hamid ebadi

With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In the present study, a structure based on learning capsule networks has been used to classify hyperspectral images, so that the network structure can have the most optimal generation of features by using a convolution layer and a capsule layer, and at the same time Avoid overfitting the on training data. The obtained results show the high quality of production features in the proposed structure. With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In the present study, a structure based on learning capsule networks has been used to classify hyperspectral images, so that the network structure can have the most optimal generation of features by using a convolution layer and a capsule layer, and at the same time Avoid overfitting the on training data. The obtained results show the high quality of production features in the proposed structure.In order to improve the classification accuracy, the feature extraction approach through the designed network and the classification by the Extreme Gradient Boosting was compared with the classification method by the global deep network. The proposed capsule approach consists of 3 basic layers: 1) Prime caps, which are capsules of size 8 and 32 with 9 × 9 filters and movement step 2, 2) Digitcaps with 10 16-dimensional capsules, and 3) fully connected layer. The results of examining two approaches for deep networking as well as combining capsule networks with XGBoost reinforcement tree algorithm were compared. Approaches such as SVM, RF-200, LSTM, GRU and GRU-Pretanh were considered to compare the proposed approach based on the configurations mentioned in their research.Up in addition to the study and quality measurement of production vector deep features by the proposed method in different classifiers, the ability of deep global networks in the application of classification should also be examined. The results of examining two approaches for deep network and also combining CapsNet with XGBoost show that by using the proposed combined method, images are classified with 99% accuracy on training data and 97.5% accuracy on test data.Up in addition to the study and quality measurement of production vector deep features by the proposed method in different classifiers, the ability of deep global networks in the application of classification should also be examined.The results of examining two approaches for deep network and also combining CapsNet with XGBoost show that by using the proposed combined method, images are classified with 99% accuracy on training data and 97.5% accuracy on test data.

The use of accurate and up-to-date photogrammetric products as a basis for urban planning is very important because cities are complex and dynamic physical and social systems that are constantly changing. Urban planning experts and scientists need a wide range of spatial maps and technologies to make decisions to advance its benefits. This requires providing fast and accurate mapping methods. One of the low-cost and accurate mapping method is UAV-based photogrammetry but the limitations of this method require special solutions for developing the system in urban areas. Currently, most UAV photogrammetry projects are carried out in urban areas, without considering changes in the elevation of the ground and the heights of various features, as well as occlusion points. All three of these cases have a great impact on the accuracy and quality of output products in areas with altitude features. The developed three-dimensional flight planning in this article, in addition to paying attention to the height of various features such as buildings and terrain, makes the scale as uniform as possible and prevents the UAV from hitting obstacles. The flight design also includes a new technique such as oblique or semi-oblique imaging to reduce occlusion points. The proposed method was implemented in an urban area. The results of the algorithm were evaluated based on the accuracy, and quality of point clouds and output products, the accuracy of the checkpoints and comparison with the expected standard errors, scale stability in images, density of point clouds, and reduction of occlusion points. The results show the high quality of the products and the planimetric accuracy of 3.6 cm and the altitude accuracy of 7 and 4 cm at the requested scale of 1.750. Also, the results show that oblique images have a significant effect in generating information in the connection between walls and the ground and the density of building facades in the area. The results show that the design and implementation of the UAV photogrammetric flight planning algorithm based on the topographic model and features is successful.

Although groundwater withdrawal is considered to be the main and most common cause of land subsidence, extraction of oil and gas, or minerals from beneath the earth's surface also results in land subsidence. Land subsidence over oil and gas fields leads to several natural and manufactured disasters such as critical and strategic infrastructure destruction. In addition, it imposes significant costs on the oil industry, which indicates the need to control and monitor land subsidence in oil fields. Due to irrecoverable damage caused by this phenomenon, it is necessary to regularly monitor areas subject to subduction in oil and gas fields. There are different ways to measure crustal deformation in Geomatics engineering based on geodetic and remote sensing technologies. Despite high accuracy, the use of geodetic techniques such as Global Navigation Satellite System (GNSS) to regularly monitor crustal deformation in a wide coverage is limited by some disadvantages like discontinuous data collection, the need for installation of the equipment on the ground or direct contact with the ground which is time- and cost-consuming. InSAR technology has been widely used in recent decades to monitor crustal deformations with high spatiotemporal resolution and lower cost and time. This study aims to present a new methodology based on the Persistent Scatterer-InSAR (PS-InSAR) method to investigate the correlation between land subsidence and petroleum extraction in the southwestern region of Iran with active oil and gas fields during two periods from 2017/04 to 2017/10 and from 2019/04 to 2019/10, respectively with the highest and lowest petroleum extraction. The results showed that subductions of 10 to 30 cm during the period of 2017/04 to 2017/10 with an annual rate of 20 to 50 cm have occurred in southwestern Iran around the active oil fields which might be due to the significant petroleum extraction since 2016.

By improvement of the spatial resolution of remote sensing images, more accurate information are provided from the image scene such as texture structures. However, extraction of land cover information from these datas has become a challenging process due to the high spectral diversity and the heterogeneity of surface materials. Visual interpretation is costly and time consuming and automatic interpretation of images does not necessarily lead to high accuracy. Achieving optimal interpretation accuracy requires the design of automatic algorithms that are capable of dealing with the complexity of the image scene. To overcome this problem, object-based image analysis (OBIA) that is sensitive to the image scene morphology, can be particularly effective in an urban area where the density of man-made structures is high. In object-based classification, pixels of a segment are analyzed in combination with each other. So the dimensions of the problem space are reduced, in compared to the pixel-based method, which leads to increasing the computational speed. Meanwhile, due to the different sizes of image segments, supervised object-based classification faces challenges in creating an optimal training set. In this research, AdaBoost algorithm was selected for the object-based classification, to overcome the problem of feature space imbalance, due to the small number of training samples in comparison with the high dimensions of the feature space (including spectral, spatial and geometric features), two strategies were proposed. In the first approach an active learning mechanism was integrated with AdaBoost to produce optimal training data set (OTD) and in another approach based on the feature-to-feature correlation (redundancy) and the feature-to-class correlation (relevance), the candidate feature subset (CFS) was generated to reduce the size of the feature space. To evaluate the proposed method, the developed algorithm was performed on the standard dataset of Vaihingen in Germany and the results were compared with the pixel-based classification. In order to evaluate the signification of the results, the McNemar statistical test was used. The experimental results showed that the proposed object-based approach improved the overall accuracy by 6% and the kappa coefficient by 7% compared to the pixel-based approach. Also, the computational speed of proposed object-based AdaBoost was significantly increased compared to the pixel-based approach. These results indicate the superiority of the proposed approach both in terms of accuracy and processing speed.

Reference evapotranspiration (ET0) is a major research area of both hydrology and water resources management, especially in irrigation agriculture. The most important and direct application of ET0 is in the field of irrigation. One of the conventional methods for estimating reference evapotranspiration using meteorological data is the Penman-Monteith-FAO equation. This equation due to satisfactory results has been used in a variety of climates around the world. However, the lack of necessary meteorological data makes it difficult to estimate spatially distributed ET0 using the FAO-PM method in the wider ungauged areas. Penman Monteith method requires air temperature, wind speed, relative humidity, solar radiation and etc.To overcome the existing limits of the FAO-PM model, various attempts aiming to estimate ET0 with limited observed data have been conducted. At present, remote sensing methods are the only way to obtain the various variables at the temporal and spatial scales needed to estimate evapotranspiration. In recent years, several algorithms have been proposed to estimate reference evapotranspiration using remote sensing data. Some of these models, which are based on the relationship of energy balance, are called surface energy balance methods. In addition to remote sensing, data analysis techniques based on machine learning (ML) are more frequently used in agricultural studies in recent years, especially in evapotranspiration. Therefore, analyses performed with ML algorithms, when coupled with remote sensing data, have the potential to predict the biophysical variables, mainly due to the adaptive capacity of the models to find patterns in nonlinear behavior variable, such as ET0. Machine learning methods are well known and have been widely used in other engineering sciences. The purpose of this study is to estimate the reference evapotranspiration using machine learning algorithms and remote sensing data and finally to analyze the algorithms used. In general, the final results of evapotranspiration estimation depend on factors such as the type of data and the method for estimating evapotranspiration.In this study, the standard method of estimating ET0 with meteorological data, Penman-Monteith FAO equation has been used. The NDVI vegetation index indicates the amount of vegetation on the ground and is sensitive to the early stages of phenology. But the enhanced vegetation index (EVI) minimizes atmospheric effects and differences in blue and red reflections. The SAVI index is used to calculate the vegetation of the land surface that has moderated the effect of soil on it. Three machine learning algorithms were introduced to train the ET0 models, including random forest (RF), gradient boosting regressor (GBR) and support vector regression (SVR). Random forest are one of the machine learning methods that performs classification and regression using Bootstrap and Bagging methods. In this research, three machine learning algorithms with different input data (vegetation indices and all bands of Landsat 7 and 8) are used and after comparing the results, the best model was selected. Performance Evaluation Indicators to compare and evaluate the performance of the studied models, the parameters of mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2) and correlation coefficient (CC) are used. Finally, according to the results of the two approaches used in this study, using the values of all Landsat bands, the reference evapotranspiration can be estimated with more accuracy.Accurate estimating of reference evapotranspiration is necessary to estimate irrigation needs and in general, to accurately manage water resources. Conventional methods of measuring evapotranspiration are reference using meteorological data. These measurements are point-based, so they are only suitable for very small scale areas. At present, remote sensing methods are the only non-terrestrial way to obtain the various variables at the temporal and spatial scales needed to estimate reference evapotranspiration. In order to reduce the dependence on climatic data and better resolution, machine learning methods are used to calculate the reference evapotranspiration. In this research, RF, GBR and SVR models have been used. In the present study, there are two approaches Used. In the first approach, the values of all bands of Landsat images are as model input, while in the second approach, vegetation indices are calculated with only a few bands of Landsat images and then used as model inputs. By examining, it can be seen that the information obtained from the Landsat image bands is related to the phenological behavior of the products, and it is also possible to contract very relevant information related to agricultural products that are examined temporarily and spatially. One of the factors influencing the accuracy of estimating reference evapotranspiration is the use of other Landsat bands in addition to the bands related to vegetation indices.

Remote 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.
 

2-1- Study Area The study area is central part of the city of Tabriz East Azerbaijan Province, which is located in northwestern of Iran. 2-2- Data Various 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-

The 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.

According 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.

Due 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.

Iran is one of the countries exposed to many natural disasters, of which the flood phenomenon is one of the most serious. According to official statistics, more than 50% of casualties due to natural disasters in Iran are due to floods. Accurate and timely information is needed to plan any flood management measures. Currently, many researchers have examined the methods of obtaining water area information. Among these methods, land use classification and change analysis are the most important applications of remote sensing techniques. One of the most widely practical classifications is the separation of water class from other classes.Also, Landsat images are one of the most common data sources in this field. In this regard researches have been done in the field of identifying water areas, due to its high importance in flood crisis management with the advancement of satellite technology in remote sensing. In this study, an automated method has been developed to prepare flood change maps and use different capabilities of different water and humidity indicators to provide automated training data. This can reduce the challenge of operator interferences. Besides, the efficiency of the high level of automation in the extraction of flooded areas in accelerating and facilitating the management of this crisis is indisputable. Therefore, when manual training data is not available, applying a fully automated approach will be helpful. The integration of indicators can also be effective in improving the accuracy of existing water indicators. Floods are one of the most important natural disasters in the world that occurred due to different factors such as human encroachment on rivers and vegetation damages. The purpose of this study is to develop an automated method for preparing training data for supervised classification of images and using the capabilities of integrated indicators to identify flooded areas. Therefore, several approaches for flood in Golestan province, 2019, were implemented by Landsat-8 data and the results of each were examined. The Kmeans clustering algorithm, Otsu, Multi and Adaptive thresholds were used to generate automated training data based on different water indices (NDWI, AWEI and MNDWI; Then the ANN, SVM, ML, MD and BE classifications) was implemented to monitor flood changes. The results compared with the manual classification method indicated that the proposed approach, in addition to providing a high-level of automation in monitoring changes, also had high efficiency and accuracy. In another development approach, different water and moisture indices were combined with the aim of improving the production process of automated training data. Among the 85 tests performed, the combined approaches had the highest accuracy. Due to the nature of different water indicators, their selection and appropriate combination, in addition to reducing the noise in the water indicator image was also effective in increasing the ability to identify impure water areas. Finally, a comprehensive deductive analysis of automated methods and hybrid indicators was carried out to help to manage flood crisis, facilitate and accelerate the process. It can be said that with the advancement of technology and the emergence of various aerial / satellite images, many opportunities and possibilities have been created for effective and sustainable management. One of the best ways to extract information from digital images is to classify the image. One of the challenges in image classification is to reduce the interference of users using algorithms that have high level of automation. This issue has high importance due to the sensitivity and sudden occurrence of the flood crisis. Therefore, to promote this challenge, in this research, an automated and efficient approach for supervised classifications with emphasis on automatic production of training data to prepare a map of flood changes is presented. In total, 85 different studies were performed to obtain flood change maps, using hybrid and non-hybrid automated methods to prepare automated training data for supervised classifications. It is considered that the obtained results had acceptable accuracy. For example, in all approaches studied, ANN classification offered better accuracy, which could be seen from the diagrams provided. Also, due to the lack of significant difference in the accuracy of the maps, obtained from this classification with different indicators and thresholds, it can be said that this classification is also highly robust. The results indicate that ANN classification in combined approaches with Otsu and Adaptive thresholds has the highest accuracy and maximum robustness. In comparison with methods related to execution speed, ML and MD classifications also provided good performance. However, the difference in the accuracy of these classifications compared to the ANN classification is not significant in some cases and will be possible to replace due to higher execution speed. Finally, by analysis of the results, it can be stated that the automated approaches proposed in this research can be replaced by traditional classification methods.

Natural hazards have always had devastating effects on human life, of which the flood is one of the most serious. Therefore, providing rapid flood identification methods for crisis management is necessary. The purpose of this study is to provide a method with proper accuracy and speed in preparing flood maps. In this study, two time series of Sentinel-1 and Landsat-8 data were used to prepare a flood intensity map by integrating statistical calculation and index extraction. The proposed algorithm is that first the map of permanent water surfaces is automatically prepared by optical images over a period of 5 years. Then, to determine the flood intensity in different regions, statistical calculations are used on the time series of radar images, and finally, using the Normalized difference flood index, which can quickly identify the flood, the final flood map is obtained. The proposed approach has been implemented following the occurrence of the flood of 1398 in two regions of Golestan and Khuzestan, which have different geographical conditions. Assessments performed with the help of ground truth maps and confusion matrices, and in addition, McNemar test was used for more complete analysis. The implementation of the algorithm in the Google-earth-engine environment showed that this method, in addition to having high accuracy, allows the use of hundreds of images without the need for special hardware. The overall accuracy in a period of time in Golestan and Khuzestan was 91.84 and 97.36, which indicates the high generalizability of the algorithm in regions with different extent.

Flood is a natural hazard that causes many deaths each year, and due to the effects of climate change, the number of occurrences is increasing worldwide. Therefore, natural disaster damage assessment, such as floods, provides important information to support decision-making and policy development in the field of natural hazard management and climate change planning. In this regard, in recent years, various methods for classifying remote sensing images have been developed, which always face challenges in differentiating a variety of land uses. Another challenge in flood crisis management is the lack of access to satellite imagery with high temporal resolution while maintaining spatial resolution, which is more pronounced in the presence of cloud cover in the area and occurs during floods. The purpose of this study is to identify flooded areas in Khuzestan province following the flood of 1398, which is based on the integration of optical images of Sentinel 2 and MODIS to produce a time series with relatively good spatial and temporal resolution. In order to classify and prepare maps, a patch-based hierarchical convolutional neural network has been designed, which solves the challenge of extracting deep features due to the relatively weak structure of images with a resolution of more than 10 meters. In addition, the effect of different neighborhood dimensions on the extraction of deep features in all images has been investigated. Finally, the area of damage to urban land cover and various agricultural lands has been estimated consecutively during the flood period.

The data used in this research are two series of different satellite images including Sentinel-2 MSI Level-1C images with a spatial resolution of 10 meters and the product of MODIS daily surface reflectance (MOD09GA) with a spatial resolution of 500 meters. The general process of implementing this research can be summarized in 7 phases. In the first phase, the data is first pre-processed. Then, in the second phase, the image fusion algorithm is implemented to predict the daily surface reflectivity of the images, and if the error and accuracy of the predicted images are appropriate, the time series of the flood period is obtained. In the third phase, Ground truth maps are prepared by the researcher using image interpretation. In the fourth phase, training samples are prepared from these data to perform various classifications such as deep learning approaches and machine learning, and the proposed network is implemented in different input dimensions. It should be noted that the number of training and validation samples in deep learning networks has been very limited and less than half a percent of images to automate and reduce user dependence. In the fifth phase, to perform damage assessment in the agricultural and vegetation regions, the relevant maps are prepared with the best approach tested in the previous phase, and finally, in the sixth phase, accuracy assessments are performed by the confusion matrix and related criteria. In the last phase which is the seventh phase, the area of flood-affected land uses is estimated.

The present study is implemented to improve one of the most important issues in crisis management in the country, namely the assessment of damage caused by the sudden phenomenon of floods. Therefore, presenting a method with appropriate speed compared to existing methods and also increasing the accuracy of final maps due to its challenging has been one of the objectives of this research. First, in order to prepare a suitable time series of optical data with an appropriate spatial and temporal resolution, the ESTARFM fusion algorithm was used. According to the evaluations performed for the two integrated images, this algorithm has high efficiency and accuracy in areas with heterogeneous coverage. Due to the change in environmental conditions between the images, the maximum errors have occurred in water-sensitive bands, but all errors due to their small values in each band indicate the efficiency of the algorithm used. In addition, since the two images are predicted in time series, so the generalizability of the algorithm has been investigated and proven. Furthermore, regarding the classification algorithms for preparing the destruction map, the proposed neural network has a significant difference in accuracy compared to other approaches. In addition, in the study of the extracted classes, in the proposed approach, the built-up areas benefit from a very high identification compared to other algorithms and the appropriateness of other uses, especially the use of water areas, is maintained. According to the studies, the highest rate of flooding in the study area was in the third week of April and after that, the area has been experiencing a decreasing trend. Therefore, the damage was estimated on April 14 and 21. According to the assessments, flooding has decreased from April 14 to April 21 in built-up areas, rainfed and fallow lands, and has increased in wetland and Aquatic cultivation areas.

In this research, the ESTARFM image fusion algorithm, which is known to be suitable for combining images in heterogeneous regions, has been used for April 8 and April 14 images, and the evaluations have been done with the help of scatter plots and least-squares error. The results showed the efficiency of the method in integrating relatively high-resolution Sentinel 2 images and low-resolution MODIS images in the field of flood management. In the field of identifying flooded areas and further due to the poor structure of images with a resolution above 10 meters, the possibility of extracting optimal and deep features is difficult. In the present study, a patch-based convolutional neural network has been designed with a minimum of layers and hyper-parameters, which provides the possibility of training from scratch with the least amount of training samples and without overfitting for images with different environmental conditions. Also, in order to find the optimal state, the dimensions of different inputs in all images have been tested to make a comparison of the effect of different neighborhoods. Thus, patches of sizes 3 to 11 were tested, patches 5 and 7 in the pre-flood image, and patches 9 and 11 in the post-flood images were the best. The results were compared with approaches such as object and pixel-based SVM, LCNN, and DCNN neural networks with dimensions of 3 × 3 and 5 × 5 according to the reference research, and had a significant improvement in accuracy. Time evaluations were performed between all approaches and the lowest time was related to the proposed approach with patch dimensions of 3 × 3 and 5 × 5 and the highest time was related to DCNN network with dimensions of 5 × 5. However, due to the importance of time in crisis management and the need to prepare a high-speed map, the proposed approach has provided an appropriate response. If the time and accuracy are proportionally considered in implementing the research, the designed network in 9 × 9 input dimensions is recommended because in this case, both the accuracy and the time superiority are satisfied.

Monitoring the earth and its biosphere is an essential task in any scale to achieve a sustainable development. Therefore, forests, as an invaluable natural resource, have an important role to control the climate changes and the carbon cycle. For this reason, biomass and consequently forest height have been known as the key information for monitoring the forest and its underlying surface. Several studies, it has been shown that Synthetic Aperture RADAR (SAR) imaging systems can greatly help to this purpose. In this framework, a novel technique called Polarimetric SAR Interferometry (PolInSAR) is an appropriate and an available tool for forest height estimation, due to its sensitivity to location and vertical distribution of the forest structural components. Based on this, from a view point, the methods employed in this field can be divided into two categories: a) based on Random Volume over Ground (RVoG) inversion model, and b) based on model-based decomposition techniques of PolInSAR data. In this study, in order to improve the forest height estimation, a novel method based on the combination of two mentioned categories has been proposed. The performance and the efficiency of the proposed method were demonstrated by two datasets simulated from the PolSARProSim software in L and P bands. So that, in L band, it obtained 0.69 m, 1.84 m and 3.38 m improvements for height estimation in Digital Elevation Model (DEM) differencing, coherence amplitude and combined (phase and coherence) methods, respectively. As well, in P band, in it obtained 1.53 m and 2.74 m improvements especially in DEM differencing and coherence amplitude methods, respectively.

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. 

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. 

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.

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.

 

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.  

   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.  

   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%.  

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.

Land cover classification in remote sensing imagery is one of the most widely used spatial information extraction methods, which can facilitate generating object imagery classes of the ground surface in order to automate and accelerate meeting the basic needs of management, organization, and exploitation of the environment. Due to the similar behavior of pixels, remote-sensing image classification using merely the spectral and textural information would lead to inefficiency in the classification. In fact, in classification process, objects are commonly identified using spectral properties of image pixels. If the spatial and conceptual properties are also considered, it causes to a better distinction between image classes and closes the machine process to human interpretation and adds to the system's performance. The present research is mainly focused on the use of interactive segmentation and interpretation processes with respect to the geometry of the image classes. The accuracy of the results have improved by introducing the knowledge-based rules to control and regulate the interactive process, taking into account the geometric properties of target classes. To evaluate the efficiency of the proposed method, the results were evaluated and compared with some of the other methods on IRS satellite images in an urban area. The results showed that geometric and conceptual features as a complementary information source, improve classification results in the urban area with heterogeneous spectral effects. Overall, the proposed hybrid technique improved overall accuracy and Kappa coefficient by %8 and 11.18, respectively.

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 the common 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 demonstrated that 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.
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.
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 matrixes in arbitrary polarization basis were extracted by altering  and  in 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 geometrical parameters, in transformation matrix (e.g. RVOG phase and DEM difference) significantly increase the tree height accuracy.On the other hand, methods that only apply magnitude of coherence such as coherence amplitude method do not show notable improvement for retrieving tree height.
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.

Analyzing multi-temporal remotely sensed images is a powerful technique for monitoring land use and land cover changes in urban areas. Apart from the change detection (CD) technique, the features space have an enormous impact on the CD accuracy.To achieve satisfactory CD results in urban area, optimum selection of textural and spectral features is necessary. Although an exhaustive search guarantees the optimality of the selected features, but it is computationally prohibitive. Data reduction techniques such as PCA considers the independence of the data to find a smaller set of variables with less redundancy without intending to improve the CD accuracy. Difficulty in setting the best threshold for JM distance in separability analysis algorithm (SAA) reduces its efficiency. The aim of this paper is finding the optimal textural and spectral features to enhance the CD accuracy using genetic algorithms (GA) and Bayesian classifier. To evaluate the effectiveness of the proposed approach, a case study using IRS-P6 and GeoEye1 satellite imagery acquired on July 15, 2006, and September 1, 2013, respectively, from Sahand New Town (Northwest of Iran) was performed. All the mentioned feature selection methods (PCA, SAA and proposed GA-based method) were implemented in MATLAB R2013a. Results show that, textural features provides a complementary source of data for CD in urban areas. Results show that features selection is an effective procedure in change detection based on textural and spectral features. Each of feature selection methods has its own limitation and advantages, but in general they increase the CD accuracy. The proposed GA-based feature selection approach was found to be relatively effective when compared to PCA and SSA approaches. Overall accuracy and Kappa coefficient of CD were increased from 53.66% to 88.49% and 58.94% to 90.39%, respectively using proposed method in compared with that using only spectral information.

Tuberculous spondylitis is not an uncommon disease of the spine. Near one percent of all cases of spinal tuberculosis (TB) involves craniocervical junction. Hypoglossal nerve palsy is not an uncommon neurological finding, but isolated involvement of the hypoglossal nerve is rare and limited to case reports or small case series. Here, we report a case of craniocervical junction tuberculosis presenting with unilateral hypoglossal nerve palsy. Case is a 41-year-old woman with neck and suboccipital pain since one month and unilateral right hypoglossal nerve palsy since one week. All laboratory tests were unremarkable except raised ESR level. Involvement of C1-C2 and hypoglossal canal were demonstrated by CT scan of craniocervical junction. Tissue diagnosis of TB was established by open biopsy of the craniocervical junction.

Precise player localization is the key step for improved analysis such as player tracking in soccer broadcast videos. Extracting player trajectories provides some essencial information for coaches and sport experts to determine weaknesses and strengths of the players and the team and to evaluate overall strategy of the game. As far as previous scenes are missed by camera motion, continuous player trajectory could be depicted by trajectory extraction on constructed mosaic. The goal of this paper is to transform player position to the field model and extract all trajectories in both image and model spaces. Therefore, players are detected using Gaussian Mixture Model and Gentle Adaboost. After removing extra regions such as goal post and lines, isolating occluded players, player labeling and transforming player positions to the same coordinate system, player tracking is carried out. Finally, the proposed player detection and tracking methods are applied for 78 frames taken from six soccer sequences.

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.