- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Field Observations of Temporal Variations of Surface Soil Moisture: Comparison with Insar Sentinel-1 Data
摘要： In this paper we summarize the results of an experiment aiming to compare soil moisture estimates obtained by Sentinel-1 interferometric data with in-situ measurements. The study area, located close to Lisbon in Companhia das Lezirias, Portugal is characterized by a flat topography, large agricultural areas and sparse vegetation. In a test site, four soil moisture sensors were deployed and soil moisture was measured (at a depth of 5 cm) for a period of 7 months in an hourly basis. For the same interval of time and with a temporal resolution of 6 days C-band Sentinel-1 SAR images were interferometrically processed and coherence, phase and phase triplet images were derived. The in-situ soil moisture measurements have been used to predict the analytical interferometric phases, coherences and phase triplets and compared with the measured interferometric phases in both VV and VH polarimetric channels. As a further analysis, a regression analysis of in-situ soil moisture measurement and Sentinel-1 backscattering images has been carried out.
关键词： soil moisture,C-band,SAR interferometry,Sentinel-1
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Car-Borne SAR System for Interferometric Measurements: Development Status and System Enhancements
摘要： Terrestrial radar systems are used operationally for area-wide measurement and monitoring of surface displacements on steep slopes, as prevalent in mountainous areas or also in open pit mines. One limitation of these terrestrial systems is the decreasing cross-range resolution with increasing distance of observation due to the limited antenna size of the real aperture radar or the limited synthetic aperture of the quasi-stationary SAR systems. Recently, we have conducted a first experiment using a car-borne SAR system at Ku-band, demonstrating the time-domain back-projection (TDBP) focusing capability for the FMCW case and single-pass interferometric capability of our experimental Ku-band car-borne SAR system. The cross-range spatial resolution provided by such a car-based SAR system is potentially independent from the distance of observation, given that an adequate sensor trajectory can be built. In this paper, we give (1) an overview of the updated system hardware (radar setup and high-precision combined INS/GNSS positioning and attitude determination), and (2) present SAR imagery obtained with the updated prototype Ku-band car-borne SAR system.
关键词： azimuth focusing,Ku-band,SAR imaging,ground-based SAR system,car-borne SAR,parallelization,SAR interferometry,GPU,CUDA,interferometry,CARSAR,Synthetic aperture radar (SAR)
更新于2025-09-23 15:22:29
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GB-SAR Interferometry Based on Dimension-Reduced Compressive Sensing and Multiple Measurement Vectors Model
摘要： To reduce the data acquisition time and the high-level sidelobes produced by conventional focusing methods for ground-based synthetic aperture radar interferometry, we present a new method to provide accurate displacement maps based on the dimension-reduced compressive sensing (CS) method combined with the multiple measurement vectors (MMVs) model. The proposed CS method consists in selecting the supported area of targets, estimated by the fast conventional method with undersampled data. The following sparse reconstruction is applied only to the selected areas. The MMV-based approach allows increasing the coherence and the precision of displacement estimates. Two experiments are carried out to assess the performance of the proposed method.
关键词： multiple measurement vectors (MMVs) model,SAR interferometry,Compressive sensing (CS),ground-based synthetic aperture radar (GB-SAR),SAR
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - The 2-Looks Tops Mode: Enhanced Sensitivity To Ground Displacement In Azimuth Direction with Burst-Mode Sar Systems. Demonstration With Terrasar-X
摘要： The Terrain Observation by Progressive Scans (TOPS) SAR acquisition mode achieves wide coverage area by employing subapertures. This way multiple subswaths can be acquired. It overcomes the problems of scalloping and azimuth-varying ambiguities inherent to the ScanSAR mode by introducing a steering of the beam in the along-track direction. The draw-back is that, due to the burst operation mode, the azimuth resolution is strongly worsened with respect to the conventional StripMap mode. This reduces the performance on the retrieval of the ground displacement in the azimuth direction. In this contribution we provide an update on the last developments with our proposed 2-looks TOPS mode. This mode has been devised to mitigate the degraded sensitivity on the retrieval of ground deformation azimuth shifts of 1-look TOPS acquisitions. We will present the last improvements to the mode as well as updated results with time-series of experimental TerraSAR-X data.
关键词： 2-looks TOPS,SAR Interferometry,North-South deformation,TerraSAR-X,Synthetic Aperture Radar (SAR)
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Assessment of the Ground Polarimetry in Crops Estimated Using MB Sar Interferometry at C-Band
摘要： In this paper, polarimetric multi-baseline (MB) Synthetic Aperture Radar (SAR) Interferometry data are used to estimate the polarimetric ground component under vegetation. However, the solution of the applied separation algorithm is not unique and depends on the constraints in the regularization. First, the effect of this non-uniqueness is analyzed and then exploited to isolate a ground component with minimized influence of depolarizing scattering mechanisms. Using experimental MB SAR data acquired by DLR’s airborne sensor F-SAR, the polarimetric entropy and mean alpha angle of the isolated ground component are compared to the original polarimetry of the full image. Finally, the ground polarimetry is interpreted for changing soil moisture vegetation conditions in corn. To this purpose, three dates are compared characterized by 1) a change in soil moisture, 2) a change in vegetation cover or 3) a simultaneous change of soil moisture and vegetation cover.
关键词： Agricultural Vegetation,SAR Interferometry,Soil moisture
更新于2025-09-23 15:21:21
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Effect of the Double-Bounce Contribution in Polinsar-Based Height Estimates of Rice Crops Using Tandem-X Bistatic Data
摘要： In bistatic acquisitions the presence of a double-bounce contribution at the ground affects the interferometric coherence with a decorrelation factor which is usually overlooked in studies employing polarimetric SAR interferometry. The standard acquisition mode of TanDEM-X is bistatic, so the influence of this contribution in the estimation of scene parameters (ground topography and vegetation height) is studied here. The analysis is carried out both with simulations and real data acquired over rice fields during the science phase of TanDEM-X. Results show that the error in height and topography is small when incidence angle is below 30 degrees, but may become noticeable for shallower incidences.
关键词： vegetation,rice,Polarimetric SAR interferometry,bistatic radar,TanDEM-X
更新于2025-09-23 15:21:21
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Cost analysis of 100% renewable electricity provider utilizing surplus electric power of residential PV systems in Japan
摘要： This paper introduces a framework for robust parameter estimation in multipass interferometric synthetic aperture radar (InSAR), such as persistent scatterer interferometry, SAR tomography, small baseline subset, and SqueeSAR. These techniques involve estimation of phase history parameters with or without covariance matrix estimation. Typically, their optimal estimators are derived on the assumption of stationary complex Gaussian-distributed observations. However, their statistical robustness has not been addressed with respect to observations with nonergodic and non-Gaussian multivariate distributions. The proposed robust InSAR optimization (RIO) framework answers two fundamental questions in multipass InSAR: 1) how to optimally treat images with a large phase error, e.g., due to unmolded motion phase, uncompensated atmospheric phase, etc.; and 2) how to estimate the covariance matrix of a non-Gaussian complex InSAR multivariate, particularly those with nonstationary phase signals. For the former question, RIO employs a robust M-estimator to effectively downweight these images; and for the latter, we propose a new method, i.e., the rank M -estimator, which is robust against non-Gaussian distribution. Furthermore, it can work without the assumption of sample stationarity, which is a topic that has not previously been addressed. We demonstrate the advantages of the proposed framework for data with large phase error and heavily tailed distribution, by comparing it with state-of-the-art estimators for persistent and distributed scatterers. Substantial improvement can be achieved in terms of the variance of estimates. The proposed framework can be easily extended to other multipass InSAR techniques, particularly to those where covariance matrix estimation is vital.
关键词： Differential interferometric synthetic aperture radar (D-InSAR),robust estimation,rank covariance matrix,robust InSAR optimization (RIO),M -estimator,SAR interferometry (InSAR)
更新于2025-09-23 15:19:57
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Study of Surface Plasmon Resonance Sensor Based on Polymer-Tipped Optical Fiber With Barium Titanate Layer
摘要： Detection of changes caused by major events—such as earthquakes, volcanic eruptions, and floods—from interferometric synthetic aperture radar (SAR) data is challenging because of the coupled effects with temporal decorrelation caused by natural phenomena, including rain, snow, wind, and seasonal changes. The coupled effect of major events and natural phenomena sometimes leads to misinterpretation of interferometric coherence maps and often degrades the performance of change detection algorithms. To differentiate decorrelation sources caused by natural changes from those caused by an event of interest, we formulated a temporal decorrelation model that accounts for the random motion of canopy elements, temporally correlated dielectric changes, and temporally uncorrelated dielectric changes of canopy and ground. The model parameters are extracted from the interferometric pairs associated with natural changes in canopy and ground using the proposed temporal decorrelation model. In addition, the cumulative distribution functions of the temporally uncorrelated model parameters, which are associated with natural changes in canopy and ground, are estimated from interferometric pairs acquired before the event. Model parameters are also extracted from interferometric SAR data acquired across the event and compared with the cumulative probabilities of natural changes in order to calculate the probability of a major event. Subsequently, pixels with cumulative probabilities greater than 75% are marked as changed due to the event. A case study for detecting volcanic ash during the eruption of the Shinmoedake volcano in January 2011 was carried out using L-band Advanced Land Observation Satellite PALSAR data.
关键词： Coherence change detection,volcanic ash,temporal decorrelation model,synthetic aperture radar (SAR) interferometry
更新于2025-09-23 15:19:57
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - GaSb/GaAs quantum nanostructures for intermediate band solar cell under high sunlight concentration
摘要： This paper introduces a framework for robust parameter estimation in multipass interferometric synthetic aperture radar (InSAR), such as persistent scatterer interferometry, SAR tomography, small baseline subset, and SqueeSAR. These techniques involve estimation of phase history parameters with or without covariance matrix estimation. Typically, their optimal estimators are derived on the assumption of stationary complex Gaussian-distributed observations. However, their statistical robustness has not been addressed with respect to observations with nonergodic and non-Gaussian multivariate distributions. The proposed robust InSAR optimization (RIO) framework answers two fundamental questions in multipass InSAR: 1) how to optimally treat images with a large phase error, e.g., due to unmolded motion phase, uncompensated atmospheric phase, etc.; and 2) how to estimate the covariance matrix of a non-Gaussian complex InSAR multivariate, particularly those with nonstationary phase signals. For the former question, RIO employs a robust M-estimator to effectively downweight these images; and for the latter, we propose a new method, i.e., the rank M -estimator, which is robust against non-Gaussian distribution. Furthermore, it can work without the assumption of sample stationarity, which is a topic that has not previously been addressed. We demonstrate the advantages of the proposed framework for data with large phase error and heavily tailed distribution, by comparing it with state-of-the-art estimators for persistent and distributed scatterers. Substantial improvement can be achieved in terms of the variance of estimates. The proposed framework can be easily extended to other multipass InSAR techniques, particularly to those where covariance matrix estimation is vital.
关键词： Differential interferometric synthetic aperture radar (D-InSAR),robust estimation,rank covariance matrix,robust InSAR optimization (RIO),M -estimator,SAR interferometry (InSAR)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 39th Central America and Panama Convention (CONCAPAN XXXIX) - Guatemala City, Guatemala (2019.11.20-2019.11.22)] 2019 IEEE 39th Central America and Panama Convention (CONCAPAN XXXIX) - Potential effect on the energetic matrix of Honduras with the installation of residential photovoltaic generators for self-consumption
摘要： Detection of changes caused by major events—such as earthquakes, volcanic eruptions, and floods—from interferometric synthetic aperture radar (SAR) data is challenging because of the coupled effects with temporal decorrelation caused by natural phenomena, including rain, snow, wind, and seasonal changes. The coupled effect of major events and natural phenomena sometimes leads to misinterpretation of interferometric coherence maps and often degrades the performance of change detection algorithms. To differentiate decorrelation sources caused by natural changes from those caused by an event of interest, we formulated a temporal decorrelation model that accounts for the random motion of canopy elements, temporally correlated dielectric changes, and temporally uncorrelated dielectric changes of canopy and ground. The model parameters are extracted from the interferometric pairs associated with natural changes in canopy and ground using the proposed temporal decorrelation model. In addition, the cumulative distribution functions of the temporally uncorrelated model parameters, which are associated with natural changes in canopy and ground, are estimated from interferometric pairs acquired before the event. Model parameters are also extracted from interferometric SAR data acquired across the event and compared with the cumulative probabilities of natural changes in order to calculate the probability of a major event. Subsequently, pixels with cumulative probabilities greater than 75% are marked as changed due to the event. A case study for detecting volcanic ash during the eruption of the Shinmoedake volcano in January 2011 was carried out using L-band Advanced Land Observation Satellite PALSAR data.
关键词： volcanic ash,synthetic aperture radar (SAR) interferometry,temporal decorrelation model,Coherence change detection
更新于2025-09-19 17:13:59