International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
ESTIMATION OF DEPTH OF RIVER BY BATHYMETRY OF SATELLITE IMAGES Saurabh Ankam1, Ayush Thite2, Chinmay Mandavkar3, Aditi Mehta4, Prof. Dr. Reena Gunjan5 1, 2, 3, 4, 5 Dept. of Computer Science and Engineering MIT Art Design and Technology University Pune, India
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Abstract - The collection of bathymetric data is crucial for
conditions of sunlight, has opened up new possibilities for researchers to explore this area.This study aims to demonstrate the estimation of river water depth using satellite images and to develop a method for mapping the underwater features of rivers.
examining the river environment, watershed hydrological response, and river hydraulics. However, traditional field surveys for acquiring such data are costly and timeconsuming, and may be hindered by inaccessibility to partially or completely submerged riverbeds. To supplement these surveys, remote sensing methods offer new possibilities. The objective of this research is to showcase how satellite imagery can be utilized to approximate the depth of water in a river. Specifically, the study focuses on the shallow portion of the Mula-Mutha River situated near Pune, utilizing four spectral bands of high-resolution multispectral satellite imagery (red, green, blue, and nearinfrared) with minimal cloud interference and adequate light penetration. Furthermore, correlation analysis between frequency bands and field measurements has been conducted at numerous survey sites.
2. EXISTING WORK Hojat Ghorbanidehno [1] discussed the importance of river bathymetry and the difficulty of obtaining direct measurements of depth. However, indirect measurements can be obtained using physics-based inverse modeling techniques. A new deep learning framework is developed and applied to three problems of identifying river bathymetry by combining connected principal component analysis (PCA) and DNN. Tatsuyuki Sagawa [2] proposed a method to create a general depth estimation model, a shallow water bathymetric mapping approach was developed using Random Forest learning and multitemporal satellite imagery. The research looked at 135, Landsat-8 images and extensive training bathymetric data from five different regions. Satellite bathymetry (SDB) accuracy was tested against reference bathymetric data.
Key Words: Satellite imagery, Bathymetry, Image processing, Machine learning
1.INTRODUCTION The analysis of the underwater topography or bathymetry of water bodies like rivers, streams, seas, and lakes is crucial for predicting shorelines, seabed depth, and managing flood protection and vessel operations. The objective of this is to create a prototype using machine learning and deep learning techniques that can perform bathymetry on a large scale. Bathymetry, which refers to the study of the floors or beds of water bodies, is an important area of research that has various applications in hydrology, water resource management, shipping operations, and flood management. Traditional approaches to collecting bathymetry data, such as field surveys, can be time-consuming and expensive. Furthermore, inaccessible riverbeds can make field surveys challenging to conduct in many cases. Remote sensing techniques, such as satellite image processing, provide new ways to supplement traditional field surveys. Satellite imagery offers a unique opportunity to obtain information about the depth and shape of underwater land, enabling us to estimate river water depth and map the underwater features of rivers. Satellite image processing for bathymetry of rivers has become an area of interest in recent years due to its potential to provide large-scale, high-resolution, and accurate depth estimation with minimal cost and time. Highresolution, multispectral satellite imagery is now available., which can penetrate the water body with favorable
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Motoharu Sonogashira [3] performed deep learning-based image super-resolution experiments to improve resolution of bathymetric data. By implementing this technique, the quantity of sea areas or points that require measurement is decreased, leading to the swift and detailed mapping of the seabed and the creation of high-resolution bathymetric maps of the encompassing regio. Manuel Erena [4] emphasized that the applicability and utility of bathymetric information is:The effectiveness of the approach is heavily reliant on the standard and spatial precision of the data, in addition to the configuration of the model network. He proposed using various remote sensing tools to obtain extensive inland bathymetric information and one approach is to obtain coastal water masses with progressively enhanced spatial resolution. Jiaxin Wan [5] concluded that coastal bathymetry is an important parameter in coastal research and management. Previous research has shown the importance of obtaining high resolution coastal bathymetric data. The study found that the combination of high-resolution multispectral
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