Hassan Mohamed Hassan

Hassan Mohamed Hassan
Tokyo Institute of Technology | TITech · Transdisciplinary Science and Engineering

Doctor of Philosophy

About

15
Publications
20,635
Reads
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168
Citations
Citations since 2016
11 Research Items
168 Citations
2016201720182019202020212022010203040
2016201720182019202020212022010203040
2016201720182019202020212022010203040
2016201720182019202020212022010203040
Additional affiliations
June 2016 - February 2017
Tokyo Institute of Technology
Position
  • Fellow

Publications

Publications (15)
Article
Full-text available
Underwater image segmentation is useful for benthic habitat mapping and monitoring; however, manual annotation is time-consuming and tedious. We propose automated segmentation of benthic habitats using unsupervised semantic algorithms. Four such algorithms––Fast and Robust Fuzzy C-Means (FR), Superpixel-Based Fast Fuzzy C-Means (FF), Otsu clusterin...
Article
Full-text available
Benthic habitats are structurally complex and ecologically diverse ecosystems that are severely vulnerable to human stressors. Consequently, marine habitats must be mapped and monitored to provide the information necessary to understand ecological processes and lead management actions. In this study, we propose a semiautomated framework for the det...
Article
Full-text available
The accurate classification and 3D mapping of benthic habitats in coastal ecosystems are vital for developing management strategies for these valuable shallow water environments. However, both automatic and semiautomatic approaches for deriving ecologically significant information from a towed video camera system are quite limited. In the current s...
Article
In this study, an approach is proposed for determining the depths of shallow waters from multispectral satellite images. This approach is a hybrid system that uses a Random Forest Ensemble (RFE) technique for Neural Network (NN) and Support Vector Regression (SVR) algorithms to improve accuracy of bathymetry estimations by reducing both errors and...
Article
Full-text available
Benthic habitat monitoring is essential for many applications involving biodiversity, marine resource management, and the estimation of variations over temporal and spatial scales. Nevertheless, both automatic and semi-automatic analytical methods for deriving ecologically significant information from towed camera images are still limited. This stu...
Article
Full-text available
This study evaluates an approach for Land-Use Land-Cover classification (LULC) using multispectral satellite images. This proposed approach uses the Bagging Ensemble (BE) technique with Random Forest (RF) as a base classifier for improving classification performance by reducing errors and prediction variance. A pixel-based supervised classification...
Article
Full-text available
Bathymetric information for shallow coastal/lake areas is essential for hydrological engineering applications such as sedimentary processes and coastal studies. Remotely sensed imagery is considered a time-effective, low-cost, and wide-coverage solution for bathymetric measurements. This study assesses the performance of three proposed empirical mo...
Article
Full-text available
This paper evaluates the performance of two proposed empirical approaches—random forest (RF) and multi-adaptive regression spline (MARS)—for bathymetry calculations in three diverse areas: the Alexandria harbor shallow coastal area, Egypt, as an example of a low-turbidity, silt-sand bottom water area with depths ranging from 4 m to 10.5 m; the Lake...
Chapter
Collecting and analyzing bathymetric information is essential to coastal and lake areas. This is commonly accomplished through field measurements, which are time consuming and costly. However, remotely sensed imagery provides wide coverage, low cost, and time-saving solutions for bathymetric measurements, especially in shallow areas with high erosi...
Article
Full-text available
Determination of bathymetric information is key element for near off shore activities and hydrological studies such as coastal engineering applications, sedimentary processes and hydrographic surveying. Remotely sensed imagery has provided a wide coverage, low cost and time-effective solution for bathymetric measurements. In this paper a methodolog...
Article
Full-text available
Collecting and analysing bathymetric information is essential for lake management. This is particularly true regarding Lake Nasser/Nubia in Egypt, where accumulated sediment in the lake must be examined. This is typically accomplished through field measurements, which are time consuming and costly. However, remotely sensed imagery provides wide cov...
Article
Full-text available
In this research, an approach for estimating shallow water depths (bathymetric map) from multispectral images is proposed. This method is based on using Artificial Neural Network (ANN) fitting algorithms using reflectance of influencing bands of water depths and their logarithms for bathymetry detection. An automated method for calibrating the para...
Conference Paper
Full-text available
In this research, an approach for estimating shallow water depths (bathymetric map) from multispectral images is proposed. This method is based on using Artificial Neural Network (ANN) fitting algorithms using reflectance of influencing bands of water depths and their logarithms for bathymetry detection. An automated method for calibrating the para...
Conference Paper
Full-text available
Determination of bathymetric information is key element for many near/off shore activities and hydrological studies such as coastal engineering applications, sedimentary processes and hydrographic surveying. Remotely sensed imagery has provided a wide coverage, low cost and time-effective solution for bathymetric measurements. In this paper a propo...

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Projects

Project (1)
Archived project
The Nile Delta book: ▶ Provides detailed insights into the requirements for the sustainable development of the Nile Delta ▶ A comprehensive review ▶ Appeals to environmental managers, scientists and policymakers