bluuebunny/crossref_metadata_2025 huggingface dataset:
SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY
Authors: Firozjaei M. K. , Makki M. , Lentschke J. , Kiavarz M. , Alavipanah S. K.
Published: October 2019

Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.
HISTORY AND APPLICATIONS OF SPACE-BORNE LIDARS
Authors: Fouladinejad F. , Matkan A. , Hajeb M. , Brakhasi F.
Published: October 2019

Abstract. LIDAR (Light Detection and Ranging) is a laser altimeter system that determines the distance by measuring pulse travel time. The data from the LIDAR systems provide unique information on the vertical structure of land covers. Compared to ground-based and airborne LIDARs providing a high-resolution digital surface model, space-borne LIDARs can provide important information about the vertical profile of the atmosphere in a global scale. The overall objective of these satellites is to study the elevation changes and the vertical distribution of clouds and aerosols. In this paper an overview on the space-borne laser scanner satellites are accomplished and their applications are introduced. The first space-borne LIDAR is the ICESat (Ice, Cloud and land Elevation Satellite) satellite carrying the GLAS instrument which was launched in January 2003. The CALIPSO (the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, 2006), CATS-ISS (the Cloud-Aerosol Transport System, 2015), ADM-Aeolus (Atmospheric Dynamics Mission, 2018), and ICESat-2 (Ice, Cloud and land Elevation Satellite-2, 2018) satellites were respectively lunched and began to receive information about the vertical structure of the atmosphere and land cover. In addition, two ACE (The Aerosol-Cloud-Ecosystems, 2022) and EarthCARE (Earth Clouds, Aerosols and Radiation Explorer, 2021) space-borne satellites were planned for future. The data of the satellites are increasingly utilized to improve the numerical weather predictions (NWP) and climate modeling.
SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING A HYBRID FRAMEWORK
Authors: Akbari D. , Moradizadeh M.
Published: October 2019

Abstract. Hyperspectral Images are worthwhile data for many processing algorithms (e.g. Dimensionality Reduction, Target Detection, Change Detection, Classification and Unmixing). Classification is a key issue in processing hyperspectral images. Spectral-identification-based algorithms are sensitive to spectral variability and noise in acquisition. There are many algorithms for classification. This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Quebec City hyperspectral dataset, demonstrate that the proposed approach achieves approximately 9% and 5% better overall accuracy than the MLP and the original MHS algorithms respectively.
THE COMBINED USE OF LONG-TERM MULTI-SENSOR INSAR ANALYSIS AND FINITE ELEMENT SIMULATION TO PREDICT LAND SUBSIDENCE
Authors: Gharehdaghi M. , Fakher A. , Cheshomi A.
Published: October 2019

Abstract. Land subsidence in Tehran Plain, Iran, for the period of 2003–2017 was measured using an InSAR time series investigation of surface displacements. In the presented study, land subsidence in the southwest of Tehran is characterized using InSAR data and numerical modelling, and the trend is predicted through future years. Over extraction of groundwater is the most common reason for the land subsidence which may cause devastating consequences for structures and infrastructures such as demolition of agricultural lands, damage from a differential settlement, flooding, or ground fractures. The environmental and economic impacts of land subsidence emphasize the importance of modelling and prediction of the trend of it in order to conduct crisis management plans to prevent its deleterious effects. In this study, land subsidence caused by the withdrawal of groundwater is modelled using finite element method software Plaxis 2D. Then, the model was verified using InSAR data. The results were in good agreement with the measurement results. The calibrated model was used to predict the land subsidence in future years. It could predict future subsidence for any assumed rate of water depletion.
AUTOMATIC BUILDING EXTRACTION USING A DECISION TREE OBJECT-BASED CLASSIFICATION ON JOINT USE OF AERIAL AND LIDAR DATA
Authors: Ghasemi Nejad R. , Pahlavani P. , Bigdeli B.
Published: October 2019

Abstract. Updating digital maps is a challenging task that has been considered for many years and the requirement of up-to-date urban maps is universal. One of the main procedures used in updating digital maps and spatial databases is building extraction which is an active research topic in remote sensing and object-based image analysis (OBIA). Since in building extraction field a full automatic system is not yet operational and cannot be implemented in a single step, experts are used to define classification rules based on a complex and subjective “trial-and-error” process. In this paper, a decision tree classification method called, C4.5, was adopted to construct an automatic model for building extraction based on the remote sensing data. In this method, a set of rules was derived automatically then a rule-based classification is applied to the remote sensing data include aerial and lidar images. The results of experiments showed that the obtained rules have exceptional predictive performance.
VEGETATION MAPPING OF SENTINEL-1 AND 2 SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND RANDOM FOREST WITH THE AID OF DUAL-POLARIZED AND OPTICAL VEGETATION INDEXES
Authors: Ghasemian Sorboni N. , Pahlavani P. , Bigdeli B.
Published: October 2019

Abstract. Vegetation mapping is one of the most critical challenges of remote sensing society in forestry applications. Sentinel-1 dataset has the potential of vegetation mapping, but because of its limited number of polarizations, full polarized vegetation indexes are not accessible. The Sentinel-2 dataset is more suitable for vegetation mapping because a wide variety of vegetation indexes can be extracted from them. Handling this large number of vegetation indexes needs a robust feature extractor. Convolutional Neural Networks (CNN) extract relevant features through their deep feature layers structure and throw out disturbances from small to large scales. Hence, they can be far useful for classifying remote sensing data when the number of input bands is considerable. After pre-processing Sentinel-1 and 2 datasets and extracting the dual-polarized and optical vegetation indexes, we fed the sentinel-1 vegetation indexes alongside the VV and VH sigma Nought bands to a Random Forest (RF) and 1D CNN classifier. Also, 13 spectral features of the Sentinel-2 and the extracted indexes like Blue Ratio (BR), Vegetation index based on Red Edge (VIRE) and Normalized Near Infrared (NNIR) were imported to a RF and 1D CNN. The classification result of Sentinel-1 data showed that Dual Polarized Soil Vegetation Index (DPSVI) is a good indicator for discriminating vegetation pixels. Also, the experiment on the Sentinel-2 dataset using 1D CNN resulted in True Positive Rate (TPR) and False Positive Rate of 0.839 and 0.034, respectively.
IMPROVED INDOOR POSITIONING TECHNIQUE BASED ON A GEOGRAPHIC WEIGHTED REGRESSION
Authors: Gholami A. , Pahlavani P. , Azimi S. , Shakibi S.
Published: October 2019

Abstract. As technology and science develops and the coming of new equipment’s, standards and different waves spread. Each of these standards and technologies have involved in indoor positioning by various scholars. Various methods have been developed based on different systems, all of which are based on specific methods and concepts. The research tries to do indoor positioning using local Wi-Fi fingerprints and signals. To reduce the error to collect local fingerprints, RSS values are recorded in 4 directions and two times. Geographic weighted regression method has been used to train the network. In this research, a genetic algorithm is used to select the appropriate parameters. Ultimately, the accuracy of the model has reached 1.76 cm. The results show that the increase in the number of access points does not affect the accuracy of position determination, but the choice of the effective access point will be effective in reducing the error.
FLOOD RISK ZONATION USING A MULTI-CRITERIA SPATIAL GROUP FUZZY-AHP DECISION MAKING AND FUZZY OVERLAY ANALYSIS
Authors: Hasanloo M. , Pahlavani P. , Bigdeli B.
Published: October 2019

Abstract. In this study, a GIS based approach has been proposed for the flood risk zonation based on a multi-criteria spatial group fuzzy AHP decision making analysis and its integration with fuzzy overlay analysis. For this purpose, 10 layers affecting flood occurrence have been used including: the Digital Elevation Model (DEM), Slope, NDVI, Flow Accumulation (Flow Ac.), HOFD, VOFD, Topographic Position Index (TPI), Topographic Wetness Index (TWI), Curve Number (CN), Modified Fournier Index. Each layer was classified into 5 sub-classes and their preference at its layer was weighted by a group of experts using fuzzy analytical hierarchy processes (GFAHP) method. Finally, the risk map of the studied area with the weight of experts and fuzzy overlay method was product and divided into five categories.
COMPARISON OF POINT AND SEGMENT BASED POINT CLOUD CLASSIFICATION METHOD IN URBAN SCENES
Authors: Hasanpour E. , Saadatseresht M. , Parmehr E. G.
Published: October 2019

Abstract. Point cloud classification is an essential requirement for effectively utilizing point cloud data acquired by different kind of sources such as Terrestrial Laser Scanning (TLS), Aerial LiDAR (Light Detection and Ranging), and Photogrammetry. Classification of point cloud is a process that points are separated into different point groups that each group has similar features. Point cloud classification can be done in three levels (point-based, segment-based, and object-based) and the choice of different level has significant impact on classification result. In this research, random forest classification method is utilized in which the point-wise and segment-wise spectral and geometric features are selected as the input of the classification. In our experiments, the results of point- and segment-based classification were compared. In addition, point-wise classification result for two different features (geometric with/without spectral features) has been compared and the results are presented. The experiments illustrated that segment based classification with both color and geometric features has the best overall accuracy of 83% especially near the object boundaries.
BAND TO BAND REGISTRATION OF MULTI-SPECTRAL AERIAL IMAGERY – RELIEF DISPLACEMENT AND MISS-REGISTRATION ERROR
Authors: Hassanpour M. , Dadras Javan F. , Azizi A.
Published: October 2019

Abstract. With the development of UAVs in agricultural applications, multi-spectral cameras are designed to be installed on these platforms in small size and weight to help users for applications. These cameras are designed in multi lenses structure each lens considered for each spectral band. Band miss-registration of low altitude multispectral images acquired via UAVs is challenging due to multi view image sensors and optics structure of the multispectral cameras. In this study, an image based registration method is proposed to reduce the effect of relief displacement on miss-registration error. For this purpose, three different data sets with different level of relief displacement are considered and three different strategies are proposed. In the proposed method, by dividing the image into patches, selecting the appropriate local window, and local matching in each window, an attempt has been made to obtain the proper distribution of points in the image. Also, by limiting the search space, the matching speed is also increased. To evaluate the proposed method, the comparison with the ENVI and ArcGIS software has been made where results clearly present the superiority of the proposed method by average of 0.28 pixel in accuracy.
INTELLIGENT MANAGEMENT OCCURRENCE AND SPREAD OF FRONT FIRE IN GIS BY USING CELLULAR AUTOMATA. CASE STUDY: GOLESTAN FOREST
Authors: Hesam S. , Valizadeh Kamran K.
Published: October 2019

Abstract. Jungles are a country’s treasures, and the survival of humans and the earth depends on the oxygen that they produce (trees). Three million hectares of jungle are destroyed each year as a result of natural or human-caused wildfires. The massive wildfires occurring in the world confirms that this destructive phenomenon leaves irreconcilable effects on regional ecosystems, vegetation and wildlife; presenting more effective approaches for preventing and managing this complex phenomenon has therefore always been the focus of environmental managers and planners. In other words, it is necessary to model the spread of wildfires, which can be used for utilizing forces and equipment most effectively. Cellular automaton is considered a very simple yet highly effective method that is very capable of modeling dynamic events, like urban development, spread of wildfires, disease outbreaks and others. This study used layers such as height, slope, aspect, vegetation density, roads, rivers and climate as its basis. This was a case study of the Golestan region’s jungles, which calculated the general accuracy of the wildfire spread model to be 65 percent, and the Cohen’s kappa coefficient to be 59 percent.
BUILDING FLOOR PLAN RECONSTRUCTION FROM SLAM-BASED POINT CLOUD USING RANSAC ALGORITHM
Authors: Hossein Pouraghdam M. , Saadatseresht M. , Rastiveis H. , Abzal A. , Hasanlou M.
Published: October 2019

Abstract. In recent years, the applications of interior and exterior model of buildings have been increased in the field of surveying and mapping. This paper presents a new method for extracting a two-dimensional (2D) floor plan of a building from Simultaneous localization and mapping (SLAM)-based point clouds. In the proposed algorithm, after preprocessing, the voxel space is generated for the point cloud. Then, the optimal section of the voxel cube to generate building floor plan is identified. Finally, the linear structures and walls are extracted using the random sample consensus (RANSAC) algorithm. The proposed algorithm was examined on a collected point clouds of a building, and the walls of this building were automatically extracted. To evaluate the proposed method, the obtained walls by the algorithm were compared with the manually extracted walls. The algorithm successfully extracted almost 90% of the walls in the test area. Moreover, the average error of 3 cm for the extracted walls proved the high accuracy of the proposed method for building floor plan modeling.
ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN
Authors: Hosseini S. A. R. , Gholami H. , Esmaeilpoor Y.
Published: October 2019

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.
BUILT UP CHANGE DETECTION BASED ON STRUCTURAL CLASSIFICATION OF DIGITAL ELEVATION MODELS
Authors: Hosseini S. , Tabib Mahmoudi F. , Aboutalebi A.
Published: October 2019

Abstract. Multi temporal changes in built up areas are mainly caused by natural disasters (such as floods and earthquakes) or urban sprawl. Detecting these changes which consist of construction, destruction and renovation of buildings can play an important role in updating three dimensional city models and making the right decisions for urban management. Generally, change detection methods based on multi temporal remotely sensed data can be divided into 2D and 3D categories. Three dimensional change detection methods are suitable for identifying the changes of three dimensional objects such as buildings and their results are more close to reality. The objective of this study is to provide an effective method for 3D change detection of buildings in urban areas based on Digital Elevation Models (DEM). The proposed method in this paper consists of three main steps; 1) generating the normalized DSM for two epochs, 2) performing segmentation and structural classification of image segments in order to generate multi temporal classification maps, 3) producing the change maps. The ability of the proposed algorithm is evaluated in a rapid developing urban area in Tehran, Iran in a 9-years interval. The obtained results represent that the ground and bare soil decrease for about −1.37% and low-rise buildings also decrease for about −9.7%. Moreover, the class of high-rise buildings increases for about +16.4% which conforms making new constructions in addition to renovation of low-rise buildings.
ATTENTION BASED CONVOLUTIONAL NEURAL NETWORK FOR BUILDING EXTRACTION FROM VERY HIGH RESOLUTION REMOTE SENSING IMAGE
Authors: Hosseinpoor H. R. , Samadzadegan F.
Published: October 2019

Abstract. Buildings are a major element in the formation of cities and are essential for urban mapping. The precise extraction of buildings from remote sensing data has become a significant topic and has received much attention in recent years. The recently developed convolutional neural networks have shown effective and superior performance to perform well on learning high-level and discriminative features in extracting buildings because of the outstanding feature learning and end-to-end pixel labelling abilities. However, it is difficult to use the features of different levels with a certain degree of importance that is appropriate to deep learning networks. To tackle this problem, a network based on U-Nets and the attention mechanism block was proposed. The network contains an encoder part and a decoder part and a spatial attention module. The special architecture presented in this article enhances the propagation of features and effectively utilizes the features at various levels to reduce errors. The other remarkable thing is that attention module blocks only lead to a minimal increase in model complexity. We effectively demonstrate an improvement of building extraction accuracy on challenging Potsdam and Vaihingen benchmark datasets. The results of this paper show that the proposed architecture improves building extraction in very high resolution remote sensing images compared to previous models.
CHARACTERIZING SPATIAL AND TEMPORAL TRENDS OF SOIL AND SURFACE PROPERTIES CHANGES IN AN AREA WITH URBAN, BARE SOIL AND WETLAND COVERS: A 30-YEAR CASE STUDY IN GOMISHAN, IRAN
Authors: Alavipanah S. K. , Konyushkova M. , Hamzeh S. , Kakroodi A. A. , Heidari A. , Firozjaei M. K. , Mijani N.
Published: October 2019

Abstract. Climate is one most important factors that can reconstructs the formation of soils. Accordingly, the objective of this study is characterizing spatial and temporal trends of soil and surface properties changes in Gomishan region during the period of 2017–1987. For this purpose, 432 monthly product of LST (MOD11C3) and vegetation cover (MOD13C2) of MODIS sensor and 3 Landsat images were used. Single-channel algorithm and various spectral indexes were used to modeling of Land surface temperature (LST) and surface properties including brightness, greenness, wetness and salinity. Then, based on the soil line analyse, pixels with the full cover of soil were extracted. Finally, trend of LST and surface properties variations were investigated for these pixels and whole studied area. The average of LST and vegetation cover changes in January, February, March and April are higher than other months. The variance of LST and surface properties for Gomishan wetland was higher than other regions of the studied area. The values of Soil salinity index in 2000 year was higher than 1987 and 2017 years. The LST of pixels with full cover of soil in the north of study area was higher than the south. Also, wetness of these pixels in the northern regions is lower than the southern regions of the study area. The results of study indicate, spatial and temporal variations of the surface properties of the Gomishan area derived from remote-sensing data were high.
A WEB-BASED SMART TELECARE SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK
Authors: Hosseinpour S. A. , Delavar M. R. , Hasani Baferani H.
Published: October 2019

Abstract. Acute coronary syndrome which could lead to a heart attack is a common and dangerous disease in many parts of the world. Telecare can accelerate the diagnosis of the disease to provide user information including his/her location in an emergency to a support unit, to reduce the treatment time. Therefore, it is essential to develop a smart telecare system that monitors the status of the user automatically in real-time. This paper has proposed and implemented a web-based smart telecare system. The architecture of this system is based on an architecture for smart care in a smart city. This system receives information from the user and uses linear regression to detect acute coronary syndrome at the initial stage of the disease. This information is descriptive and selected according to the opinion of the cardiologist. Also, in case of diagnosis of acute coronary syndrome, the user's location is determined and sent to a support unit via email. The accuracy of this system is 77.5%. This accuracy is acceptable compared to the same system that predicts myocardial infarction. As the proposed system, unlike the comparator system, does not use electro cardio graphic signals (ECG), it can significantly reduce the time of diagnosis of acute coronary syndrome and its complications through early diagnosis of the disease and its notification. The innovation of this research is the presentation of an intelligent decision maker system that reports the hearth attack to the decision support system via an email message.
RANGE MIGRATION ALGORITHM IN THE PROCESSING CHAIN OF SIGNALS OF A GROUND-BASED SAR SENSOR
Authors: Hosseiny B. , Amini J. , Esmaeilzade M. , Nekoee M.
Published: October 2019

Abstract. Synthetic aperture radar (SAR) system based on frequency modulated continuous wave (FM-CW) transmission is a viable option for producing high-resolution ground-based imaging radars. Compared with pulsed SAR systems, the combination of FM-CW technology and SAR processing techniques have the advantages of small cubage, lightweight, cost-effectiveness, and high resolution in the SAR image. These characteristics make FM-CW SAR suitable to be deployed as payload on ground Based SARs (GB-SARs) for environmental and civilian applications. In this paper, the Range Migration Algorithm (RMA) is used in the processing chain of a Ground-Based SAR (GB-SAR) sensor. The mentioned sensor has been developed in Microwave Remote Sensing Laboratory (MReSL) at the School of Surveying and Geospatial Engineering, the University of Tehran for the generation of a complex image from the raw signal. The raw signal is acquired with that sensor working at S-band, frequency modulating from 2.26 GHz to 2.59 GHz.
A CONVOLUTIONAL NEURAL NETWORK FOR FLOOD MAPPING USING SENTINEL-1 AND SRTM DEM DATA: CASE STUDY IN POLDOKHTAR-IRAN
Authors: Hosseiny B. , Ghasemian N. , Amini J.
Published: October 2019

Abstract. Flood contributes a key role in devastating natural and man-made areas. Floods usually are occurred when there is a considerable number of clouds in the sky making optic data useless. Synthetic aperture radar (SAR) images can be a valuable data source in earth observation tasks. The most important characteristic of the radar image is its ability to penetrate the cloud and dust. Therefore, monitoring earth in cloudy or rainy weather can be available by this kind of dataset. In the last few years by improving machine learning methods and development of convolutional neural networks in remote sensing applications we are facing with extremely high improvement in classification tasks. In this paper, we use dual-polarized VV and VH backscatter values of Sentinel-1 and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) dataset in a proposed convolutional neural network to generate a land cover map of a flooded area before and after happening. Obtained classification results vary between 93.3% to 98.5% for different training sizes. By comparing the generated classified maps, flooded areas of each class can be extracted.
APPLICATION OF RADAR AND OPTICAL SATELLITE IMAGERY DATA IN LANDSLIDE POTENTIAL MAPPING OF SHESHPEER SUB-CATCHMENT IN IRAN
Authors: Imanian A. , Tangestani M. H. , Asadi A.
Published: October 2019

Abstract. One of the serious dangers which threatens human communities especially those who are living in mountainous areas is the occurrence of landslides. Therefore, determination of the areas with potential for landslide events is very important for avoiding establishment of residential areas or industrial facilities. The aim of this study is to provide a landslide potential map in Sheshpeer sub-catchment, Iran, using an integration of remote sensing (RS) techniques and geographical information systems (GIS). Compared with the traditional approaches, these techniques are very fast, inexpensive and trustworthy in landslide mapping. For this purpose, we collected and produced seven data layers using GIS and RS, and then Analytic Hierarchy Process (AHP) method was applied for data analysis. Our results showed that among the twelve pervious landslide events in this area, nine of them are located in the regions with very high potentiality and the others are in highly potential regions for landslides occurrence.
INVESTIGATION OF SPECTRAL CHARACTERISTICS OF CARBONATE ROCKS – A CASE STUDY ON POSHT MOLEH MOUNT IN IRAN
Authors: Imanian A. , Tangestani M. H. , Asadi A.
Published: October 2019

Abstract. Recent developments in the image processing approaches and the availability of multi and/or hyper spectral remote sensing data with high spectral, spatial and temporal resolutions have made remote sensing technique of great interest in investigations of geological sciences. One of the biggest advantage of the application of remote sensing in geology is recognizing the type of unknown rocks and minerals. In this study, an investigation on spectral features of carbonate rocks (i.e. calcite, dolomite, and dolomitized calcite) were done in terms of main absorptions, the reasons of those absorptions and comparison of these absorption with Johns Hopkins University (JHU) spectral library and laboratory spectra of Analytical Spectral Devices (ASD) instrument. For this purpose, we used the VNIR and SWIR bands of ASTER and OLI datasets. Finally, we applied the Spectral Analyst Algorithm in order to comparison between the obtained spectra from ASTER dataset and carbonate spectra of JHU spectral library.
INVESTIGATING TEMPORAL AND SPATIAL EFFECTS OF URBAN PLANNING VARIABLES ON CRIME RATE: A GWR AND OLS BASED APPROACH
Authors: Irandegani Z. , Mohammadi R. , Taleai M.
Published: October 2019

Abstract. Spatial, temporal, environmental and urban planning variables are some factors effected crime occurrence as a social undesirable phenomenon. In this paper, the effect of urban planning variables (including land use diversity and police station area) and temporal parameters (including daily and weekly time windows) on crime incidence were investigated by doing some spatial-temporal analysis. To tackle this, at first, in order to determine crime clusters, DBSCAN algorithm is utilized. Then by using the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) the global effect of urban planning variables on crime spatial clusters are modelled and compared. Finally, the OLS method is used to investigate the effects of temporal variables on crime spatial clusters. The results demonstrate that there is a negative correlation between crime clusters and the police station coverage, and a positive relationship between crime clusters and land-use diversity. Furthermore, increasing the number of police stations in districts with high land-use diversity can result in reducing the crime rate. Also, the result of our temporal analysis shows that the high crime rate was happened at noon and on weekdays. Therefore, increasing the number of police officers at noon and on weekdays in the districts with high land-use diversity is recommended.
ANNUAL MONITORING OF DUST STORM IN IRAN AND ADJACENT AREAS USING MODIS IMAGES (1396 AND 1397 HIJRI SHAMSI)
Authors: Jafary P. , Zandkarimi A. , Jannati M.
Published: October 2019

Abstract. The occurrence of dust storms is one of the major problems in Iran and neighboring countries. Monitoring dust storms and identifying their patterns, areas with high frequency of occurrence and high dense storms using remote sensing technology can help decision makers in different dimensions. This paper tries to investigate the occurrence of dust storms in Iran and adjacent regions over two annual periods, and produce the maps of cumulative density and frequency of occurrence in order to find dust patterns and hotspots and make a comparison between these periods. The daily MODIS images received at the Mahdasht Space Center in 1396 and 1397 were used. Brightness temperature images were prepared using these data and the bands 20, 29, 31 and 32 of them were used. After applying appropriate algorithms, daily dust storm maps were produced. Using the data of 212 synoptic stations throughout the country, the accuracy of the algorithms were assessed and the thresholds were modified. By combining these daily maps, annual cumulative density maps were prepared. Also, by combining daily occurrence maps of dust storms, frequency of occurrence maps were produced. The results show that in 1397, more areas have experienced dust storms in comparison to 1396. Besides, the number and density of dust storms at west and southwest areas have been reduced in 1397. Conversely, in the eastern and southeast areas we have faced with an increase in the number and density of monitored dust storms in 1397. It can be concluded that the problem of dust storms in Iran have been shifted from the west and southwest of the country to the eastern and southeastern regions.
BUILDING OUTLINE EXTRACTION FROM AERIAL IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
Authors: Alidoost F. , Arefi H. , Tombari F.
Published: October 2019

Abstract. Automatic detection and extraction of buildings from aerial images are considerable challenges in many applications, including disaster management, navigation, urbanization monitoring, emergency responses, 3D city mapping and reconstruction. However, the most important problem is to precisely localize buildings from single aerial images where there is no additional information such as LiDAR point cloud data or high resolution Digital Surface Models (DSMs). In this paper, a Deep Learning (DL)-based approach is proposed to localize buildings, estimate the relative height information, and extract the buildings’ boundaries using a single aerial image. In order to detect buildings and extract the bounding boxes, a Fully Connected Convolutional Neural Network (FC-CNN) is trained to classify building and non-building objects. We also introduced a novel Multi-Scale Convolutional-Deconvolutional Network (MS-CDN) including skip connection layers to predict normalized DSMs (nDSMs) from a single image. The extracted bounding boxes as well as predicted nDSMs are then employed by an Active Contour Model (ACM) to provide precise boundaries of buildings. The experiments show that, even having noises in the predicted nDSMs, the proposed method performs well on single aerial images with different building shapes. The quality rate for building detection is about 86% and the RMSE for nDSM prediction is about 4 m. Also, the accuracy of boundary extraction is about 68%. Since the proposed framework is based on a single image, it could be employed for real time applications.
ASSESSING AND COMPARING THE PERFORMANCE OF ENDMEMBER EXTRACTION METHODS IN MULTIPLE CHANGE DETECTION USING HYPERSPECTRAL DATA
Authors: Jafarzadeh H. , Hasanlou M.
Published: October 2019

Abstract. Endmember extraction is a process to identify the hidden pure source signals from the mixture. Endmember finding has become increasingly important in hyperspectral data exploitation because endmembers can be used to specify unknown particular spectral classes. This paper evaluates the change detection problem in bi-temporal hyperspectral remote sensing images using the unmixing process. A complete spectral unmixing process contains estimating the number of endmembers, endmember extraction and abundance estimation. Endmember extraction is a vital step in spectral unmixing of hyperspectral images. Hyperspectral change detection by unmixing has the potential to provide subpixel information from hyperspectral images. In this study, four methods including Simplex Identification via variable Splitting and Augmented Lagrangian (SISAL), N-finder algorithm (N-FINDR), Vertex Component Analysis (VCA), and Fast algorithm for linearly Unmixing (FUN) are used to produce multiple change detection maps. This paper explores and compares the performance of these methods in multiple change detection. The empirical results reveal the superiority of the FUN method in providing multiple change map with an overall accuracy of 87% and a kappa coefficient of 0.70.
INVESTIGATING THE RHYTHMS OF HUMAN MOVEMENTS IN GENEVA LAKE REGION USING MDC DATA
Authors: Javanmard R. , Esmaeili R. , Malekzadeh M. , Karimipour F.
Published: October 2019

Abstract. Movement data are becoming extensive and comprehensive with the advent of GPS (global positioning system) and pervasive use of smartphones, which has led to an increasing rate of studies about movement such as mobility pattern of oil spills, taxies, storms and animals. Studying the movement of people has long been the topic of much thought and debate among researchers within the field of transportation, social issues, and policy. One of the basic prerequisites for studying human movement behavior is modeling the movement, which show how people move so that the effect of different variables can be revealed. For this purpose, this research intends to deploy the concept of activity space (i.e., the part of the space in which a person is active) and its determinants to display the trajectory of individuals, and then modeling the effect of different variables on human mobility behavior. This study explores the effect of time (movement on weekends and weekdays) and demographic (age, gender, occupation state) factors on the characteristics of human mobility pattern and analyzes the extent to which the mobility pattern of different group of people is related to time by using Swiss human movement sample dataset, called MDC. These movement characteristics can be used later in a wide range of applications, such as predictions, urban planning, and traffic forecasting.
INTERFEROMETRIC PATH MODELS FOR GNSS GROUND-BASED PHASE ALTIMETRY
Authors: Khajeh S. , Ardalan A. A. , Schuh H.
Published: October 2019

Abstract. Altimetry by using GNSS (Global Navigation Satellite Systems) reflectometry is regarded as a new promising technique. One important step to utilize this technique is modeling the interferometric path (the difference between the direct and the reflected signal paths). This paper evaluates three models: the planar model, the planar model with tropospheric correction and a model based on ray-tracing. If decimeter level accuracy for water surface fluctuation is required, the planar model cannot be used when a receiver is at an altitude of a few hundred meters and observations are taken at low elevation angles. On the other hand, depending on the mapping function and zenith total delay in the tropospheric correction, the planar model with tropospheric correction can provide decimeter level accuracy for low altitude stations. If simulated observations with a 1 cm accuracy have been employed to estimate Sea Surface Height (SSH) by the model based on ray-tracing the numerical results present 1 cm as RMSE for phase retrieval and 5 cm for Doppler retrieval. The planar model with tropospheric correction does not yield RMSE better than some decimeters for the same condition.
FLOOD MONITORING USING NDWI AND MNDWI SPECTRAL INDICES: A CASE STUDY OF AGHQALA FLOOD-2019, GOLESTAN PROVINCE, IRAN
Authors: Khalifeh Soltanian F. , Abbasi M. , Riyahi Bakhtyari H. R.
Published: October 2019

Abstract. Assessment of changes of water bodies and vegetation by traditional methods is very difficult and costly. The use of satellite data makes it possible to study water bodies and vegetation more accurately and cost effectively. Accordingly, various digital methods have been developed to discover and detect changes of earth's surface features. Flood is one of the important factors contributing to the destruction of natural resources. The purpose of this research is to evaluate the flood areas in the Aghqala area in Golestan province of Iran. The level of water bodies in the spring of 2018 and 2019 was compared and evaluated based on the NDWI and MNDWI indices using Landsat images. The results showed that water bodies’ area in the spring of 2018 was 24.13 km2 which increased to 185.34 km2 at 2019 using NDWI; while the MNDWI due to the excessive sensitivity to the water considered agriculture wetlands as an area of water bodies. Therefore, the NDWI yielded more logical results. Also, change detection methods based on spectral and radiometric information using indices are more accurate than the classification maps and more changes can be shown. Using satellite imagery to monitor changes is essential to facilitate the planning of natural hazards management.
ROBUST BUILDING FOOTPRINT EXTRACTION FROM BIG MULTI-SENSOR DATA USING DEEP COMPETITION NETWORK
Authors: Khoshboresh Masouleh M. , Saradjian M. R.
Published: October 2019

Abstract. Building footprint extraction (BFE) from multi-sensor data such as optical images and light detection and ranging (LiDAR) point clouds is widely used in various fields of remote sensing applications. However, it is still challenging research topic due to relatively inefficient building extraction techniques from variety of complex scenes in multi-sensor data. In this study, we develop and evaluate a deep competition network (DCN) that fuses very high spatial resolution optical remote sensing images with LiDAR data for robust BFE. DCN is a deep superpixelwise convolutional encoder-decoder architecture using the encoder vector quantization with classified structure. DCN consists of five encoding-decoding blocks with convolutional weights for robust binary representation (superpixel) learning. DCN is trained and tested in a big multi-sensor dataset obtained from the state of Indiana in the United States with multiple building scenes. Comparison results of the accuracy assessment showed that DCN has competitive BFE performance in comparison with other deep semantic binary segmentation architectures. Therefore, we conclude that the proposed model is a suitable solution to the robust BFE from big multi-sensor data.
INVESTIGATION OF POTENTIAL DUST SOURCES USING SENTINEL-1 AND NEURAL NETWORK: A CASE STUDY FROM BANDAR-E EMAM-OMIDIYE
Authors: Khoshnoud S. , MirMazloumi S. M. , Amani M. , Asgari H. M.
Published: October 2019

Abstract. Aeolian erosion is a serious environmental threat that damages soils. Dust storms are one example of the consequences of aeolian erosion in dry and semi-arid areas across the world. In this regard, soil surface roughness is an important parameter for monitoring climate changes on the Earth and modelling aeolian erosion. Synthetic Aperture Radar (SAR) systems are valuable resources for estimating soil surface roughness. In arid soils, SAR backscatter is sensitive to the soil surface roughness at higher frequencies and higher incident angles. Based on these facts and lack of studies in the field of dust and erosion using remote sensing methods, an Artificial Neural Network (ANN) along with Sentinel-1 images in two polarizations (VV and VH) were initially applied to estimate surface roughness for the first time in Bandar-e Emam-Omidiye, Khuzestan, Iran. Subsequently, the results were used to investigate potential dust sources. The parameters used to train the ANN included the radar backscatter coefficient, incident angle, and in-situ roughness. The training accuracy of the proposed ANN was relatively high with an RMSE of 0.8821 and RMSE = 0.8804 for VH and VV polarizations respectively. These data were subsequently used to identify areas prone to dust. The results obtained from the investigation of 25 stations located in areas with five different land covers indicated accurately that locals on clay flats (RMSE = 1.08) are the most prone to aeolian erosion in the form of dust.
ASSESSMENT, MONITORING AND EARLY WARNING SYSTEM FOR DESERTIFICATION BASED ON WATER CRITERION (CASE TUDY: KASHAN, IRAN)
Authors: Khosravi H. , Zehtabian G. R. , Abolhasani A. , Eskandari Damaneh H.
Published: October 2019

Abstract. Desertification is the third most important global challenge after crisis of water shortage and drought in the 21st century. Many countries, especially developing countries, are faced this phenomenon. Identifying the regions exposed to this phenomenon is so important for combating desertification. Regarding to the importance of this phenomenon, in this study, we assessed desertification in Kashan, Iran, using IMDPA model and GIS software. According to the region condition, water was selected as the criterion and some indices were identified for it. For monitoring, the effect of criterion and indices changes was investigated during 1966–1974, 1974–1992, 1992–2002 and 2002–2011. Finally, for providing the desertification early warning system, data related to the criterion and indices were collected during 2003–2011 and the threshold of each index was determined. The results showed that the class of desertification based on Electrical Conductivity index was very severe in the study area, so this index is one of the most important desertification indices in arid areas in Iran.
VIRTUAL TRAINING SAMPLE GENERATION BY GENERATIVE ADVERSARIAL NETWORKS FOR HYPERSPECTRAL IMAGES CLASSIFICATION
Authors: Alipourfard T. , Arefi H.
Published: October 2019

Abstract. Convolutional Neural Networks (CNNs) as a well-known deep learning technique has shown a remarkable performance in visual recognition applications. However, using such networks in the area of hyperspectral image classification is a challenging and time-consuming process due to the high dimensionality and the insufficient training samples. In addition, Generative Adversarial Networks (GANs) has attracted a lot of attentions in order to generate virtual training samples. In this paper, we present a new classification framework based on integration of multi-channel CNNs and new architecture for generator and discriminator of GANs to overcome Small Sample Size (SSS) problem in hyperspectral image classification. Further, in order to reduce the computational cost, the methods related to the reduction of subspace dimension were proposed to obtain the dominant feature around the training sample to generate meaningful training samples from the original one. The proposed framework overcomes SSS and overfitting problem in classifying hyperspectral images. Based on the experimental results on real and well-known hyperspectral benchmark images, our proposed strategy improves the performance compared to standard CNNs and conventional data augmentation strategy. The overall classification accuracy in Pavia University and Indian Pines datasets was 99.8% and 94.9%, respectively.
ASSESSMENT AND MAPPING OF IRAN DESERTIFICATION INTENSITY USING ARCGIS ENVIRONMENT
Authors: Khosravi H. , Zehtabian G. R. , Eskandari Damaneh H. , Abolhasani A.
Published: October 2019

Abstract. Desertification phenomenon is described as one of the most obvious forms of natural resources degradation in the world. This phenomenon, which occurs because of natural factors or anthropogenic factors, is accounted as the third most important global challenge after crisis of water shortage and drought in the 21st century. Awareness of desertification criteria and indicators, investigation of a regional model and determining the most important factors affecting desertification are essential for combating desertification. So in this study, IMDPA model (Iranian Model of Desertification Potential Assessment) was used in order to prepare Atlas of Iran desertification. 8 criteria and 130 indicators affecting desertification have determined for this model. Regarding to these criteria and indicators and quantifying them in arid, semi-arid and dry sub-humid region of Iran, the map of desertification intensity was prepared. The results of this study showed that 88.73% of the country surface was affected by desertification that is equal to 143365238.6 hectare. The surface more than 49425703.3 hectare equal to 30.59% of total surface of country was in low desertification class, the surface more than 935677913.6 hectare equal to 57.91% was in class II or medium and the surface about 371621.7 hectare equal to 0.23% was in class III or intense. Class IV of desertification or very intense was omitted regarding to IMDPA model and 8 criteria, and natural desert areas which their surface was equal to 15624274.3 hectare or 9.67% is beyond this class.