of-the-art high-resolution solutions, which require long sensing periods, indicating significant agreement between the methods (averaged accuracy of around 90%)
Unambiguous Imaging by a Distributed SAR System With Cross-Track Baselines
No full textAlong-track (AT) formations of synthetic aperture radar (SAR) satellites are widely discussed in the literature for their ability to solve the trade-off between resolution and coverage. It is usually assumed that all the satellites in the constellation follow the same orbit with a negligible orbital tube since cross-track (XT) baselines introduce major complexity to high-resolution wide-swath (HRWS) imaging. However, addressing the XT issue is crucial for the feasibility of future formation missions, as realistic orbit control conditions and collision risk considerations will surely impose such baselines. This work discusses HRWS imaging with nonzero XT baselines and derives an algorithm for combining the different channels. The approach differs from traditional methods because the combination is done after focusing, where each target is compressed to several pixels. In this manner, one can address the varying elevation locally. Further performance improvement is obtained by a data-driven definition of the forward model, which utilizes the Matching-Pursuit algorithm to identify the significant ambiguities in each area. The proposed method is highly flexible, as it locally adapts the solution to the actual backscatter and elevation of the scene. Validation and testing were achieved by simulations of X-band acquisition, showing promising results
HIGH-RESOLUTION URBAN MAPPING BY FUSION OF SAR AND OPTICAL DATA
Full text linkMapping the exact extent of urban areas is a critical prerequisite in many remote sensing applications, such as hazard evaluation and change detection. The usage of Synthetical Aperture Radar (SAR) data has gained popularity due to the unique characteristics of the backscattered radio signal from human-made targets. The Sentinel-1 (S1) constellation, with a global revisit time of 6–12 days in Interferometric Wide Swath (IW) mode and free and open access to the data, allows the development of new applications to monitor urban sites. However, S1 is rarely considered when fine resolution is required due to the large pixel size and the need for spatial averaging to obtain robust estimators. We propose a method to improve Sentinel-1 urban classification performance by exploiting one Multi-Spectral (MS) image acquired by Sentinel-2 (S2). MS data is used for tracing the precise natural boundaries in a scene through superpixels segmentation. A machine learning approach is then applied to interpret the thematic context of each segment from short temporal stacks of coregistered SAR data. We use a short sensing period (around two months), so rapid changes can be traces. The proposed fusion of S1 and S2 data was tested in the area of Milan (Italy), with a total accuracy of about 90%. The ability to follow high-resolution details in a mixed environment is demonstrated, opening the possibility of efficiently tracing the human footprint
SAR-BASED COASTLINE DETECTION AND MONITORING
Full text linkThe coastal environment is among the most fragile regions on our planet. Its efficient monitoring is crucial to properly manage human and natural resources located in this environment where a large portion of our population lives. The objective of this contribution is to design and develop a new set of methods suitable for detecting and tracking the coastline. Synthetic aperture radar (SAR) technology is chosen because of the characteristic response from water and the acquisition consistency allowed by constant illumination, day-and-night, and all-weather functioning. The proposed iterative detection method is based on superpixel segmentation. The resulting superpixels are filtered and then partitioned in land and water classes based on their median backscattering with Otsu’s algorithm. The rationale is that the segmentation can follow the coastline before the filtering can degrade the spatial resolution. A quantitative assessment of the results measures the distance to a manually-detected shoreline for the Lizard Island case study; the average distance is 12.63 m, with 80% of the sampled points within 20 m. The innovative coastline monitoring process exploits the consistency of SAR by analyzing a long time series. After a season-wise grouping, the land-water index is introduced to erase the time oscillation of water backscattering caused by different sea states. The proposed index is modeled in time on a pixel basis. A visualization technique that exploits the HSV codification of the color space highlights where and when changes happened. A case study for this technique is carried out over the Reentrancias Maranhenses natural area. A quality assessment shows good accordance with optical data that depicts the region’s dynamic
SAR sensing of the atmosphere: stack-based processing for tropospheric and ionospheric phase retrieval
Full text linkThis paper is intended to summarize the research conducted during the first 2 years of the Dragon 5 project 59,332 (geophysical and atmospheric retrieval from Synthetic Aperture Radar (SAR) data stacks over natural scenarios). Monitoring atmospheric phenomena, encompassing both tropospheric and ionospheric conditions, holds pivotal significance for various scientific and practical applications. In this paper, we present an exploration of advanced techniques for estimating tropospheric and ionospheric phase screens using stacks of Synthetic Aperture Radar (SAR) images. Our study delves into the current state-of-the-art in atmospheric monitoring with a focus on spaceborne SAR systems, shedding light on their evolving capabilities. For tropospheric phase screen estimation, we propose a novel approach that jointly estimates the tropospheric component from all the images. We discuss the methodology in detail, highlighting its ability to recover accurate tropospheric maps. Through a series of quantitative case studies using real Sentinel-1 satellite data, we demonstrate the effectiveness of our technique in capturing tropospheric variability over different geographical regions. Concurrently, we delve into the estimation of ionospheric phase screens utilizing SAR image stacks. The intricacies of ionospheric disturbances pose unique challenges, necessitating specialized techniques. We dissect our approach, showcasing its capacity to mitigate ionospheric noise and recover precise phase information. Real data from the Sentinel-1 satellite are employed to showcase the efficacy of our method, unraveling ionospheric perturbations with improved accuracy. The integration of our techniques, though presented separately for clarity, collectively contributes to a comprehensive framework for atmospheric monitoring. Our findings emphasize the potential of SAR-based approaches in advancing our knowledge of atmospheric processes, thus fostering advancements in weather prediction, geophysics, and environmental management
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