1,720,964 research outputs found
Polarization analysis of the impact of temporal decorrelation in synthetic aperture radar (SAR) tomography
No full textAfter almost two decades of long investigations into 3D imaging of natural environments, synthetic aperture radar (SAR) tomography (TomoSAR) is now at an operational level. Yet, a major problem that limits the potential of TomoSAR is related to the temporal decorrelation of natural scatterers during multitemporal multibaseline data acquisition. In this paper, a comparative investigation into the effect of temporal decorrelation between employed polarizations is presented. A particular focus is put on practical and statistical analysis of the dispersion of polarimetric vertical reflectivity in the presence of temporal decorrelation. The analysis is based on the synthesis of all feasible polarimetric responses of a given scatterer from its measurements of a linear orthonormal basis. Such an analysis offers a comprehension of the expected level of temporal decorrelation in TomoSAR focusing with respect to employed polarization. The analysis was performed by simulating temporal decorrelation with different terms, depending on the vertical structure and polarization, which are important aspects in a forest scenario. Moreover, the experiment was extended to a P-band dataset relative to the forest site of Remningstorp, Sweden, which was acquired through the German Aerospace Center's experimental SAR (E-SAR) airborne system in the framework of the European Space Agency (ESA) campaign BioSAR
Corrections to: Regularization of SAR tomography for 3-D height reconstruction in urban areas (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2019) 12:2 (648–659) DOI: 10.1109/JSTARS.2018.2889428)
No full textCorrections have been made to author affiliations in the paper, Regularization of SAR Tomography for 3-D Height Reconstruction in Urban Areas, (Aghababaee, et al.), IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 12, no. 2, pp. 648-659, Feb. 2019
Differential SAR Tomography Reconstruction Robust to Temporal Decorrelation Effects
No full textTemporal decorrelation is one of the major problems in synthetic aperture radar (SAR) tomography (TomoSAR) of a natural environment that leads to blurring and spreading in focused image space. In the context of spatiotemporal focusing using the multioral multi-baseline (MB) SAR data, a model-based differential TomoSAR is employed. Along this and with the aim of temporal decorrelation-robust focusing, a differential tomography framework based on generalized Capon estimator is investigated. The method can cope with temporal decorrelation of the distributed environment by spatiotemporal focusing with optimal bandwidth of the distributed signal. In addition, the method employs an additional parameter for coherence channel balancing in the model of generalized Capon that benefits from it in characterizing the spatiotemporal backscattering by mitigating the inconsistency between channels. The analysis is performed with a realistic simulation of temporal decorrelation in the presence of different decorrelation sources and taking into account the dependence on the vertical structure of the forested area. Effectiveness of the proposed framework has been assessed on both simulated and real data sets by evaluation and characterization of the canopy and under foliage ground in terms of deviation between the estimated covariance matrix and one of the generalized TomoSAR models
The use of NL paradigm in SAR applications
No full textThe development of Non Local (NL) approaches has had a strong impact in the framework of image processing. In particular, with respect to Synthetic Aperture Radar (SAR) imaging domain, several algorithms, expoliting NL paradigm, have been proposed. The aim of this paper is to investigate and analyze the newly developed NL filters methodologies for SAR images and to highlight the new applications in the framework of earth observation that take advantages of the NL approaches. In particular the application of NL apporaches in Forest Tomography, height reconstruction and image restoration will be addressed
Assessment of temporal decorrelation in differential SAR tomography for forestry applications
No full textDifferential synthetic aperture radar tomography (TomoSAR) has been proven to be effective in characterizing the bi-dimensional spatial-temporal backscattering from the distributed volumetric media. The purpose of this paper is to investigate the effectiveness of differential SAR tomography under the presence of temporal decorrelation. Under the assumptions of short and long terms decorrelation (due f.i. to motion caused by winds, or to dielectric changes caused by temporal changes of the scattering properties, or to sudden decorrelation induced by rain, snow and deforestation), differential SAR tomography using model-based Capon focusing technique is evaluated for volumetric media characterization and sub-canopy ground monitoring. he analysis is performed by simulating the temporal decorrelation with different terms and including the dependence on the vertical structure of volumetric media. This is a very important aspect to be taken into account for the assessment of different sources of decorrelation in forest reality. Moreover, the experiment is extended to the P-band data set relative to the forest site of Remningstorp, Sweden, acquired by German Aerospace Center's E-SAR airborne system in the framework of the European Space Agency (ESA) campaign BioSAR
ON THE SEPARATION OF GROUND AND CANOPY SCATTERINGS USING SINGLE POLARIMETRIC MULTI-BASELINE SAR TOMOGRAPHY
No full textBackscattering separation coming from ground and canopy is one of the main aims in dealing with forest scenario using synthetic aperture radar (SAR) tomography. Theoretically SAR tomography (TomoSAR) provides layover solution, but in practice, insufficient vertical resolution using typical reconstruction approaches may not be sufficient for identification of the vertically aligned scatterers. To cope with this intrinsic issue, we proposed a method that separates the ground and canopy backscatterings based on Random-Volume-over-Ground (RVOG) model and by employing the generalized likelihood ratio test (GLRT) detection schemes over the covariance matrix. Such a separation allows identification of interference of the backscattering, which simply brings the possibility to resolve and separate ground and canopy superposition in the tomogram. Experimental validation of the proposed methodology is provided using a real data set acquired by the ONERA SETHI in the framework of the ESA's campaign, TropiSAR
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
THREE-DIMENSIONAL TARGET SCATTERING CLASSIFICATION USING FULL-RANK POLARIMETRIC TOMOGRAPHIC SAR FOCUSING
No full textThis paper deals with the characterization of permanent scatterers in polarimetric synthetic aperture radar (SAR) images of urban environment. To this aim, the main purpose of this paper is to investigate how spaceborne SAR tomography (TomoSAR) can be employed to identify and distinguish the target scattering mechanisms. Along this, the conventional H-alpha classifier can be adapted to the reconstructed polarimetric coherence matrix, i.e. T, in a multi-dimensional space. However, dealing with multi-temporal multi-baseline satellite images, the accurate tomographic reconstruction requires permanent scatterers between all the acquisitions. To cope with this issue, a generalized likelihood ratio test (GLRT)-based tomographic approach for polarimetric SAR tomography is developed. The proposed framework of scatterer detection and characterization is evaluated using TerraSAR-X polarimetric multi-baseline data sets over an urban area in France
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