1,721,033 research outputs found
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
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
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%)
Accurate optimal doppler centroid estimation for SAR data
No full textThe paper addresses the problem of finding an optimal estimation of the Doppler centroid for Synthetic Aperture Radar (SAR) data. The original idea is to exploit a bandwidth much wider than the PRF, say 3-5 times, by selecting non-aliased point targets. Non-aliased band of natural and isolated targets with close-to-ideal features is evidenced by spotlight processing of strip map acquisitions and accurate Doppler centroid is estimated by means of a joint Maximum Likelihood (ML) estimator. Lower bound of the estimate is determined and results on both simulated and real X-band SAR data are shown
A Space Adaptive Quantizer for Spaceborne SAR
No full textA space adaptive flexible block quantizer (SA-FBQ), suited for spaceborne synthetic aperture radar missions, is presented. This quantizer is actually an extension of the flexible dynamic block adaptive quantizer (FDBAQ), proposed in [1], which, in turn, is an extension of the block adaptive quantizer (BAQ). The BAQ is the optimal quantizer for a homogeneous target. The FDBAQ gets better performances on heterogeneous targets by adaptively selecting the best BAQ according to the local signal-to-thermal-noise ratio: The worst the SNTR, the lower the quantizer rate. The quantizer selection is precomputed in a lookup table (LUT), by assuming a fixed and known probability distribution function (pdf) of the reflectivity sigma(0). The SA-FBQ extends further this concept allowing the reflectivity pdf to vary, coping with this by exploiting many LUTs (i.e., quantizer set), each adapted to the local statistics. In this paper, we introduce an algorithm to adaptively find the best set of quantizers constrained on the mean bit rate; we discuss the implementation of the SA-FBQ, and we estimate its performances in comparison with the FDBAQ and the BAQ under different scenarios. Preliminary results are shown by exploiting the worldwide mosaic of C-band reflectivity derived from European Space Agency ENVISAT data and Sentinel-1 system parameters
Joint exploitation of spaceborne SAR images and GIS techniques for urban coherent change detection
No full textThis paper proposes a simple and fast method for the identification of structural changes affecting buildings in urban environments by using a combination of Synthetic Aperture Radar (SAR) imagery and Geospatial Information System (GIS) processing. The identification of changes in urban settlements is of great interest for damage assessment after natural disasters, cadastral mapping and monitoring urban development or illegal activities, such as the construction of unauthorized buildings. Satellite remote sensing is useful in this scenario and SAR data is attractive due to its wide and ubiquitous coverage, the day and night all-weather availability, the exact repetition of the acquisition geometry, the repeated illumination and the sensitivity to slight changes in the geometrical structure of the targets in the scene. This sensibility is an advantage, but turns into a drawback especially in an urban environment where every subtle change may cause an unwanted detection. This environment is indeed one of the most challenging for the detection of those changes that are of any real interest since these events are masked by thousands of irrelevant detections. This paper tackle this problem with a combination of an improved, high-resolution coherent change detection technique called M-CCD and with a GIS post-processing. The result is a map of changes affecting buildings that are of a significant scale and consequently of a certain interest in an urban environment. In this contribution, the complete workflow is detailed and an assessment of the detected changes is done with high resolution optical images through visual photo-interpretation. A comparison with other SAR and optical change detection methods is also carried out
Joint exploitation of SAR and GNSS for atmospheric phase screens retrieval aimed at numerical weather prediction model ingestion
Full text linkThis paper proposes a simple and fast method to estimate Atmospheric Phase Screens (APSs) by jointly exploit a stack of Synthetic Aperture Radar (SAR) images and a dataset of GNSS-derived atmospheric product. The output of this processing is conceived to be ingested by NumericalWeather Prediction Models (NWPMs) to improve weather forecasts. In order to provide wide and dense area coverage and to respect requirements in terms of spatial resolution of ingestion products in NWPMs, both Permanent Scatterers (PSs) and Distributed Scatterers (DSs) are jointly exploited. While the formers are by definition stable targets, but unevenly distributed, the latter are ubiquitous but stable only within a certain temporal baseline that can vary depending on the operational frequency of the radar. The proposed method is thus particularly suited for C, L, and P band missions with low temporal baseline between two consecutive acquisitions of the same scene: these conditions, that are both necessary to provide the dense space-time coverage required by meteorologists, allow for a reliable and robust estimation of APSs thanks to the intrinsic limitation of temporal decorrelation. The proposed technique integrates Zenith Total Delay (ZTD) products computed on a very sparse grid from a network of GNSS stations to correct for SAR orbital errors and to provide the missing phase constant from the derived APS map. In this paper, the complete workflow is explained, and a comparison of the derived APSs is performed with phase screens derived from state-of-the-art SAR processing workflow (SqueeSAR®)
- …
