Publication Date: 2023
Applied Soft Computing (1568-4946)148
Although the high number of bands in hyperspectral remote sensing images increases their usefulness, it also causes some processing difficulty. In supervised classification, one problem is decreasing classification accuracy due to the insufficient training samples against the bands. A way to deal with this problem is the selection of appropriate bands by the metaheuristic methods. Because of the stochastic search, the selected bands differ in any implementation of a metaheuristic method. So, the results obtained from the classification of these different band subsets will also have some differences. In this study, a fusion-based approach has been proposed to improve the classification of hyperspectral remote sensing images by multiple implementations of a metaheuristic method for band selection. We have tested the proposed method using ten metaheuristic methods with different objective functions on four well-known datasets. The results show the proposed fusion-based approach successfully improves the classification accuracy in all experiments. The accuracy improvement varies depending on the metaheuristic method, the objective function, and the dataset and ranges from 0.4% to 15.7%. The proposed method improves the classification of complex datasets more and affects weaker objective functions considerably. The results also show the proposed method brings the accuracy of different metaheuristic methods close to each other and reduces the sensitivity of selecting the proper ones. Thus, an automated classification system can be obtained using a parameter-less method. © 2023 Elsevier B.V.
Publication Date: 2024
Journal of Surveying Engineering (07339453)150(1)
Multipath is a limiting factor for accurate positioning by global navigation satellite system (GNSS). Different hardware and computational techniques have been proposed for its mitigation. Here a geometrical approach for multipath localization and mitigation is presented: localization is performed by ray-tracing and mitigation by analyzing the residuals of ambiguity resolved precise point positioning. The main advantages of the method are its ability to correct nearly the multipath-affected parts of raw data and its relative independence of observation duration (e.g., more than 1 h). The method is based on a ray-tracing algorithm and is applicable to all GNSS constellations. It is independent of physical properties of reflecting surfaces, receiver/antenna type and observation sampling rate. The methodology was implemented on static real global positioning system (GPS) data acquired during six consecutive days in presence and in absence of a metallic reflecting plate. The analysis was performed on two kinds of data series: observations residuals and epoch-wise coordinates. The overall RMSs of observations residuals were reduced by 55% on average. The RMS of easting, northing, and elevation residual time series resulting from affected observations were 8.1 mm, 13.3 mm, and 23.8 mm, respectively; while they were reduced to 6.9 mm, 9.7 mm, and 22.4 mm after correction (22% improvement in horizontal and a minor improvement in vertical components). © 2023 American Society of Civil Engineers.
Publication Date: 2022
Advances in Space Research (02731177)69(9)pp. 3333-3349
This paper investigates the possibility of high resolution mapping of PM2.5 concentration over Tehran city using high resolution satellite AOD (MAIAC) retrievals. For this purpose, a framework including three main stages, data preprocessing; regression modeling; and model deployment was proposed. The output of the framework was a machine learning model trained to predict PM2.5 from MAIAC AOD retrievals and meteorological data. The results of model testing revealed the efficiency and capability of the developed framework for high resolution mapping of PM2.5, which was not realized in former investigations performed over the city. Thus, this study, for the first time, realized daily, 1 km resolution mapping of PM2.5 in Tehran with R2 around 0.74 and RMSE better than 9.0 [Formula presented]. © 2022 COSPAR
Dovom-niasar, S.J.,
Seifi, A.,
Bahramian, A.R.,
Abzal, A. Publication Date: 2023
Journal of Vinyl and Additive Technology (15480585)29(5)pp. 849-863
An epoxy-based intumescent coating containing the silica and zinc borate nanoparticles was fabricated. The fire performance of the coating with the optimum formulation was investigated in terms of the changes in the physical and chemical structure of the formed char layer during the exposure to a temperature of 1000°C. The state of the chemical structure was analyzed by performing the Fourier-transform infrared spectroscopy, x-ray diffraction, and x-ray photoelectron spectroscopy analysis from the char layer at the three-time intervals of 10, 30, and 60 min of the heating process. The innovative Condorcet method was also employed to examine the changes in the physical structure of the formed char layer. Some instabilities were detected in the physical structure of the char layer in the middle period of heating. Moreover, a gradual formation of silicon carbide crystalline structure was observed on top of the surface, followed by its oxidation to silica over time. In contrast, in the bulk structure, silicon crystalline structures (Coesite) intensified with time. Boron nitride was also increasingly created on the top surface and in the bulk of the coating over the heating time. These findings proved the effective role of the silica and zinc-borate nanoparticles in the fire performance of epoxy-based intumescent coatings. © 2023 Society of Plastics Engineers.
Publication Date: 2019
International Journal of Disaster Risk Reduction (22124209)
The paper proposes an alternative new approach in contrast with the traditional methods to deal with multi-criteria group decision-making problems. It takes into account the multi-criteria group decision-making process as a multi-stakeholder multi-issue negotiation problem, in which stakeholders attempt to lead a consensus on the relative importance of the criteria by using software agents. To do so, it suggests three main steps: pre-negotiation, automated negotiation, and evaluation phases. The pre-negotiation phase is a human-computer interaction by which software agents attempt to exhibit and model the preferences space of the stakeholders. In the automated negotiation phase, the agents come together to negotiate on the criteria weights to reach an agreement on behalf of the stakeholders. Finally, in the evaluation phase, the evaluator agent applies a sensitivity analysis method to determine output variations due to the inputs and parameters. The proposed method is applied to a disaster management practice as a real-world case study, in which some stakeholders jointly attempt to identify the strategic roads in disaster situations specifically, flood events. Three spatial criteria are used for evaluating the road transportation network: load capacity, access to emergency suppliers, and importance of the roads in geometric structure of the network. The results of the study confirm that the proposed method is an efficient alternative approach to deal with multi-criteria group decision-making problems. © 2019 Elsevier Ltd
Publication Date: 2012
Photogrammetric Record (14779730)27(139)pp. 330-359
In this paper, a multi-resolution hybrid approach is proposed for the reconstruction of building models from point clouds of lidar data. The detection of the main roof planes is obtained through a polyhedral approach, whereas the models of appended parts, in this case the dormers, are reconstructed by adopting a model-driven approach. Clustering of the roof points in a multi-resolution space is based on the fuzzy c-mean in the polyhedral section of this hybrid approach. A weighted plane algorithm is developed in order to determine the planes of each cluster. The verification of planes between multi-resolution spaces adopts a method based on a least squares support vector machine that, in the model-driven section, is applied for detecting types of projecting structures. A method is then developed to determine the dormer models' parameters. Finally, the detection of boundary roof lines is obtained through a customised fuzzy Hough transform. The paper outlines the concept of the algorithms and the processing chain, and illustrates the results obtained by applying the technique to buildings of different complexities. © 2012 The Authors. The Photogrammetric Record © 2012 The Remote Sensing and Photogrammetry Society and Blackwell Publishing Ltd.
Publication Date: 2019
Infrared Physics and Technology (13504495)99pp. 222-230
In this paper, a novel adaptive algorithm for target detection in hyperspectral images (HSIs) is proposed. In a general classification, the proposed method belongs to the category of those methods which are not based on the statistical moments of the observed HSI (e.g. correlation or covariance matrix). The main processing burden of the proposed method is over a known set of spectral signatures. Assuming a linear spectral mixing model, the proposed method takes a set of spectral signatures which one of them relates to the target material and the others relate to the background materials. Based on an adaptive approach, the normalized least mean square (NLMS) adaptive algorithm is engaged to estimate a weight vector which is almost orthogonal to the background materials spectral signature whereas it makes an absolutely non-orthogonal pair with the target material spectral signature. The estimated weight vector is multiplied by the observed HSI to make the final decision. One synthetic and two real hyperspectral images are considered to evaluate the performance of the proposed method. The evaluation results show that the proposed method outperforms its counterparts. © 2019
Publication Date: 2024
Environmental Science and Pollution Research (09441344)31(40)pp. 53140-53155
Accurately predicting the spatial-temporal distribution of PM2.5 is challenging due to missing data and selecting an appropriate modeling method. Effective imputation of missing data must consider the relationships between variables while preserving their inherent variability and uncertainty. In this study, we employed machine learning techniques to impute missing data by analyzing the relationships between meteorological variables and other pollutants. Subsequently, we introduced an innovative spatiotemporal hybrid model, AC_GRU, which integrates a one-dimensional convolutional neural network (CNN), GRU, and an attention-based network to predict PM2.5 concentrations in urban areas. The AC_GRU model utilizes meteorological variables, PM2.5 concentrations from nearby air quality monitoring stations, and concentrations of other pollutants as inputs. This approach allows the model to learn spatiotemporal correlations within the time-series data, enhancing the accuracy of PM2.5 predictions. Additionally, the attention mechanism improves prediction accuracy by automatically weighting the past input variables based on their importance for future PM2.5 predictions. The experimental results demonstrate that our AC_GRU model outperforms state-of-the-art methods, making it a valuable tool for urban air quality management and public health protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Publication Date: 2026
Remote Sensing (20724292)18(2)
Highlights: What are the main findings? The trained ANN algorithm simultaneously retrieves soil moisture and vegetation optical depth from CYGNSS observations, showing strong agreement with reference satellite products (SMAP SM: R = 0.83, RMSE = 0.063 m3/m3; SMOS VOD: R = 0.89, RMSE = 0.088). ANN-derived VOD shows strong correlation with independent vegetation indicators—biomass (R~0.77), canopy height (R~0.95), Leaf Area Index (R = 0.96), and vegetation water content (R~0.90)—confirming reliable sensitivity to vegetation structure. What are the implications of the main findings? The combination of GNSS-R data with environmental variables enables reliable dual retrieval of soil moisture and vegetation optical depth, serving as a cost-effective, higher-resolution alternative/complement to SMAP and SMOS. Joint retrieval of SM and VOD enables improved characterization of land–atmosphere interactions, supporting hydrological, ecological, and climate applications. Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide reliable global SM and VOD products, they are limited by coarse spatial resolution and infrequent revisit times. Global Navigation Satellite System Reflectometry (GNSS-R) observations, particularly from the Cyclone GNSS (CYGNSS) mission, offer an improved spatiotemporal sampling rate. This study presents a deep learning framework based on an artificial neural network (ANN) for the simultaneous retrieval of SM and VOD from CYGNSS observations across the contiguous United States (CONUS). Ancillary input features, including specular point latitude and longitude (for spatial context), CYGNSS reflectivity and incidence angle (for surface signal characterization), total precipitation and soil temperature (for hydrological context), and soil clay content and surface roughness (for soil properties), are used to improve the estimates. Results demonstrate strong agreement between the predicted and reference values (SMAP SM and SMOS VOD), achieving correlation coefficients of R = 0.83 and 0.89 and RMSE values of 0.063 m3/m3 and 0.088 for SM and VOD, respectively. Temporal analyses show that the ANN accurately reproduces both seasonal and daily variations in SMAP SM and SMOS VOD (R ≈ 0.89). Moreover, the predicted SM and VOD maps show strong agreement with the reference SM and VOD maps (R ≈ 0.93). Additionally, ANN-derived VOD demonstrates strong consistency with above-ground biomass (R ≈ 0.77), canopy height (R ≈ 0.95), leaf area index (R = 96), and vegetation water content (R ≈ 0.90). These results demonstrate the generalizability of the approach and its applicability to broader environmental sensing tasks. © 2026 by the authors.
Publication Date: 2021
Computers and Electronics in Agriculture (0168-1699)186
This study evaluates the potential of AMSR2 (Advance Microwave Scanning Radiometer2) data for the estimation of Volumetric Soil Moisture (VSM) for bare and agricultural areas. At the first step, the sensitivity of the Microwave Polarization Difference Index (MPDI) to variations in soil and vegetation characteristics were examined at different frequencies. At lower frequencies, the signal attenuation due to vegetation is minimal and thus, denser vegetation usually depolarizes the soil emission. Interestingly, the results also reveal that at higher frequencies, the sensitivity of V and H polarizations over relatively dense vegetation covers is not the same at all. Therefore, MPDI at both low and high frequencies can be a good indicator of the soil moisture and Vegetation Water Content (VWC), respectively. After evaluation of AMSR2 datasets, a model called Multi-channel/MPDI-based Land Parameters Retrieval Model (MMLPRM) is proposed. The MMLPRM optimizes optical depth of vegetation and soil dielectric constant, with simultaneous retrieval of soil moisture and surface temperature by using the AMSR2 brightness temperature data. This algorithm also includes the surface roughness parameters to increase the soil moisture retrieval efficiency. In this way, calibration and validation have been done, using in situ observations of 50 monitoring stations obtained from the International Soil Moisture Network (ISMN) over the United States. Consequently, the analysis on the MMLPRM retrieval model demonstrates its potential and usefulness for soil moisture retrieval. The outcome of this study will help in estimating the accurate soil moisture to optimize the irrigation management strategies and help in water conservation. © 2021 Elsevier B.V.
Publication Date: 2017
ISPRS International Journal of Geo-Information (22209964)(9)
Urban land-use allocation is a complicated problem due to the variety of land-uses, a large number of parcels, and different stakeholderswith diverse and conflicting interests. Various approaches and techniques have been proposed for the optimization of urban land-use allocation. The outputs of these approaches are almost optimum plans that suggest a unique, appropriate land-use for every land unit. However, because of some restrictions, such stakeholder opposition to a specific land-use or the high cost of land-use change, it is not possible for planners to propose a desirable land-use for each parcel. As a result, planners have to identify other priorities of the land-uses. Thus, ranking land-uses for parcels along with optimal land-use allocation could be advantageous in urban land-use planning. In this paper, a parcel-levelmodel is presented for ranking and allocating urban land-uses. The proposed model benefits from the capabilities of geographic information systems (GIS), fuzzy calculations, and Multi-Criteria Decision-Making (MCDM) methods (fuzzy TOPSIS), intends to improve the capabilities of existing urban land-use planning support systems. In this model, as a first step, using fuzzy calculations and spatial analysis capabilities of GIS, quantitative and qualitative evaluation criteria are estimated based on physical characteristics of the parcels and their neighborhoods. In the second step, through the fuzzy TOPSIS method, urban land-uses are ranked for each of the urban land units. In the third step, using the proposed land-use allocation process and genetic algorithm, the efficiency of the model is evaluated in urban land-use optimal allocation. The proposed model is tested on spatial data of region 7, district 1 of Tehran. The implementation results demonstrate that, in the study area, the land-use of 77.2% of the parcels have first priority. As such, the land-use of 22.8% of the parcels do not have first priority, and are prone to change. © 2017 by the Author.
Publication Date: 2019
International Journal of Remote Sensing (13665901)40(18)pp. 7221-7251
Combining optical and polarimetric synthetic aperture radar (PolSAR) earth observations offers a complementary data set with a significant number of spectral, textural, and polarimetric features for crop mapping and monitoring. Moreover, a temporal combination of both sources of information may lead to obtaining more reliable results compared to the use of single-time observations. In this paper, an operational framework based on the stacked generalization of random forest (RF), which efficiently employed bi-temporal observations of optical and radar data, was proposed for crop mapping. In the first step, various spectral, vegetation index, textural, and polarimetric features were extracted from both data sources and placed into several groups. Each group was classified separately using a single RF classifier. Then, several additional classification tasks were accomplished by another RF classifier. The earth observations used in this paper were collected by RapidEye satellites and the Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system over an agricultural region near Winnipeg, Manitoba, Canada. The results confirmed that the proposed methodology was able to provide a higher overall accuracy and kappa coefficient than traditional stacking method, and also than all the individual RFs using each group. These accuracy metrics were also better than those of the RFs using the stacked features. Moreover, only the proposed methodology could achieve standard accuracy (F-score ≥85%) for all crop types in the study area. The visual comparison also demonstrated that the crop maps produced by the proposed methodology had more homogeneous, uniform appearances. Moreover, the mixed pixels of crop types, which abundantly existed in the traditional stacking and individual RFs̕ maps, were significantly eliminated. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2018
International Journal of Remote Sensing (13665901)39(3)pp. 684-703
Multiresolution segmentation (MRS) is one of the most commonly used image segmentation algorithms in the remote-sensing community. This algorithm has three user-defined parameters: scale, shape, and compactness. The scale parameter (SP) is the most crucial one in determining the average size of the image segments generated. Since setting this parameter typically requires a trialand- error process, automatically estimating it can expedite the segmentation process. However, most of automatic approaches are still iterative and can lead to a time-consuming process. In this article, we propose a new, non-iterative framework for estimating the SP with an emphasis on extracting individual urban buildings. The basis of the proposed method is to investigate the feasibility of associating the size of urban buildings with a corresponding ‘optimal’ (or at least reasonable) SP using an explicit mathematical equation. Using the proposed method, these two variables are related to each other by constructing a mathematical (regression) model. In this framework, the independent variables were chosen to be the typical size of buildings in a given urban area and the spatial resolution of the image under consideration; and the dependent variable was chosen to be the corresponding optimal SP. To assess the potential of the proposed approach, two regression models that yielded explicit equations (i.e. degree-2 polynomial (DP), and regression tree (RT)) were employed. In addition, as a sophisticated and versatile nonlinear model, support vector regression (SVR) was utilized to further measure the performances of DP and RT models compared with it. According to the comparisons, the DP model was selected as a representative of the proposed approach. In the end, to evaluate the proposed methodology, we also compared the results derived from the DP model with those derived from the Estimation of Scale Parameter (ESP) tool. Based on our experiments, not only did the DP model produce acceptable results, but also it outperformed ESP tool in this study for extracting individual urban buildings. © 2017 Informa UK Limited, trading as Taylor and Francis Group. All rights reserved.
Maleki, J.,
Masoumi Z.,
Hakimpour F.,
Coello Coello C.A. Publication Date: 2020
Land Use Policy (0264-8377)
Spatial urban land-use planning is a complex process, through which we aim to allocate suitable land-uses while taking into consideration multiple and conflicting objectives and constraints under certain spatial contexts. Landowners should be modeled as players that are able to interact with each other so as to seek their best land-uses while considering multiple objectives and constraints simultaneously. Game theory provides a tool for land-use planners to model and analyze such interactions. In this paper, spatial urban land-use planning is considered as a game to model local competitions between landowners, whose players (i.e. landowners) of which play to pick the most suitable land-use for themselves. The game is defined based on the objectives of consistency, dependency, suitability, compactness of land-uses, and land-use per capita demand. In this paper, three different scenarios are designed for the players. In the first scenario, the players are greedy and only accept the most compatible land-use. In the second scenario, conversely, the players are fully collaborative and care about other players’ payoff. In the third scenario, the players are first greedy, but when they cannot achieve an agreement with other players, they change their attitude to become gradually collaborative for reaching the Nash equilibrium (NE). Furthermore, the dissatisfaction index (DI), which represents the number of unsatisfied landowners with their current land-use, is defined to compare the different scenarios. The proposed model is tested in a district located in District 7 in Tehran (the capital city of Iran) with 2710 parcels. Results of the first scenario showed that, at the beginning of the game, 50 % of the landowners were not satisfied with their current land-uses, but after 50 iterations, about 100 landowners were dissatisfied with their land-use and this scenario was not able to reach the NE. Results of the second scenario indicated that, in order to reach an optimized layout, 325 parcels needed to be changed. Also, after reaching the NE in this scenario, values of the objective functions did not significantly improve. So, lowering the expectations of the players would not lead to appropriate results. The outcomes of the third scenario provided appropriate results, which could be achieved when the expectation levels of the players could be changed during the game. Furthermore, the NE was obtained among the players and the objective functions improved by 20 % on average in comparison with the other scenarios. Moreover, results of the scenarios were compared with the optimized layout obtained by a genetic algorithm (GA) using different parameter values. Results of the comparison also revealed that the urban layouts produced by game theory improved the objective function values obtained by the GA in about 10 % and improved the GA's running time in more than 85 %, on average in this research. © 2020 Elsevier Ltd
Publication Date: 2008
Photogrammetric Engineering and Remote Sensing (00991112)74(5)pp. 637-646
Estimation of land surface temperature and emissivity has taken on a great deal of importance in recent remote sensing studies. The estimation of temperature and emissivity from thermal radiation observations is involved with an under-determined equation set. In this study, an approach is proposed to overcome the problem based on statistical theory of observations and error propagation. First, the under-determined radiance equations have been completed using two NDVI-based equations for the mean and difference emissivities as constraint equations. The two added constraint equations provide the possibility of weighted least squares solution to estimate temperature and emissivity from the over-determined equation set simultaneously. The weights have been calculated based on the uncertainty of each of the equations. The weighting basis of the proposed approach allows statistical control on the uncertainties. The advantages of the weighted least squares solution which is contributed by this study are weighted observations used in the solution, the uncertainty considerations of the used observations, uncertainty propagation control, statistical standard deviation estimation for the unknowns, statistical quality control criteria, and the opportunity of systematic error detection. The numerical efficiency of the proposed approach is examined using a great number of simulated sample data. Then, the proposed approach is validated using the in situ measurements of land surface temperature. The validations accompanied by some statistical tests represent the acceptable performance and accuracy of the proposed approach (approximately 0.5°K for LST standard deviation and approximately 0.0075 for standard deviation of the bands 31 and 32 emissivities). In addition, the simplicity and robustness of the proposed approach may be regarded as a considerable achievement. © 2008 American Society for Photogrammetry and Remote Sensing.
Publication Date: 2025
Remote Sensing Applications: Society and Environment (23529385)38
Crop mapping, vital for informed decision-making in agricultural and food planning, relies on accurate and current information about the distribution of agronomic lands. Remote Sensing and Earth Observation technologies have emerged as indispensable tools, providing up-to-date data and images in diverse spatial and temporal resolutions, offering a practical and cost-effective alternative to traditional methods. This paper surveys over 400 publications spanning four decades, with a notable increase in studies after 2010, focusing on crop mapping and monitoring using remote sensing imagery. Categorizing these studies based on the type of remote sensing data utilized—optical, radar, or a combination thereof—it also delves into the diverse strategies employed, including attributes used, processing units, and classification algorithms. To date, there has not been a comprehensive review study specifically focused on crop mapping. This paper emphasizes the innovations and advancements in remote sensing technologies and their applications in crop mapping. It highlights the integration of cutting-edge deep learning techniques, the utilization of high-resolution satellite data, and the development of hybrid models that combine multiple data sources for enhanced accuracy. Furthermore, this review identifies emerging trends and future directions in the field, offering insights into the potential of new technologies and methodologies. Through this comprehensive overview of crop mapping studies published in reputable scientific journals between 1980 and 2024, we illuminate the dynamic landscape of this field and underscore the unique contributions of our review to the existing body of literature. © 2025 Elsevier B.V.
Publication Date: 2026
Atmospheric Research (01698095)328
The aerosol extinction coefficient (AEC) is a critical parameter in atmospheric research, providing valuable insights into aerosol concentration, composition, and their effects on solar radiation, air quality, and climate change. While the Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite offers high temporal continuity in vertical profiling, its AEC retrievals rely on multiple assumptions —such as fixed lidar ratios, layer homogeneity, and pre-defined aerosol models—which introduce uncertainties and limit retrieval accuracy. To address these limitations, this study proposes a deep learning-based method utilizing a ResNet architecture to estimate and retrieve AEC profiles more accurately. The model is trained using CALIOP data and ground-based measurements from European Aerosol Research Lidar Network (EARLINET) stations, enhancing predictive performance and generalization. The proposed model's performance was evaluated across multiple EARLINET stations, CALIOP Level 2 (L2) products, and two major aerosol events—a European dust storm and aged volcanic ash over north Europa—demonstrating robustness across diverse atmospheric conditions. Comparisons of total column Aerosol Optical Depth (AOD) and LiDAR ratio (LR) profiles derived from the estimated AEC with CALIOP L2 retrievals and EARLINET measurements highlighted the model's superior accuracy and generalization. Specifically, the model showed excellent agreement with EARLINET AOD (R2 = 0.98, RMSE = 0.01), significantly outperforming CALIOP (R2 = 0.21, RMSE = 0.06). Moreover, the model provides vertically resolved LR profiles from 0 to 15 km, whereas CALIOP L2 offers limited and often fixed LR values due to missing AEC data and restrictive assumptions. Notably, the backscatter, AEC, and LR profiles produced by the model consistently outperformed CALIOP L2 retrievals when validated against EARLINET Raman measurements. Additionally, AOD estimates showed strong agreement with EARLINET data, achieving R2 and RMSE values of 0.98 and 0.01, respectively, compared to CALIOP's 0.21 and 0.06. The analysis of LR values for the significant aerosol events aligned well with the physical characteristics of these phenomena, underscoring the model's ability to capture complex aerosol behavior across vertical layers of the European troposphere and lower stratosphere. © 2024
Publication Date: 2015
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (16821750)40(1W5)pp. 441-445
NRTK1 is an efficient method to achieve precise real time positioning from GNSS measurements. In this paper we attempt to improve NRTK algorithm by introducing a new strategy. In this strategy a precise relocation of master station observations is performed using Sagnac effect. After processing the double differences, the tropospheric and ionospheric errors of each baseline can be estimated separately. The next step is interpolation of these errors for the atmospheric errors mitigation of desired baseline. Linear and kriging interpolation methods are implemented in this study. In the new strategy the RINEX2 data of the master station is re-located and is converted to the desired virtual observations. Then the interpolated corrections are applied to the virtual observations. The results are compared by the classical method of VRS generation.
Publication Date: 2020
Process Safety and Environmental Protection (09575820)
The urban sewer pipeline network is a vital urban infrastructure that is highly at risk of failure and its deterioration can be harmful to the environment and public health and safety. Therefore, for performing an effective rehabilitation program, it is needed to prioritize the sewer pipelines. In this paper, a novel risk assessment approach is proposed for prioritizing sewer pipelines based on a combination of Geospatial Information System (GIS) and Analytic Hierarchy Process (AHP)- Data Envelopment Analysis (DEA). To do so, it calculates the Probability of Failure (PoF), along with the Consequence of Failure (CoF) for the sewer pipelines. Bayesian Network (BN) as the probabilistic method is used to calculate PoF. The main contribution of the study lies in using a combination of GIS, AHP, and DEA for quantitatively assessing the CoF, firstly, the criteria weights are determined by the AHP method through experts’ judgments. Then, GIS functionalities along with DEA, are used to calculate scores for the alternatives. Finally, the outputs of the AHP method are integrated with the outputs of the DEA method in order to calculate CoF. The proposed method is applied to a local sewer pipeline network as a real-world case study to assess its risk of failure. The results indicated that the sewer pipelines are in good condition in the study area and among 1605 sewer pipelines, only 48 of them (about 3 %) are in a critical situation that it is needed to perform rehabilitation program. © 2019 Institution of Chemical Engineers
Khosravi, I.,
Razoumny, Y.,
Hatami afkoueieh, J.,
Alavipanah, S.K. Publication Date: 2021
International Journal of Image and Data Fusion (19479832)12(1)pp. 48-63
This paper proposed an extended rotation-based ensemble method for the classification of a multi-source optical-radar data. The proposed method was actually inspired by the rotation-based support vector machine ensemble (RoSVM) with several fundamental refinements. In the first modification, a least squares support vector machine was used rather than the support vector machine due to its higher speed. The second modification was to apply a Platt calibrated version instead of a classical non-probabilistic version in order to consider more suitable probabilities for the classes. In the third modification, a filter-based feature selection algorithm was used rather than a wrapper algorithm in order to further speed up the proposed method. In the final modification, instead of a classical majority voting, an objective majority voting, which has better performance and less ambiguity, was employed for fusing the single classifiers. Accordingly, the proposed method was entitled rotation calibrated least squares support vector machine (RoCLSSVM). Then, it was compared to other SVM-based versions and also the RoSVM. The results implied higher accuracy, efficiency and diversity of the RoCLSSVM than the RoSVM for the classification of the data set of this paper. Furthermore, the RoCLSSVM had lower sensitivity to the training size than the RoSVM. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2011
Journal of the Earth and Space Physics (2538371X)37(1)pp. 11-24
The least squares harmonic estimation is applied to the hourly time-series of Total Electron Contents (TEC) derived from ionospheric models using seven years of GPS observations processed by Bernese software. The frequencies of dominant spectral components in the spectrum are estimated. We observe significant periodic patterns with periods of 24 h and its fractions 24h/n, n=2,.,11, which are the well-known Fourier series decomposition of the diurnal periodic pattern of the ionospheric variations. The principal component with daily signal is due to the day-night variation of TEC values. The semidiurnal and tri-diurnal components can be explained by the substorm signatures in both auroral electrojet (in layer E) and ring current variations (related to magnetosphere at low latitudes) and tidal effects. Also, the spectrum shows the well-known 27-day period of solar cycle variations. We observe annual, semi-annual and tri-annual signals in the series. The detected signals are then applied to perform an ionospheric prediction. The results indicate that a substantial part (in the absolute sense) of the TEC values can be predicted using this base function, and an undetectable part remains as disturbed noise which can exceed 20 TEC units for the disturbed ionosphere. In comparison with the standard Klobuchar model, the model presented in this contribution will significantly improve the single frequency GPS positioning accuracy.
Publication Date: 2021
Remote Sensing Letters (2150704X)12(5)pp. 499-509
Monitoring the melting of Greenland ice using various sensors is of great importance due to global sea level rise. The mass changes in Greenland can be observed with the GRACE (Gravity Recovery and Climate Experiment) mission from 2002 to 2016. The GRACE limitations and noise are due to the geometrical and instrumental properties along its orbit, which requires investigations for further improvement. The innovation of this research is to introduce a new method in four-dimensional (4D) wavelet decomposition (WD) for increasing the efficiency of the GRACE signal, used for the reconstruction of the Greenland mass changes. The results show that the overall downward trend in the west Greenland coast is 25.25 ± 6.95 cm/year, and the highest decline rate is 33.60 ± 6.23 cm/year from 2013 to 2016. The northern regions of Greenland have less mass loss than the west and south. For verification, the 4D WD output has been compared with the CryoSat-2 results from 2011 to 2016. The GRACE and CryoSat-2 show a significant correlation of 0.62, which indicates an improvement of 0.18 compared to the forward modelling. The 4D WD improves the overall performance of the reconstructed signal in the frequency time-space domain and reduces the noise in each dimension. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2018
Environmental Monitoring And Assessment (01676369)190(4)
Urban expansion can cause extensive changes in land use and land cover (LULC), leading to changes in temperature conditions. Land surface temperature (LST) is one of the key parameters that should be considered in the study of urban temperature conditions. The purpose of this study was, therefore, to investigate the effects of changes in LULC due to the expansion of the city of Isfahan on LST using landscape metrics. To this aim, two Landsat 5 and Landsat 8 images, which had been acquired, respectively, on August 2, 1985, and July 4, 2015, were used. The support vector machine method was then used to classify the images. The results showed that Isfahan city had been encountered with an increase of impervious surfaces; in fact, this class covered 15% of the total area in 1985, while this value had been increased to 30% in 2015. Then LST zoning maps were created, indicating that the bare land and impervious surfaces categories were dominant in high temperature zones, while in the zones where water was present or NDVI was high, LST was low. Then, the landscape metrics in each of the LST zones were analyzed in relation to the LULC changes, showing that LULC changes due to urban expansion changed such landscape properties as the percentage of landscape, patch density, large patch index, and aggregation index. This information could be beneficial for urban planners to monitor and manage changes in the LULC patterns. © 2018, Springer International Publishing AG, part of Springer Nature.
Publication Date: 2020
Geocarto International (17520762)35(12)pp. 1311-1326
Generation of precise digital elevation models (DEMs) from stereo satellite images by using rational polynomial coefficients (RPCs) usually needs several ground control points (GCPs). This is mainly due to RPCs biases. However, since GCPs collection is a time consuming and expensive process, global DEMs (GDEMs), as the most inexpensive geospatial information, can be used to improve stereo satellite imagery-based DEMs (IB-DEMs). In this study, a 2.5 D mutual information based DEM matching, between a GDEM and an IB-DEM, was introduced for bias correction of satellite stereo images. Three well-known 30-meter GDEMs, namely, SRTM, ASTER, and AW3D30, were used and compared to assess the efficiency of this approach. The performance of the proposed method was evaluated by processing the stereo images acquired by CARTOSAT-1 satellite from two regions with flat, hilly, and mountainous topography. Evaluation results revealed that the proposed method could significantly improve the geometric accuracy of IB-DEM using all GDEMs. © 2019 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2004
CTIT workshop proceedings series (16821750)35
In the context of the analysis of remotely sensed data the question arises of how to analyse large volumes of data. In the specific case of agricultural fields in flat areas these fields can often be modelled in terms of geometric primitives such as triangles and rectangles. In this case the options are classical i.e. bottom-up, starting at the pixel level and resulting in a segmented, labelled image or top-down, starting with a model for image partitioning and resulting in a minimum cost estimation of shape hypotheses with corresponding parameters. Standard bottom-up classification methods usually concern the pixel as a main element and try to label the pixel individually. But various errors are involved in the image analysis with these methods. Mixed pixels, simplicity of the basic assumptions in the classification algorithms, sensor effects, atmospheric effects, and radiometric overlap of land cover objects lead to the wrong detection in image analysis. In this paper we propose a Model-Based Image Analysis (MBIA) approach to analyze the remotely sensed data. In this manner using the available knowledge about the remote sensing system we generate some hypothesis maps and then test them using the radiometric measurements (images). In order to test the method we used the boundaries of the agricultural fields stored in a GIS to model the objects in the scene. The results of the method have been compared with the result of a traditional Maximum-Likelihood classification and a standard Object-Based Classification using the boundaries. Using this approach we could reach to the 94% overall accuracy. © 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved.
Publication Date: 2019
International Journal of Remote Sensing (13665901)40(12)pp. 4526-4543
RapidEye satellite images with high spatial resolution, affordable prices and having Red-Edge band have high potential for time series issues, especially in vegetation studies. Despite these beneficial properties, RapidEye images with 5 m spatial resolution are not sufficiently useful for some applications. According to this problem, enhancing the spatial resolution of RapidEye images can significantly improve the results of the subsequent processes on these images. Fusion of high spatial resolution with high spectral resolution images is known as an effective way to enhance the quality of multispectral remotely sensed images. Unfortunately, the lack of panchromatic band with high spatial resolution has been faced the procedure of improving the spatial resolution of RapidEye images with major problems. In this paper, we have proposed using the free Google Earth (GE) images which have high spatial resolution and high-coverage of land surface to enhance the spatial information of RapidEye images. A simulated panchromatic image has been generated by three band GE image and with three different methods: Mean, principal component analysis (PCA) and weighted average of GE image bands. In the last method, the weights are extracted from the spectral response curve of the satellite which captured the GE image. The simulated panchromatic image has been utilized for pansharpening of RapidEye image in five well-known methods: Brovey, Gram-Schmidt (GS), intensity-hue-saturation (IHS), Pansharp1 and Pansharp2. The most important point is finding the GE image with lowest lag time with RapidEye image. By satisfying this condition, the experiments illuminated that the proposed method can effectively enhance the spatial quality of RapidEye image. Also, this study presented that Pansharp2 method, which used simulated panchromatic image generated by the spectral response curve information, has revealed the best results of RapidEye image pansharpening. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
