Hadi Farhadi; Tayebe Managhebi; Hamid Ebadi
Abstract
Extended Abstract1- IntroductionRemote Sensing (RS), as one of the most efficient mapping technologies, is employed in wide areas due to its speed, cost-effectiveness, monitoring over wide areas and using time series data. So far, several data and methods are used for this purpose. In general, RS active ...
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Extended Abstract1- IntroductionRemote Sensing (RS), as one of the most efficient mapping technologies, is employed in wide areas due to its speed, cost-effectiveness, monitoring over wide areas and using time series data. So far, several data and methods are used for this purpose. In general, RS active and passive sensors provide useful information in various applications such as building extraction, natural resource management, agricultural monitoring, etc. The extraction of accurate information about the location, density and distribution of buildings in the urban areas is one of the major challenges in the urban study which is used in various applications. In this framework, the monitoring of the urban parameters, such as urban green space, public health, and environmental justice, urban density and so on has been accomplished by radar and optical image processing, in the last three decades. So far, various methods, including Artificial Intelligence (AI), Deep Learning (DL), object-based methods, etc. have been proposed to extract information in the urban areas. However, an important issue is access to the powerful computer hardware to process the time-series images. In such a situation, the use of the Google Earth Engine (GEE) as a web-based RS platform and its ability to perform spatial and temporal aggregations on a set of satellite images has been considered by many researchers. In this research, a semi-automatic method was developed building extraction in Tabriz, northwest of Iran, based on the satellite images using the GEE cloud computing platform. Since accessible data is one of the most important challenges in the use of space RS, in this study, the free Sentinel-1 and sentinel-2 data, which belongs to the European Space Agency (ESA), has been utilized. 2- Materials & Methods2-1- Study AreaThe study area is central part of the city of Tabriz East Azerbaijan Province, which is located in northwestern of Iran. 2-2- DataVarious data sources have been used in this study, including Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). In addition, 400 training samples were created using High-Resolution Google Earth Imagery (GEI) in two classes: urban-residential (buildings) and non-residential areas (vegetation, soil, road, water and etc.).2-3- MethodologyThe goal of this research is to develop a method for identifying the buildings in an urban area. For this purpose, after importing images and pre-processing them in the GEE Platform, a map of the Primary Urban Areas (PUA) and High-Potential Buildings (HPB) was produced from Sentinel-1 images according to the sensitivity of the radar images to the target physical parameters. Then, in order to remove the annoying features and extract the Secondary Urban Areas (SUA), spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Renormalized Difference Vegetation Index (RDVI), Normalized Difference Water Index (NDWI), Soil Extraction Index (SOEI), Normalized Difference Built-up Index (NDBI), and Build-up Extraction Index (BUEI) were extracted from Sentinel-2 images. Also, the high slope of the area and the mountainous areas was extracted from the SRTM DEM data and used as a mask in the final results. Afterwards, the unimodal histogram thresholding method was used in order to determine the threshold value for each index. Finally, by merging the map of HPB and the map of SUA, the final map was produced and evaluated by other methods. In this research, the proposed method used images from GEI with a very high spatial resolution to validate the generated map. As a result, sampling was carried out using a visual interpretation of GEI in two classes: residential areas (buildings) and non-residential areas. The samples were selected randomly and 400 points were collected for each residential and non-residential class. In the study area, a total of 800 test points were used to evaluate the results of the proposed method. To evaluate the accuracy of the results, the criteria of overall accuracy (OA), kappa coefficient (KC), user accuracy (UA) and producer accuracy (PA) were used. 3- Results & DiscussionAccording to the visual interpretation, all buildings in urban areas with a length and width greater than 10 meters (spatial resolution of the four major bands of Sentinel2) can be extracted using the proposed method in this study, and the results are acceptable in various features. According to the proposed method, annoying features such as vegetation and water body areas were removed from the building identification process with high accuracy, and the accuracy in the study area was improved. The results showed that the OA and KC were 90.11 % and 0.803, respectively. Based on the quantitative and qualitative comparisons, the proposed method had a very satisfying performance. 4- ConclusionDue to the spectral diversity and the presence of various features in urban environments, preparing a map related to it in a large area is extremely difficult. In this regard, the current study presented a very fast semi-automatic method for preparing the urban area map and extracting buildings in Tabriz using Sentinel-1 and Sentinel-2 satellite images as a time series in the GEE platform. One of the most significant benefits of the proposed method is that the data and processing system used in our study is free. Thus, in addition to not having to download large amounts of data, the method presented in the current study has the ability to eliminate many of the limitations of traditional methods, such as classification methods and their requirement for large training samples. The proposed method did not extract the map of buildings using heavy and complex algorithms, which was an important consideration in the discussion of computational cost. Therefore, it can be concluded that the simultaneous use of Radar and optical RS data in the GEE Web-Based platform has a very high potential in distinguishing features and building mapping.
Amir Aghabalaei; Hamid Ebadi; Yasser Maghsoudi
Abstract
Extended Abstract Introduction Monitoring and assessment of the biosphere are two essential tasks at any scale. Based on this, forests play an important role in controlling the climate and the global carbon cycle. For this reason, biomass and consequently forest height are known as the key information ...
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Extended Abstract Introduction Monitoring and assessment of the biosphere are two essential tasks at any scale. Based on this, forests play an important role in controlling the climate and the global carbon cycle. For this reason, biomass and consequently forest height are known as the key information for forest monitoring. In the recent decade, several studies have shown that the Synthetic Aperture RADAR (SAR) imaging systems in Compact Polarimetry (CP) mode can overcome the disadvantages of Full Polarimetric (FP) SAR imaging systems and provide a good performance in various remote sensing applications such as monitoring and managing the important natural resources like forests. In this regard, a novel technique named Polarimetric Interferometry SAR (PolInSAR) has been further considered as a powerful tool for forest height estimation. Materials & Methods In this research, the performance of the Compact PolInSAR (C-PolInSAR) data in Dual Circular Polarization (DCP) mode has been investigated in order to retrieve the forest height. For this reason, the common methods which are used for forest height estimation including Digital Elevation Model (DEM) differential method, coherence amplitude inversion, and phase & coherence inversion methods were applied and implemented on these data. In all of the aforementioned methods, LL+RR and LR polarizations were considered as the selected channels for estimating the volumetric and ground coherences, respectively. Then, the estimated coherences were considered as the input parameters for all of the mentioned methods. Results & Discussion To evaluate the performance and the efficiency of C-PolInSAR data in DCP mode, the results obtained from these data were compared with those obtained from Full PolInSAR (F-PolInSAR) data. The results obtained in this study in two datasets simulated from PolSARProSim software in both L and P bands showed that the C-PolInSAR data in DCP mode yielded a similar result compared to the F-PolInSAR data for forest height estimation (when the HH+VV polarization is adopted as the ground backscattering), because, in this case the LL+RR and the LR polarizations are equal to the HV and the HH+VV polarizations, respectively, particularly, the C-PolInSAR data in DCP mode yielded 0.78 m and 0.55 m improvements for forest height estimation in L and P bands, respectively. In addition, all of the employed methods provided better and closer results compared to the real forest height (i.e. 18 m) in L band compared to P band, because the electromagnetic (EM) waves have a more penetration into the canopy in L band compared to P band. Thus, the attenuation of these waves is low and consequently the height estimation is more accurate. Without considering the used bands, the DEM method provided the lowest precision compared to other methods, because the HV (or LL+RR) phase center can lie anywhere between half the tree height and top of the canopy. The exact location of this phase depends on two vegetation parameters which are the wave mean attenuation and the vertical canopy structure variations. In this case, the trees have very thin canopies, and consequently, the attenuation is small, but the phase center is high due to the structure. In other words, when the canopy extends over the entire forest height, then the phase center can be at half the true height for low density (low attenuation), through to the top of the canopy for dense vegetation (high attenuation). This ambiguity is inherent in single baseline methods, and in order to overcome this, model-based correction methods need to be employed. It was also observed that the coherence amplitude method is among the weak algorithms due to ignoring the phase and its sensitivity to the attenuation and structural variations but it can be used as a backup solution when other approaches fail. Finally, the phase and the coherence inversion method had better results than two aforementioned methods for the forest height estimation. In this method, selecting the factor ‘’ is very important and it should be selected in a way to be strong towards the attenuation changes. In this study, = 0.4 was adopted to maintain the height error variations. Conclusion As the final result, the C-PolInSAR data can be an efficient strategy due to its performance, when the full polarimetric imaging systems are either limited or not available. Moreover, utilizing these data in long wavelengths (e.g. P band) is more appropriate due to the effect of the Faraday rotation on the transmitted polarization.
Fariborz Ghorbani; Hamid Ebadi; Masoud Varshosaz
Abstract
Extended Abstract Introduction In the past few decades, urban environments have expanded much larger than before. One of the most important problems in most metropolises and even small cities is the management of the transportation system. An advanced monitoring system of urban vehicles ...
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Extended Abstract Introduction In the past few decades, urban environments have expanded much larger than before. One of the most important problems in most metropolises and even small cities is the management of the transportation system. An advanced monitoring system of urban vehicles allows for overcoming the traffic problems. With the development of unmanned aerial vehicles (UAVs), continuous and accurate monitoring of urban environments has been provided for the users. In this research, an efficient method is presented that detects the vehicle in the UAV images. The proposed method is effective in terms of computational speed and accuracy. Materials and Methods The foundation of the proposed method is based on the characteristics of the local features in the UAV images. The presented method consists of two main stages of training classification model and detecting vehicles. In the first part, local features are extracted and described by the SIFT algorithm. The SIFT algorithm is one of the most powerful algorithms for extracting and describing local features that are used in various photogrammetry and machine vision applications. This algorithm is robust to geometric and radiometric changes of the images. Due to the high dimensions of extracted features from all the training samples, the BOVW (Bag of Visual Word) model has been applied. This model is used to reduce the dimensions of the features and display the images. Simple and efficient computing is one of the significant features of the BOVW model. At this stage, after producing a library of features, the SVM classification model is trained. In the detection part of the algorithm, the images are entered into the algorithm and the local features are extracted in all images by the SIFT algorithm. The BOVW model is often used to display an image patch. In most researches, this model is implemented by applying a search window to the whole image. This type of methods has a higher confidence level in detection, but it is a very time consuming process and increases the volume of the computations. For this purpose, the approach of points clustering and their representation by the BOVW model is proposed. In this method, features that are within a certain range are considered as a cluster. Euclidean distance is used in image space for clustering. Then, the clusters produced by the BOVW model are displayed. Then, a feature vector is constructed for each cluster. The trained SVM is applied to each of the production vectors and each cluster is classified as a vehicle and non-vehicle. If the cluster is detected as a car, the position of the center of that cluster is marked on the image. Results and discussion The proposed method was implemented on 8 images with a number of different car targets. Also, considering the use of the search window approach in many researches, our results were compared with the results obtained by other researchers. The results show that the calculation time of the proposed method is 82 seconds, while the search window method takes 2496 seconds to run. In order to verify the accuracy of the algorithm, two criteria were used. The first criterion is the “Producer's accuracy”, which represents the proportion of correct detections of the vehicle to the entire vehicles existing in the images. This criterion is 75.79% for the proposed method. The second criterion is the “User's accuracy”. This criterion is obtained by dividing the correctly detected samples into the sum of the correctly and incorrectly detected samples. The User's accuracy criterion has been reported to be 59.50%. Conclusion The value of the Producer's accuracy criterion is greater for the search window method which has led to a more accurate detection of vehicles compared to our method. This is due to the small moving steps of the search window in the entire image. However, the search window method has increased the amount of the time spent on the calculations. The User's accuracy criterion shows that the proposed method has less incorrect detections. The results indicate that our method has a higher degree of reliability. The average of these two criteria indicates the superiority of the proposed method in terms of the accuracy of the calculations. On the other hand, the proposed algorithm has a great advantage in terms of computational speed compared to the search window method.
Samira Hosseini; Hamid Ebadi; Yasser Maghsoudi
Abstract
Extended Abstract
Introduction
Estimation of forest biomass has received much attention in recent decades including assessing the capability of different sensor data (e.g., optical, radar, and LiDAR)and the development of advanced techniques such as synthetic aperture radar (SAR),polarimetry and polarimetric ...
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Extended Abstract
Introduction
Estimation of forest biomass has received much attention in recent decades including assessing the capability of different sensor data (e.g., optical, radar, and LiDAR)and the development of advanced techniques such as synthetic aperture radar (SAR),polarimetry and polarimetric SAR interferometry for forest biomass estimation. Accurate estimation of forest biomass is of vital importance to model global carbon cycle. Deforestation and forest degradation will result in the loss of forest biomass and consequently increases the greenhouse gases. Radar systems including SAR have a great potential to quantify biomass and structural diversity because of its penetration capability. These systemsare also independent of weather and external illumination condition and can be designed for different frequencies and resolutions.Moreover, SAR systems operating at lower frequencies such as L- and P-band have shown relatively good sensitivity to forest biomass. Regression analysis is among thecommon methods for evaluation forest biomass which have been investigated for many years on different areas. This analysis is based on the correlation between backscattering coefficient values and the forest biomass. However, previous studies demonstratedthat such approaches are very simple and they do not consider structural effects of different species. One of the restrictions and limitations of these methods is the low saturation level. The level of saturation is lower in higher frequencies and vice versa. Considering the structural parameters, researchers have tried to use the interferometry techniques.Forest canopy height is one of the important parameters that can be used to estimate Above Ground Biomass (AGB) using allometric equations.
Materials &Methods
Recentforest height retrieval methods rely on model based interferometric SAR analysis. The random volume over ground (RVOG) model is one of the most common algorithms. This method considers two layers, one for the ground under the vegetation and one for the volumetric canopy. This model has been investigated in different forest environments (e.g. tropical, temperate and boreal forests). Estimation of forest biomass based on forest height using allometric equations can overcome radar signal saturation to some extent.Improvement of Forest height estimation can play an important role to retrieve accurate forest biomass estimation. In this paper, a new method using scattering matrix optimization is introduced to extract forest height by changing polarization bases. Scattering matrices for slave and master images have been extracted by changing polarization bases. Then polarimetric interferometry coherences have been calculated and forest height was estimated by various forest height methods including DEM Difference, coherence amplitude inversion, RVOG Phase, Combined and RVOG.
Results& Discussion
P-band full Polarimetric synthetic aperture radar (SAR) images acquired by SETHI sensor over Remningstorp (a boreal forest in south of Sweden) were investigated for forest biomass estimation.Mean of Lidar height values which fall in each shapefile was used to check corresponding results with the heights of retrieval methods.
The results of tree height retrieval methods without changing polarization bases between PolInSAR tree height and LIDAR height show that three methods including coherence amplitude inversion, RVOG Phase and RVOG have low R2 value. DEM Difference and combined methods yielded better results in comparison with the other three aforementioned methods; however the results are not satisfactory.DEM Difference method underestimated the tree height compared to that of LIDAR. This is perhaps due to the fact that volume phase center does not lie at the top of the tree.Temporal decorrelation decreases volume correlation, consequently small values in the SINC function lead to generate large values in results; therefore RMSE of coherence amplitude method is relatively high.New master and slave scattering matrices in arbitrary polarization basis were extracted by alteringandin transformation matrix.Results show that RVOG phase has the best result with R2=0.76 and RMSE=3.76. Following this method, DEM difference method shows R2=-0.69.It is likely that methods which include phase information by changing geometricalparameters, in transformation matrix (e.g. RVOG phase and DEM difference) significantly increase the tree height accuracy.sOn the other hand, methods that only apply magnitude of coherence such as coherence amplitude method do not show notable improvementfor retrieving tree height.
Conclusion
Robustness of forest height estimation using Scattering Matrix Optimization by changing Polarization Bases was studied in this paper.PolInSAR data was acquired by SETHI on Remningstorp, a boreal forest in south of Sweden. Results indicated that forest height retrieval methods which included phase parameter shows remarkable improvement by changing the geometrical parameters for height estimation.Therefore RVOG phase method with R2=0.76, RMSE=3.76m and DEM Difference method with R2=-0.69 gave the best results, whereas coherence amplitude method which only included magnitude of coherence with R2=0.17 showed the lowest correlation.
Vahid Sadeghi; Hamid Enayati; Hamid Ebadi
Abstract
Analyzing multi-temporal remotelysensed images is an effective technique for detecting land useand land cover changes in urban areas. Apart from thetechnique used to detect the changes, the features space has an enormous impact on the accuracy of the results. Achieving satisfactory results in detecting ...
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Analyzing multi-temporal remotelysensed images is an effective technique for detecting land useand land cover changes in urban areas. Apart from thetechnique used to detect the changes, the features space has an enormous impact on the accuracy of the results. Achieving satisfactory results in detecting changes inurban areasrequires the use of optimal spectral and spatial features (texture). Although global search is the only guarantees of achieving the optimal set of features, but it is a very timely and impractical process in practice. Data reduction techniquessuch as PCA considers the independence of the data tofind a smaller set of variables with less redundancy withoutintending to improve the CD accuracy. Difficulty in setting thebest threshold for JM distance in Separability Analysis Algorithm (SAA)reduces its efficiency. The main purpose of this paper is to select the optimaltextural and spectral features to enhance the CD accuracy usinggenetic algorithms (GA) and Bayesian classifier. To investigate the effectivenessof the proposed tecknique, a case study using IRS-P6and GeoEye1 satellite imagery taken from Sahand New Town (Northwest ofIran on July 15, 2006, andSeptember 1, 2013) was performed. All of the aforementioned methods of feature selection (PCA, SAA and proposed GA-based method) were implemented in MATLABR2013a. The results show that, textural features provides a complementary sourceof data for CD in urban areas. The results show thatfeature selection is an effective process fordetecting changes basedon textural and spectral features. Each of the techniques for selecting features has its own limitations and advantages, but in general, improve the CD accuracy. The proposed GA-based feature selectionapproach was found to be relatively effective when compared withPCA and SSA approaches. Overall accuracy and Kappa coefficient ofCD were increased from 53.66% to 88.49% and 58.94% to90.39%respectivelyusing proposed methods compared tothe use of spectral information.
Hamid Ebadi; Ruzbeh Shad
Volume 13, Issue 49 , May 2004, , Pages 46-50
Abstract
With increasing development of GIS, the analysis functions applicable by it have also been significantly expanded, including network analysis. Finding the shortest path is among the important network analyses that is considered as one of the important applications in transportation issues. Considering ...
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With increasing development of GIS, the analysis functions applicable by it have also been significantly expanded, including network analysis. Finding the shortest path is among the important network analyses that is considered as one of the important applications in transportation issues. Considering the many applications of routing analysis, the variation in the type and volume of input information and various parameters affecting the efficiency of a routing algorithm in a geographic information system, various solutions have been proposed to solve the routing problem, including the Dijkstra and Genetic Algorithms.Dijkstra's algorithm is one of the most famous methods for finding the shortest route that can be used to find the shortest path in a given network using matrix computations. However, in instant applications, due to large volume of input information, complex constraints and the need for fast performance, this algorithm will lose its effectiveness. As computational volume increases in the network matrix, its time complexity increases as well. Genetic algorithm can be used to solve this problem. Genetic algorithm is an optimization technique that can reduce the amount of computations and number of comparisons by minimizing the search range.In this paper, with a brief overview of the Theory of Graphs, the operation of Dijkstra and genetics routing algorithms are reviewed and results of several practical works are presented. Finally, by comparing and verifying the results, the strengths and weaknesses of each of them will be determined.
