P. Haghighatmehr; M. J. Valadanzouj; R. Tajik; S. Jabari; M. R. Sahebi; R. Eslami; M. Ganjiyan; M. Dehghani
Abstract
A large area in Hashtgerd plain, in southwest of Tehran, is subject to the land subsidence induced by overexploitation of groundwater. In this paper, in order to study the subsidence SAR interferometry (InSAR) and global positioning system (GPS) are used. The small baseline subset (SBAS) algorithm is ...
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A large area in Hashtgerd plain, in southwest of Tehran, is subject to the land subsidence induced by overexploitation of groundwater. In this paper, in order to study the subsidence SAR interferometry (InSAR) and global positioning system (GPS) are used. The small baseline subset (SBAS) algorithm is used for deformation time series analysis. Time series analysis is performed using 6 interferograms calculated from 4 ENVISAT ASAR data spanning 4 months in 2008. A smoothing constraint that reduces the atmospheric noise, unwrapping and orbital errors whereas it preserves the non-linear deformation features is added to the least-squares solution. The time series results revealed that the area is subsiding continuously. Mean LOS deformation velocity map obtained from time series analysis demonstrated a considerable subsidence rate of 47 (mm/month). In order to assess the time series analysis results a dense GPS network consisting of 18 measuring stations is then established. The network design is carried out based on the subsidence spatial pattern extracted from an interferogram calculated from radar data of 2003-2004. The GPS stations are collecting the data simultaneously with radar data acquisitions. Horizontal and vertical components of the subsidence are extracted from GPS measurements. The comparison of InSAR and GPS time series shows the high compatibility of the results demonstrating the high performance of InSAR technique.
S. Adham Khiabani; M. R. Mobasheri; M. J. Valadanzoej; M. Dehghani
Abstract
SAR interferometry has shown its abilities in measuring the surface deformation in various applications. Atmospheric signals as an important factor affecting the interferometric measurements have temporally uncorrelated and complicated behavior. In this paper, a model based on the error source is presented ...
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SAR interferometry has shown its abilities in measuring the surface deformation in various applications. Atmospheric signals as an important factor affecting the interferometric measurements have temporally uncorrelated and complicated behavior. In this paper, a model based on the error source is presented to reduce the atmospheric contributions on the interferometric measurements in Mashhad subsidence area. In this model, the Full-Resolution (RF) MODIS data and meteorological information were used in order to estimate the water vapor and reduce the pressure effect, respectively. Moreover, water drops as well as the clouds effects were considered in the proposed model. Utilizing error propagation, model error was estimated as 7.2 mm. The Root Mean Square Error (RMSE) as a quantitative comparison between GPS measurements and interferometric results showed an improvement from 9 mm (before atmospheric correction) to 2 mm after applying the correction model.
Y. Rezaei; M. R. Mobasheri; M. J. Valadanzouje
Abstract
Estimation of noise present in Hyperspectral images is a way to enhance the quality of the extracted information and to reduce the uncertainties in the results. The simplest method widely used in noise estimation is Shift Difference. This method has two weaknesses; first, it is based upon the assumption ...
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Estimation of noise present in Hyperspectral images is a way to enhance the quality of the extracted information and to reduce the uncertainties in the results. The simplest method widely used in noise estimation is Shift Difference. This method has two weaknesses; first, it is based upon the assumption that the adjacent pixels have the same signal information which is not necessarily valid in hyperspectral data sets; second, in order to calculate the correct values of noise it needs homogeneous regions that is usually being determined by supervision. In this study, a new method in noise estimation (NETAL) is introduced. In this method the satellite images are divided into homogeneous regions using spectral absorption parameters such as location of absorption lines, width and depth of these absorption features for every individual pixels. Then in each region the noise was calculated using regression between adjacent bands and finally the total noise was estimated through accumulation of the calculated noises in each region. The NETAL algorithm was evaluated by using simulated and real hyperspectral data sets. The results show that the noise estimation by NETAL method is faster than Multiple Regression method while the accuracy will remain the same as and even better than the Multiple Regression method.