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Mineral target detection using the spaceborne hyperspectral images do not leads to accurate results because of the low spatial resolution. Therefore it might be better to use images with much more spatial resolutions in order to achieve good results. However, although airborne hyperspectral images have moderately good spatial resolutions but they are too much costly. Consequently it would be great if we could find a solution for the mentioned problem in low cost satellite images (such as Hyperion). In this study, a process was proposed to apply an intermediate spectrum from Hymap airborne image, to detect some minerals in the spaceborne Hyperion image. The study was conducted in east Iran and it was carried out on four mineral targets including: Kaolinite, Alunite, Epidote and Hematite. Experimental results indicate that the performance was better in intermediate spectrum for entire minerals, by 4.47, 11.38, 4.32 and 10.28 percent respectively in Alunite, Kaolinite, Epidote and Hematite.  

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Recent studies show that image classification techniques which use both spectral and spatial information are more suitable, effective, and robust than those that use only spectral information. Although late model support vector machines (SVMs) have been providing accurate results in the studies, this method is intrinsically non-contextual, which represents significant limitation in image classification. In this paper, we propose a rigorous framework which integrates SVMs and Markov random field models in a unique formulation for spatial contextual classification of various species of forest trees, ground vegetation, and water using polar metric synthetic aperture radar data. Genetic algorithm is employed for selecting appropriate features and automatic estimation of optimal parameters. Comparison of the accuracy of the proposed method with baseline methods was performed. Comparison of the accuracy of the proposed method with some other methods was carried out. The results show that this algorithm allowed approximately 19%, 14%, 11%, 5% and 3% increase in overall accuracy with respect to the Wishart, WMRF, SVM, aMRF and MSVC methods, respectively.

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The information of the wheat and barley cultivated areas and its yield are indispensable for sustainable management and economic policy making for these strategic food crops. Introduction of high spectral and special resolution satellite data has enabled production of such information in a timely and accurate manner. Evaluation of the spectral reflectance of plants using field spectroradiometry provides the possibility to identify and map different wheat and barley varieties especially while using hyperspectral remote sensing. Therefore, the behavior of the spectral curve corresponding to the nine varieties of wheat and five varieties of barley in a field at Seed and Plant Improvement Institute in Karaj growth were measured in four sections. Measurements were carried out using ASD FieldSpec®3 spectroradiometer in the range of 350-2,500 nm under natural light and environmental conditions. In the preprocessing phase noise of steam three areas are identified and eliminated and then the quality of the data collected using statistical methods, erroneous observations were excluded. A set of 65 indices of important vegetation indices sensitive to canopy chlorophyll content, photosynthesis intensity, nitrogen and water content were employed to enhance probable differences in spectral reflectance among various wheat and barley varieties in four growth stage. Analysis of variance and Tukey’s paired test were then used to compare wheat and barley varieties. The results showed that more indices number can be observed in the third stage of the cultivars are separated from each other. This promises the possibility of accurate mapping of wheat and barley varieties cultivated areas based on hyperspectral remotely sensed data.

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Biomass estimation plays an important role in the investigation of climate changes and global warming on terrestrial ecosystems. In recent years, related researches show PolInSAR techniques can significantly improve biomass estimation. Tree height can be estimated using PolInSAR techniques which by using that, the tree’s biomass can also be estimated. It is known that coherence optimization has an effective role on improvement of tree height estimation using PolInSAR. In this paper, various tree height estimation methods, such as coherence amplitude inversion algorithms, DEM differentiating, and combined methods are validated and compared using simulated data. Coherence optimization methods which are applied in these algorithms are numerical radius and phase diversity coherence optimization algorithms were estimated respectively. According to the fact that phase diversity algorithm was a phase based method, it didn’t have significant effect on improvement of tree height using coherence amplitude algorithm. However, improvement of tree height estimation by DEM differentiating method is obvious. In comparison to the previous method, although numerical radius method is time consuming and has a complicated process but it improves tree height estimation in great deals.

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In order to use the combination of spectral and spatial information, the fusion of satellite images are used. The fusion result is an image which includes spectral information of multi-spectral image and spatial information of panchromatic image. This paper investigates the capability of Fast Fourier Transform-Principal Component Analysis (FFT-PCA) method in the fusion of two set of images, including Hyperion and IRS-1D images and IKONOS images, where this method uses the replacement of the panchromatic image with fast Fourier filtering for the purpose of fusion. The fusion results of this method have been compared with the fusion result of Intensity Hue Saturation (IHS), Principal Component Analysis (PCA), Wavelet-Intensity Hue Saturation (Wavelet-IHS), Fast Fourier Transform-Intensity Hue Saturation (FFT-IHS). To compare and analyze the results of the these methods, the criteria for evaluation of the quality of spectral and spatial include correlation coefficient, signal to noise ratio, RMSE, filtered correlation coefficient, SAM and ERGAS were used. The results demonstrate that the FFT-PCA method achieve more precision in image fusion. This method acts more efficient than other methods in terms of information and spectral content preservation of Hyperion and IKONOS images. This method also shows very good performance in preservation of spatial content for IRS and IKONOS images.

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 Recently, a new mode is proposed in Dual Polarimetry (DP) imaging systems that is called Compact Polarimetry (CP) which has several important advantages in comparison with Full Polarimetry (FP) such as reduction ability in complexity, cost, mass, and data rate of a Synthetic Aperture RADAR (SAR) system. Despite these advantages, the CP mode, compared to the FP mode, still achieves less information to be extracted from targets. Therefore, accuracies of classification obtained from CP data are lower than those obtained from FP data. In this paper, a new method is proposed to improve the results of classification obtaind by using CP data. For this propose, two ways are considered. First, the CP modes simulated by RADARSAT-2 FP mode, and second, Pseudo Quad Polarimetry (PQ) modes reconstructed by exploited CP modes are combine in the extracted polarimetric feature level. Results of this study show that this combination can be increase the classification accuracies.

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Estimation of forest biomass has received much attention in recent decades. Airborne and spaceborne (SAR) have a great potential to quantify biomass and structural diversity because of its penetration capability. Polarizations are important elements in SAR systems due to sensitivity of them to backscattering mechanisms and can be useful to estimate biomass. Full Polarimetric Synthetic Aperture Radar (SAR) data used in this research was acquired by SETHI over Remningstorp, a boreal forest in south of Sweden. A new method based on Polarimetric indicators from covariance and coherency matrixes by changing the polarization basis using transformation matrix in the boreal forests at L and P-band is presented. The presented method showed its capability to improve forest biomass estimation. The correlation between biomass and extracted Polarimetric indicators is investigated before and after changing polarization basis. Particle swarm optimization in binary version is used to select optimum Polarimetric indicators and afterward biomass is estimated based on these optimum parameters. Results indicated that maximum correlation between biomass and Polarimetric indicators was in HV and HH-VV polarizations before changing polarization basis. After changing the polarization bases, the results show significantly higher correlation of biomass with the extracted polarization variables. The results have been improved approximately about 6% and 2% in L and P band respectively, after extraction of optimum parameters by particle swarm optimization and using linear regression model for estimation of forest biomass.

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The monitoring of maritime areas with remote sensing is essential for security reasons and also for the conservation of environment. The synthetic aperture radar (SAR) can play an important role in this matter by considering the possibility of acquiring high-resolution images at nighttime and under cloud cover. Recently, the new approaches based on the sub-look analysis for preserving the information of point targets (such as ship) in the spectrum of the SAR image have been proposed. In the sub-look analysis, the correlation of the ships in two sub-look images is preserved. Based on this property, in this paper first by using the second order statistics of polarimetric SAR data and the information of different polarization bases, the complex correlation between sub-look images is calculated. Then, using a criterion dependent on each of the four polarimetric channels and all polarization bases, the identification of ships from the sea is carried out. The proposed ship detection method is implemented on RADARSAT-2 image at C-band of Sanfrancisco area. The experimental results demonstrate that the method can discriminate the ships from background (sea clutter) with optimal contrast and desirable accuracy. The accuracy of the proposed method is about 19 and 17 percent better than other polarimetric methods and about 30, 2 and 35 percent better than the methods based on the spectral analysis.

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This paper addresses an algorithm for forest height estimation using single frequency and single baseline dual polarization radar interferometry data. The proposed method is based on a physical two layer volume over ground model and is represented by using polarimetric synthetic aperture radar interferometry (PolInSAR) technique. The presented algorithm provides the opportunity to take advantages of the dual polarimetric data, i.e, better spatial resolution and wider swath width, in comparison with the full polarimetric data, in forest height estimation application. In this research, a polarimetric optimization method is utilized to choose the optimum volume polarization basis in order to improve the results of the three-stage inversion algorithm. For the performance analysis of the proposed approach, the L-band ESAR data of the European Space Agency from BioSAR 2007 campaign (ESA) which is acquired over the Remningstorp test site in southern Sweden, is employed. The experimental result shows the dual PolInSAR HH/HV data capability in the forest height estimation without decreasing the accuracy of the result compared with the full polarimetric data.  The suggested method leads to the average root mean square error (RMSE) of 4.39 m and the determination of coefficient of 0.66 in the forest height estimation in 15 predetermined stands in comparison with the  LiDAR reference heights.

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Due to their significant impact on the balance of energy on the surface and in the atmosphere, the clouds have always been considered by various researchers in the meteorological and climatological fields. The ability to remotely measure the characteristics and parameters of the cloud has been proven to examine their changes in different locations and times. One of the most important aspects of cloud research is cloud detection in remotely sensed images. The purpose of the present study is to provide a stereographic based technique for detecting clouds according to the height of the clouds with the highest possible spatial resolution and using Geostationary meteorological images. First, a stereo pair is selected on board two platforms of the Meteosat-8 (IODC) and the Meteosat-10, using the SEVIRI Sensor with high resolution spatial resolution (HRV). Then, with respect to the different viewing geometry of the two sensors, both images are reprojected into a similar reference grid and finally, by forming line of sight (LOS) of the two sensors in an epipolar sheet, the parallax value in two images on the clouds is estimated. The advantage of this method in estimating cloud height is that the stereo measurements only depend on the fundamental geometric relationships between observations of the visible components of the clouds. Other estimation methods, require the assumption that the cloud has a local thermodynamic equilibrium and some knowledge about the apparent temperature, etc. But in most cases, the amount of cloud emission is unknown, the temperature profile of the atmosphere is unavailable, and the cloud does not have thermodynamic equilibrium with its surroundings. In this study, a new method for revealing cloud pixels based on cloud height is presented. After estimating the height of the clouds, it is possible to separate the pixels of clouds from the intersection pixels based on the existing altitude difference, and in fact, it is possible to detect the cloud based on the estimated height in pixels. The results of this study indicate the high accuracy and the feasibility of using stereography to detect cloud pixels in satellite imagery. The advantage of our proposed method is the use of cloud-height information that not only increases the spatial resolution, but also helps to extract 3D cloud information, which is of particular importance in the studies of solar irradiance, and other cloud research applications. And finally, having a knowledge in this regard in Iran is also very important, because a new branch of meteorological studies, entitled "Meteorological stereography" will be established in the country and will help to lead to more extensive research in this area.
 

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3D point Clouds have made a significant contribution to airborne and spaceborne observations in recent years. The sensor limitations and processing challenges of these observations have been investigated in several studies. We generated 3D point clouds using the persistent scatterers interferometric SAR method in order to estimate the obstacles heights. By estimating the height error of the topographic model used in PS-InSAR processing, the permanent scatterer's heights were estimated. The height accuracy was improved by making changes in master image selection. Due to the low density as well as SAR side-looking geometry, the derived 3D point clouds do not represent the complete geometry of complications. In this article, the shape completion deep learning neural network was used to increase the point density and complete the shape geometry. By performing learning steps, these networks directly map non-dense and incomplete shape to dense and complete shape geometry. The global features of the incomplete shapes were determined while retaining the details to an optimal extent. The cost criterion of this network is based on the Chamfer Distance, which measures the distance between the non-dense input and dense output point clouds. The PS-InSAR processing was done on 27 images of the Sentinel-1 satellite in Philadelphia city, USA. This output obtained from PS-InSAR will be able to estimate the height of various urban complications. We prepared 30000 individual building datasets for training the network in the urban areas. The amount of obtained loss was 0.048 in the training process and 0.0482 in the network evaluation process. By evaluating the surface elevation model extracted by lidar reference data of the studied area, the average absolute height estimation error was 4. 67 meters which is quite near the worldwide amounts.