15 results on '"Aghababaei, Hossein"'
Search Results
2. Spectral estimation model for linear displacement and vibration monitoring with GBSAR system
- Author
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Hosseiny, Benyamin, Amini, Jalal, and Aghababaei, Hossein
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- 2024
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3. Structural displacement monitoring using ground-based synthetic aperture radar
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Hosseiny, Benyamin, Amini, Jalal, and Aghababaei, Hossein
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- 2023
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4. The comparative study of three nonparametric methods of SAR tomography for building reconstruction
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Omati, Mehrnoosh, Sahebi, Mahmod Reza, and Aghababaei, Hossein
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- 2022
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5. A deterministic descriptive regularization-based method for SAR tomography in urban areas.
- Author
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Armeshi, Hossein, Sahebi, Mahmod Reza, and Aghababaei, Hossein
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CITIES & towns ,SYNTHETIC aperture radar ,TOMOGRAPHY ,THREE-dimensional imaging ,CONSTRUCTION cost estimates - Abstract
In recent years, Synthetic Aperture Radar (SAR) Tomography (TomoSAR) has ascertained great potential for the three-dimensional (3-D) reconstruction of observed scenes, especially in urban areas. However, the number of proceed snapshots (observations) is usually less than that of slant height samples (unknowns) in TomoSAR inversion processes. This impairs the quality of the reconstructed vertical information. To cope with this issue and improve the reliability of reconstructed vertical information, this paper investigates the possible potential of a deterministic descriptive regularization-based method. Deterministic descriptive regularization is a well-conditioned optimization framework based on the descriptive idea of a regularization solution. This strategy can help to mitigate the effect of the ill-posed problem. Thus, it can assist SAR tomography to deal with the possible impairing issues arising from low numbers and the distribution of baselines. For this purpose, the result of the proposed strategy is compared with the outcomes from the standard TomoSAR techniques, including Beamforming, Capon, and Minimum Norm. The proposed method for reconstruction of the reflectivity function of the observed scene has been performed on a dataset acquired by the Sentinel-1 sensor in 2022 over Tehran City, Iran. The experimental results indicate that the proposed algorithm can estimate building heights with a vertical accuracy of better than 91%. These results demonstrate the great potential of the proposed method for reconstructing the full 3-D images of urban areas. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. Micro-vibration monitoring of pipelines using millimetre-wave MIMO radar.
- Author
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Hosseiny, Benyamin, Amini, Jalal, and Aghababaei, Hossein
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VIBRATION (Mechanics) ,STRUCTURAL health monitoring ,WATER pipelines ,MIMO radar ,MIMO systems ,RADAR interferometry ,INTERFEROMETRY - Abstract
To ensure pipelines' safety it is essential to have a regular and accurate monitoring system to inspect the behaviour of pipelines over time to prevent potential damage. This paper explores the capability of a millimetre-wave multiple-input multiple-output (MIMO) radar system for micro-vibration monitoring of pipelines. Compared to the conventional radars, MIMO radar provides cross-range resolution, while still being capable of monitoring a scene with sub-second data acquisition intervals. This makes it a non-contact system suitable for fast displacement and vibration monitoring. However, the application of MIMO radars has not been fully investigated and analysed for structural monitoring, especially for pipeline vibrations. In this study, a dense time-series of MIMO radar data was collected and processed by proposing a framework based on persistent scatterer interferometry to obtain a map of pipeline vibrations. Experiments were conducted in a controlled environment consisting of both functional and inactive water pipelines. The results showed that the system could detect displacements from micrometre up to sub-millimetre levels, with a displacement error of less than 3 μm. Additionally, the radar could identify the dominant frequencies of 24.375 and 24.500 Hz even in pipelines with very small vibration patterns. These results validate the high potential of millimetre-wave MIMO radar systems for non-contact monitoring of micro-vibrations in pipelines. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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7. Enabling High-Resolution Micro-Vibration Detection Using Ground-Based Synthetic Aperture Radar: A Case Study for Pipeline Monitoring.
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Hosseiny, Benyamin, Amini, Jalal, Aghababaei, Hossein, and Ferraioli, Giampaolo
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SYNTHETIC apertures ,SYNTHETIC aperture radar ,VIBRATION (Mechanics) ,PIPELINE inspection ,FREQUENCIES of oscillating systems ,WATER pipelines ,SIGNAL processing - Abstract
The wellbeing of pipelines is influenced by a range of factors, such as internal and external pressures, as well as deterioration over time due to issues like erosion and corrosion. It is thus essential to establish a reliable monitoring system that can precisely examine pipeline behavior over time in order to prevent potential damages. To this end, pipelines are inspected based on internal and external approaches. Radar, as a non-contact sensing system, can be a suitable choice for external pipeline inspection. Radar is capable of the transmission and receiving of thousands of signals in a second, which reconstructs the displacement signal and is used for a vibration analysis. Synthetic aperture radar (SAR) imaging adds cross-range resolution to radar signals. However, a data acquisition rate of longer than several seconds makes it unsuitable for sub-second vibration monitoring. This study aims to address this limitation by presenting a method for high-resolution vibration monitoring using ground-based SAR (GBSAR) signals. To this end, a signal processing method by modifying the radar's signal model is presented, which allows for estimating scattering targets' vibration parameters and angle of arrival with high resolution. The proposed method is validated with numerical simulation and a real case study comprising water pipelines. Moreover, various analyses are presented for the in-depth evaluation of the method's performance in different situations. The results indicate that the proposed method can be effective in detecting pipeline vibration frequencies with micro-scale amplitudes while providing high spatial resolution for generating accurate vibration maps of pipelines. Also, the comparison with the radar observations shows a high degree of agreement between the frequency responses with the maximum error of 0.25 Hz in some rare instances. [ABSTRACT FROM AUTHOR]
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- 2023
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8. First Demonstration of Space-Borne Polarization Coherence Tomography for Characterizing Hyrcanian Forest Structural Diversity
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Poorazimy, Maryam, Shataee, Shaban, Aghababaei, Hossein, Tomppo, Erkki, Praks, Jaan, Department of Electronics and Nanoengineering, Gorgan University of Agricultural Sciences and Natural Resources, University of Twente, University of Helsinki, Aalto-yliopisto, Aalto University, Department of Earth Observation Science, Digital Society Institute, Faculty of Geo-Information Science and Earth Observation, and UT-I-ITC-ACQUAL
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number of trees ,broad-leaved forests ,TanDEM-X ,PCT ,standard deviation of dbh ,SAR - Abstract
Funding Information: This research received no funding. Publisher Copyright: © 2023 by the authors. Structural diversity is recognized as a complementary aspect of biological diversity and plays a fundamental role in forest management, conservation, and restoration. Hence, the assessment of structural diversity has become a major effort in the primary international processes, dealing with biodiversity and sustainable forest management. Because of prohibitive costs associated with the ground measurements of forest structure, despite their high accuracy, space-borne polarization coherence tomography (PCT) can introduce an alternative approach given its ability to provide a vertical reflectivity profile and spatiotemporal resolutions related to detecting forest structural changes. In this study, for the first time ever, the potential of space-borne PCT was evaluated in a broad-leaved Hyrcanian forest of Iran over 308 circular sample plots with an area of 0.1 ha. Two aspects of horizontal structure diversity, including standard deviation of diameter at breast height (sigma dbh) and the number of trees (N), werepredicted as important characteristics in wood production and biomass estimation. In addition, the performance of prediction algorithms, including multiple linear regression (MLR), k-nearest neighbors (k-NN), random forest (RF), and support vector regression (SVR) were compared. We addressed the issue of temporal decorrelation in space borne PCT utilizing the single-pass TanDEM-X interferometer. The data were acquired in standard DEM mode with single polarization of HH. Consequently, airborne laser scanning (ALS) was used to estimate initial values of height hv and ground phase ?0. The Fourier-Legendre series was used to approximate the relative reflectivity profile of each pixel. To link the relative reflectivity profile averaged within each plot with corresponding ground measurements of sigma dbh and N, thirteen geometrical and physical parameters were defined (P1 - P13). Leave-one-out cross validation (LOOCV) showed a better performance of k-NN than the other algorithms in predicting sigma dbh and N. It resulted in a relative root mean square error (rRMSE) of 32.80%, mean absolute error (MAE) of 4.69 cm, and R2* of 0.25 for sigma dbh, whereas only 22% of the variation in N was explained using the PCT algorithm with an rRMSE of 41.56%. This study revealed promising results utilizing TanDEM-X data even though the accuracy is still limited. Hence, an entire assessment of the used framework in characterizing the reflectivity profile and the possible effect of the scale is necessary for future studies.
- Published
- 2023
9. A Deep Learning Solution for Height Inversion on Forested Areas Using Single and Dual Polarimetric TomoSAR.
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Yang, Wenyu, Vitale, Sergio, Aghababaei, Hossein, Ferraioli, Giampaolo, Pascazio, Vito, and Schirinzi, Gilda
- Abstract
Forest characterization and monitoring are highly important for tracking climate change, using ecology resources, and biodiversity applications. Synthetic aperture radar tomography (TomoSAR) provides the opportunity to reconstruct 3-D structures of the penetrable media relying on multibaseline image acquisition. In forest applications, TomoSAR serves as a powerful technical tool for reconstructing forest height and underlying topography. Presently, a number of reconstruction methods are based on fully polarimetric (FP) TomoSAR (Pol-TomoSAR) datasets which require costly data acquisition. The aim of this letter is to go beyond the limitation of the requirement for full polarization by extending tomographic SAR neural network (TSNN), a neural network for TomoSAR, to the case of single-polarimetric (SP) and dual-polarimetric (DP) TomoSAR data for retrieving forest height and underlying topography. Experimental results indicate that TSNN trained by SP or DP TomoSAR data is a powerful candidate to estimate forest height and underlying topography with high accuracy. [ABSTRACT FROM AUTHOR]
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- 2023
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10. Estimation of aboveground biomass from PolSAR and PolInSAR using regression-based modelling techniques.
- Author
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Mukhopadhyay, Ritwika, Kumar, Shashi, Aghababaei, Hossein, and Kulshrestha, Anurag
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STATISTICAL sampling ,MICROWAVE remote sensing ,FOREST biomass ,SYNTHETIC aperture radar ,BIOMASS estimation ,RANDOM forest algorithms ,STATISTICAL correlation - Abstract
In the field of forestry studies, microwave remote sensing has broad applications due to the penetration into the semi-transparent media. This feature is used for the estimation of biophysical parameters and monitoring of deforestation. Therefore, the estimation of biophysical parameters is essential for assessing carbon stock management. Hence, the aboveground biomass (AGB) using synthetic aperture radar (SAR) data is recognized as typical approaches in forest application. However, the integrated use of polarimetric (PolSAR) and interferometric (PolInSAR) data might be more efficient tools for AGB mapping. Accordingly, in this study with the integrated data, the efficiency of machine learning techniques including random forest regression (RFR) and multiple linear regression (MLR) model were assessed and compared for the prediction of AGB. The analyses were performed using an image pair of fully polarimetric Radarsat-2 C-band data set and the related field data of Malhan Forest Range, Dehradun Forest Division, which were collected using the systematic sampling technique. Particularly, the training and testing of the models were done using the field sample plots. The experimental results showed that the RFR algorithm provided a better prediction result of AGB than the MLR model. The correlation coefficient (R
2 ) and root-mean-square error (RMSE) for the RFR algorithm was estimated to be around 0.65 and 24.33 Mg/ha, respectively, while for the MLR model, R2 and RMSE are estimated as 0.54 and 33.05 Mg/ha, respectively. Therefore, it was concluded that the prediction of AGB through the machine learning technique using PolSAR and PolInSAR data has a significant advantage for accurate estimation of the AGB. [ABSTRACT FROM AUTHOR]- Published
- 2022
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11. Polarization Optimization for the Detection of Multiple Persistent Scatterers Using SAR Tomography.
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Aghababaei, Hossein, Ferraioli, Giampaolo, Stein, Alfred, and Déniz, Luis Gómez
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SYNTHETIC aperture radar , *RADAR antennas , *TOMOGRAPHY , *LIKELIHOOD ratio tests , *ORTHONORMAL basis , *POLARIMETRY - Abstract
The detection of multiple interfering persistent scatterers (PSs) using Synthetic Aperture Radar (SAR) tomography is an efficient tool for generating point clouds of urban areas. In this context, detection methods based upon the polarization information of SAR data are effective at increasing the number of PSs and producing high-density point clouds. This paper presents a comparative study on the effects of the polarization design of a radar antenna on further improving the probability of detecting persistent scatterers. For this purpose, we introduce an extension of the existing scattering property-based generalized likelihood ratio test (GLRT) with realistic dependence on the transmitted/received polarizations. The test is based upon polarization basis optimization by synthesizing all possible polarimetric responses of a given scatterer from its measurements on a linear orthonormal basis. Experiments on both simulated and real data show, by means of objective metrics (probability of detection, false alarm rate, and signal-to-noise ratio), that polarization waveform optimization can provide a significant performance gain in the detection of multiple scatterers compared to the existing full-polarization-based detection method. In particular, the increased density of detected PSs at the studied test sites demonstrates the main contribution of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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12. Statistical Indices for Despeckling Evaluation in Multichannel SAR Images.
- Author
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Aghababaei, Hossein and Ferraioli, Giampaolo
- Abstract
With reference to the application of multichannel (polarimetric or interferometric) synthetic aperture radar (SAR) data, despeckling is a mandatory task in order to exploit fully the image information. Thanks to the long-standing studies, there exist several despeckling techniques that have shown to be powerful tools for filtering multichannel SAR images. Often, such techniques rely on the estimation of the covariance matrix. However, in the literature, the performance evaluation of the filtering operation, which represents a fundamental aspect, has been mainly addressed for a single intensity/amplitude SAR image. In this letter, we present two nonreference indices for the evaluation of the polarimetric/interferometric covariance matrix. The proposed quality indices assess the statistical similarity of the ratio between the original and filtered covariance matrices to the properties derived from the pure speckle model. The analyses both on simulated and real data show that the results of filter ranking by the proposed method are in agreement with the visual evaluation and are consistent with the common polarimetric quality measures. [ABSTRACT FROM AUTHOR]
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- 2021
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13. Deformation Velocity-Based Regularization of Multibaseline SAR Interferometry.
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Zamani, Roghayeh and Aghababaei, Hossein
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Synthetic aperture radar (SAR) interferometry (InSAR) has shown great potential in the monitoring of Earth’s surface and detection of the possible slow temporal deformations. Within the framework of multibaseline SAR interferometry, the availability of multiple interferograms obtained from multipass satellite observations can significantly improve the accuracy of the estimated target parameters, i.e., the residual height and the mean deformation velocity. The parameters can be estimated in the maximum likelihood (ML) sense and through the data covariance matrix. However, the presence of artifact and outliers may impair the parameter estimation, specifically when the candidate cells are subject to temporal decorrelation and atmospheric phase noise effects. In this letter, the exploitation of contextual spatial information is proposed to reduce the possible ambiguity and improve the accuracy of ML-based parameter estimation. The proposed approach adds a regularization term (or a constraint) to the ML’s model in order to include the information about the scene velocity variation. Hence, the resulted nonconvex optimization is resolved using the graph-cut concept. The method is evaluated using the simulated and two real data sets acquired by Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed) and Sentinel-1A sensors over Tehran, Iran; and the results are validated using the global positioning system-based measurements. [ABSTRACT FROM AUTHOR]
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- 2021
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14. The Antibacterial Effect of Polypropylenimine-G2 Dendrimer on Escherichia coli, Enterobacter cloacae, Bacillus subtilis, and Staphylococcus aureus.
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Izanloo, Hassan, Jebelli, Mohammad Ahmadi, Mjidi, Gharib, Khazaei, Mohammad, Tashayoe, Hamid Reza, Vazirirad, Vahid, Aghababaei, Hossein, and Vakili, Behnam
- Published
- 2014
15. On the Assessment of Non-Local Multi-Looking in Detection of Persistent Scatterers Using SAR Tomography.
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Aghababaei, Hossein
- Subjects
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SYNTHETIC aperture radar , *TOMOGRAPHY , *LIKELIHOOD ratio tests , *TIME series analysis , *COVARIANCE matrices - Abstract
Synthetic aperture radar (SAR) tomography has shown great potential in multi-dimensional monitoring of urban infrastructures and detection of their possible slow deformations. Along this line, undeniable improvements in SAR tomography (TomoSAR) detection framework of multiple permanent scatterers (PSs) have been observed by the use of a multi-looking operation that is the necessity for data's covariance matrix estimation. This paper attempts to further analyze the impact of a robust multi-looking operation in TomoSAR PS detection framework and assess the challenging issues that exist in the estimation of the covariance matrix of large stack data obtained from long interferometric time series acquisition. The analyses evaluate the performance of non-local covariance matrix estimation approaches in PS detection framework using the super-resolution multi-looked Generalized Likelihood Ratio Test (GLRT). Experimental results of multi-looking impact assessment are provided using two datasets acquired by COSMO-SkyMED (CSK) and TerraSAR-X (TSX) over Tehran, Iran, and Toulouse, France, respectively. The results highlight that non-local estimation of the sample covariance matrix allows revealing the presence of the scatterers, that may not be detectable using the conventional local-based framework. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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