Mehran Sattari, Ph.D. ,

Assistant Professor

Department Of Geomatics Engineering
Faculty Of Civil Engineering And Transportation

Email

sattari@eng.ui.ac.ir

Address

University of Isfahan Azadi square Isfahan, Iran
Postal Code : 8174673441

Overview

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Articles

2025

Multi-layer retrieval of aerosol optical depth in the troposphere using SEVIRI data: a case study of the European continent

( Article . )

Pashayi, M., Sattari, M., Momeni shahraki, M.

Atmospheric Measurement Techniques (18671381)18(6)pp. 1415-1439

Multi-layer aerosol optical depth (AOD) estimation with sufficient spatial and temporal resolution is crucial for effective aerosol monitoring, given the significant variations over time and space. While ground-based observations provide detailed vertical profiles, satellite data are essential for addressing the spatial and temporal gaps. This study utilizes profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to estimate vertical AOD values at 1.5, 3, 5, and 10 km layers. These estimations are achieved with spatial and temporal resolutions of 3 km_3 km and 15 min, respectively, over the European troposphere. We employed machine learning models - XGBoost (XGB) and random forest (RF) - trained on SEVIRI data from 2017 to 2018 for the estimations. Validation using CALIOP AOD retrievals in 2019 confirmed the reliability of our findings, emphasizing the importance of wind speed (Ws) and wind direction (Wd) in improving AOD estimation accuracy. A comparison between seasonal and annual models revealed slight variations in accuracy, leading to the selection of annual models as the preferred approach for estimating SEVIRI multi-layer AOD values. Among the annual models, the XGB model demonstrated superior performance over the RF model at all four layers, yielding more reliable AOD estimations with R2 values of 0.99, 0.97, 0.98, and 0.98 for the four layers from low- to high-altitude layers. Further validation using data from European Aerosol Research Lidar Network (EARLINET) stations across Europe in 2020 indicated that the XGB model still achieved better agreement with EARLINET AOD profiles, with R2 values of 0.86, 0.80, 0.75, and 0.59 and RMSE values of 0.022, 0.012, 0.015, and 0.005. We performed a qualitative validation of multi-layer AOD estimations by comparing spatial trends with CALIOP AOD retrievals for SEVIRI pixels on four dates in 2019, showing strong agreement across varying AOD levels. Additionally, the model successfully estimated AOD at 15 min intervals for two real events - a Saharan dust plume and the Mount Etna eruption - revealing consistent physical characteristics, including long-range transport in the upper layers and a gradual increase in AOD from lower to higher tropospheric layers during volcanic events. The results demonstrate that the proposed method facilitates comprehensive monitoring of AOD behavior throughout the four vertical layers of the troposphere, offering important insights into the dynamics of aerosol occurrence. © Author(s) 2025.

Trend Change Point Detection in InSAR Derived Displacement Time Series Using MALkCNN: A Deep Learning Approach

( Article . )

Arya fakhri, S., Sattari, M.

PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science (25122819)93(4)pp. 335-350

2024

Improving the accuracy of AOD by using multi-sensors data over the Red Sea and the Persian Gulf

( Article . Bronze )

Pashayi, M., Sattari, M., Momeni shahraki, M., Kavianpour, N.

Atmospheric Pollution Research (13091042)15(1)

Aerosol Optical Depth (AOD) retrieved using Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) and Moderate Resolution Imaging Spectroradiometer (MODIS) plays a crucial role in aerosol sensing. However, the performance of these products varies across different aerosol concentrations. This research assessed the performance of CALIOP and MODIS AOD products over a three-year period (January 2017 to June 2019) under various aerosol concentrations in the Red Sea and the Persian Gulf. The products were compared with ship-based AERONET (Aerosol Robotic Network) measurements conducted in the study area. The findings reveal that MODIS AOD products exhibit greater reliability during clear days, whereas CALIOP AOD products are more accurate in regions characterized by high aerosol concentrations. However, CALIOP's limited product resolution prevents it from providing adequate spatial-temporal coverage under heavy aerosol concentrations. To enhance the accuracy of MODIS AOD, this paper proposes a methodology based on various machine learning (ML) algorithms, including Random Forest (RF), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVR), and Bayesian network. The study explores the performance of MODIS and CALIOP AOD products during moderate and pollution days in the Persian Gulf and Red Sea regions. The ML models are ranked based on their accuracy, with the order being XGBoost > MLP > SVRrbf > RF > Bayesian > SVRlinear for the annual and two semi-annual networks. In other words, the Bayesian method demonstrates higher efficiency (R2 = 0.97, 0.91, and 0.90) in handling seasonal subsets. The proposed model's outcomes are validated using ship-based AERONET AOD data. By comparing the estimated AOD values with the ship-based AERONET AOD values and analyzing the overall correlation results, it is evident that machine learning techniques effectively provide accurate AOD estimates for moderate and pollution days. © 2023 Turkish National Committee for Air Pollution Research and Control

MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach

( Article . )

Pashayi, M., Sattari, M., Momeni shahraki, M., Amini, S.

Atmospheric Pollution Research (13091042)15(7)

2023

A Conditional Generative Adversarial Network for urban area classification using multi-source data

( Article . )

Sirous, A., Sattari, M., Momeni shahraki, M., Pashayi, M.

Earth Science Informatics (18650473)16(3)pp. 2529-2543

Classifying urban land use/cover types poses significant challenges due to the complex and heterogeneous nature of urban landscapes. Recent years have witnessed notable advancements in land use/cover classification, driven by improvements in classification methods and the utilization of data from multiple sources. Deep learning networks, especially, have gained prominence in various image analysis tasks, including land use/cover classification. However, when it comes to urban areas, the classification of urban land use/cover encounters additional obstacles, including the complexity of classes, limited training data, and the presence of numerous urban categories. To overcome the limitations arising from similar classes and insufficient training data, we propose a novel approach that integrates hyperspectral and LiDAR data through a Conditional Generative Adversarial Network (CGAN) for semantic segmentation. Our methodology leverages the UNet + + generator and PatchGAN discriminator to achieve accurate segmentation. The CGAN-generated segmented images are then processed by a fully connected neural network (FCN) to classify 20 land use/cover classes. By validating our approach on the 2018 GRSS Data Fusion dataset, our study demonstrates its exceptional operational performance. The CGAN architecture outperforms previous algorithms in terms of class diversity and training data volume. By generating synthetic data that closely resembles the ground truth, the CGAN enhances the classification performance. Clear visual distinctions are observed among various urban features, such as vegetation, trees, buildings, roads, and cars. Classes associated with healthy grass, stressed grass, bare earth, and stadium seats achieve high accuracy. However, road and railway classes exhibit poorer performance due to their similarity with sidewalk, highway, major thoroughfare, and crosswalk classes. Overall, our study showcases a significant improvement in classification accuracy, achieving an approximate accuracy of 96.98% compared to the winning articles presented in the 2018 competition, which achieved accuracies of 64.95% and 76.54%, respectively. This improvement in accuracy can be attributed to the effective extraction and combination of high and low-level urban land cover/land use features within our proposed architecture. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Data level and decision level fusion of satellite multi-sensor AOD retrievals for improving PM2.5 estimations, a study on Tehran

( Article . )

Mirzaei, A., Bagheri, H., Sattari, M.

Earth Science Informatics (18650473)16(1)pp. 753-771

One of the techniques for estimating the surface particle concentration with a diameter of fewer than 2.5 micrometers (PM2.5) is using aerosol optical depth (AOD) products. Different AOD products are retrieved from various satellite sensors, like MODIS and VIIRS, by various algorithms, such as Deep Blue and Dark Target. Therefore, they don’t have the same accuracy and spatial resolution. Additionally, the weakness of algorithms in AOD retrieval reduces the spatial coverage of products, particularly in cloudy or snowy areas. Consequently, for the first time, the present study investigated the possibility of fusing AOD products from observations of MODIS and VIIRS sensors retrieved by Deep Blue and Dark Target algorithms to estimate PM2.5 more accurately. For this purpose, AOD products were fused by machine learning algorithms using different fusion strategies at two levels: the data level and the decision level. First, the performance of various machine learning algorithms for estimating PM2.5 using AOD data was evaluated. After that, the XGBoost algorithm was selected as the base model for the proposed fusion strategies. Then, AOD products were fused. The fusion results showed that the estimated PM2.5 accuracy at the data level in all three metrics, RMSE, MAE, and R2, was improved (R2= 0.64, MAE= 9.71μgm3, RMSE= 13.51μgm3). Despite the simplicity and lower computational cost of the data level fusion method, the spatial coverage did not improve considerably due to eliminating poor quality data through the fusion process. Afterward, the fusion of products at the decision level was followed in eleven scenarios. In this way, the best result was obtained by fusing Deep Blue products of MODIS and VIIRS sensors (R2= 0.81, MAE= 7.38μgm3, RMSE= 10.08μgm3). Moreover, in this scenario, the spatial coverage was improved from 77% to 84%. In addition, the results indicated the significance of the optimal selection of AOD products for fusion to obtain highly accurate PM2.5 estimations. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Improvement of spatial-temporal resolution of aerosol profile by using multi-source satellite data over the Persian Gulf

( Article . Bronze )

Pashayi, M., Sattari, M., Momeni shahraki, M.

Atmospheric Environment (13522310)292

2019

3-D convolution-recurrent networks for spectral-spatial classification of hyperspectral images

( Article . Gold )

Seydgar, M., Alizadeh naeini, A., Zhang, M., Li, W., Sattari, M.

Remote Sensing (20724292)11(7)

Exploring the potential of full waveform airborne lidar features and its fusion with rgb image in classification of a sparsely forested area

( Article . All Open Access; Gold Open Access )

Sattari, M., Babadi, M., Iran Pour S., Babadi, M., Iran Pour S.

CTIT workshop proceedings series (16821750)42(4/W18)pp. 147-152

SIFT Detector Boosted by Adaptive Contrast Threshold to Improve Matching Robustness of Remote Sensing Panchromatic Images

( Article . )

Safdari, M., Moallem, P., Sattari, M.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (21511535)12(2)pp. 675-684

2018

A regression modelling approach for optimizing segmentation scale parameters to extract buildings of different sizes

( Article . )

Jozdani, S.E., Momeni shahraki, M., Johnson, B.A., Sattari, M.

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.

2017