
Hossein AghighiShahid Beheshti University | SBU · Department of Remote Sensing and GIS
Hossein Aghighi
PhD in Geomatics Engineering
About
38
Publications
8,149
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
227
Citations
Publications
Publications (38)
Water turbidity is one of the most important parameters of water quality, which represents the transparency of water and is effective in eutrophication. This research was done to estimate the amount of water turbidity using remote sensing data and the random forest technique. For this purpose, the water quality monitoring data of Chitgar Lake in Te...
Graph partitioning is essential for uncovering cohesive communities within complex networks. This paper introduces LouvainSplit, an innovative algorithm designed to enhance graph partitioning efficiency and accuracy. LouvainSplit leverages advanced techniques in feature representation, community detection, and evaluation, providing a robust framewo...
Currently, the information on the structural attributes of forests, such as the diameter at breast height (DBH) and the aboveground biomass (AGB), is being used widely in various disciplines. In this study, we first proposed a novel tree detection algorithm called multi‐scale individual tree detection (MSITD) algorithm, which combines the strengths...
Remote sensing is a cost-effective method for monitoring chlorophyll-a (Chl-a) concentration, an indicator of eutrophication, due to its spatiotemporal effectiveness and availability of historical data. However, its application in shallow, small water bodies poses challenges due to the need for high spatial and temporal resolutions, significant opt...
Co-occurring biodiversity and global heating crises are systemic threats to life on Earth as we know it, especially in relatively rare freshwater ecosystems, such as in Iran. Future changes in the spatial distribution and richness of 131 riverine fish species were investigated at 1481 sites in Iran under optimistic and pessimistic climate heating s...
Abstract
1. Iran is one of the world's fish biodiversity hotspots. Most riverine fish species in this country are currently under threat by human activities. In addition to those threats, climate change is expected to alter rainfall and temperature regimes, imposing further limitations, particularly to endemic fishes. Therefore,
understanding how...
This research was carried out to acquire the optimized retrieval algorithm by the random forest method and based on remote sensing data to monitor total nitrogen and phosphorus parameters as key drivers for eutrophication in water bodies. For this purpose, the water quality monitoring data of Chitgar Lake in Tehran were used, which is an artificial...
In recent years, various techniques have been developed to generate crop-type maps based on remote sensing data. Wheat and barley are two major cereal crops cultivated as the first and fourth largest grain crops across the globe. The variations in spectral temporal profile of both crops are generally insignificant at small scales and therefore the...
The future changes in the spatial distribution and richness of 131 riverine fish species were investigated at 1481 sites in Iran under optimistic and pessimistic climate change scenarios of 2050 and 2080. The maximum entropy model was used to predict species’ potential distribution under current and future climate conditions. The hydrologic unit (H...
Lidar point cloud dataset and 3-D models are widely used in urban feature extraction, forest, urban and tourism management, robotics, computer game production etcetera. On the other hand, The existence of outliers in the lidar point cloud is inevitable. Therefore, outlier detection and removing them from lidar point cloud data have been known as n...
Remote Sensing (RS) technology provides regular monitoring of alfalfa farms, as a major source of forage production worldwide. Phenological characteristics derived from time series of RS imagery provide a valuable information source to estimate crop yield accurately. In this study, we computed spectral vegetation indices (SVIs) from time series of...
2D and 3D urban structures play an important role in characterizing Land Surface Temperature (LST). The effects of 2D urban structures on changes of LST have been studied in many studies, but less attention has been paid to the effects of 3D urban structures. Therefore, in this study, we aimed at employing LiDAR point clouds and a Landsat-8 image t...
Introduction
The world is rapidly moving towards urbanization, and with large populations living in cities, and ever increasing population in urban areas, urban sprawl has occurred in many cities around the world. Lack of urban planning and management regarding the development of urban sprawl has been known as the source of many problems in cities...
Over the past decades, urban growth has been known as a worldwide phenomenon that includes widening process and expanding pattern. While the cities are changing rapidly, their quantitative analysis as well as decision making in urban planning can benefit from two-dimensional (2D) and three-dimensional (3D) digital models. The recent developments in...
The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs satura...
Light Detection and Ranging (LiDAR) point cloud dataset and 3 dimensional (3-D) models have been extensively used for urban feature extraction, urban management, forestry management, managing urban green space, tourism management, robotics, and video and computer games' production. One of the main steps toward reaching accurate 3-D models is cluste...
Recently, three-dimensional designs with the aid of LiDAR cloud points in areas, such as extraction of urban tolls, urban management, computer games, etc. have been widely used and has provided a great deal of research. There are many ways to create a three-dimensional model. Using image data (single-image, stereo images, multiple images), LiDAR-ba...
Today, urbanization and the interest of rural to migrate to the big cities in developing countries, is caused increasing number of population and made vast changes in the location of cities and suburbs. Hence, mayors need accurate information and horizontal and vertical growth of cities to manage the process and address correct principles. Therefor...
This article presents a fully spatially adaptive Markov random field (MRF)-based super-resolution mapping (SRM) technique to produce land-cover maps at a finer spatial resolution than the original coarse-resolution image. MRF combines the spectral and spatial energies; hence, an MRF-SRM technique requires a smoothing parameter to manage the contrib...
Traditionally, forest tree crowns are extracted using airborne or spaceborne hyper-/multi-spectral remotely sensed images or pansharpened images. However, these medium/low spatial resolution images suffer from the mixed pixel problem, and the cost to collect very high resolution image collection is high. Moreover, existing feature extraction techni...
A Markov random field is a graphical model that is commonly used to combine spectral information and spatial context into image classification problems. The contributions of the spatial versus spectral energies are typically defined by using a smoothing parameter, which is often set empirically. We propose a new framework to estimate the smoothing...
In the context of remote sensing image classification, Markov random fields (MRFs) have been used to combine both spectral and contextual information. The MRFs use a smoothing parameter to balance the contribution of the spectral versus spatial energies, which is often defined empirically. This paper proposes a framework to estimate the smoothing p...
Markov random field (MRF) is currently the most common method to find
the optimal solution for the classification of image data incorporating
contextual visual information. The labeling for a site in MRF is
dependent on smoothing parameters. Therefore, this paper deals with the
development of a new robust two-step method to determine the smoothing...
The contribution of spectral and contextual information has an important effect on the classification accuracy of Markov random field (MRF). Therefore, this paper deals with the development of a new method to determine the smoothing parameter to balance the spatial and spectral energies. In this research both an MRF based model and an integration o...
Linear and nonlinear optimization problems can be solved using classical methods. However, the solutions become inefficient as the number of variables and limitations increase. Routing pipelines requires optimum solutions in which technical, economical and environmental parameters are taken into consideration. In this study capabilities of GIS and...
Traditionally, the time of prayer was being announced through mosques. Nowadays, there’s a special time in cities of Muslim countries, known as prayer times table. Mean while villages lacksuch division or table, they use the Prayer times of nearby cities. In non-muslim countries, such tables just belong to big cities and other areas have difficulti...
In this research, usefulness of IRS-LISS-III data of Gorgan Bay, South-east of Caspian Sea located in North of Iran for water turbidity mapping, has been tested. After correction of geometric and radiometric errors, the resulting radiance data were used for examination of correlations between the remotely sensed and in situ water turbidity data sim...
In order to extract the wheat the bi-temporal Spot images were ordered based on cultivation calendar of wheat and other crops. As the crops reflection properties showed many variations, for precise classification many signatures are needed. Appropriate bands for classification were selected by divergence algorithm. These bands converted to IHS and...