Prediction of Water Depth From Multispectral Satellite Imagery—The Regression Kriging Alternative


Bathymetric data is crucial to the study and management of coastal zones. Passive remote sensing provides a price-effective different to acoustic surveys and bathymetric LiDAR techniques. Most previous studies estimated water depth from multispectral imagery in shallow coastal and inland waters by establishing the link between image pixel spectral values and known water depth measurements, in that the log-linear inversion model is most generally used. Given a group of known water depth sample points, a bathymetric grid/map will be created by employing a spatial interpolation technique. However, when a restricted range of water depth sample points are on the market, the interpolation result's often unsatisfactory for portraying benthic morphology. During this letter, we have a tendency to propose to use the regression kriging (RK) approach to combine the optimal spatial interpolation of kriging with the high-resolution auxiliary information of multispectral imagery for a detailed bathymetric mapping. A case study has been performed to demonstrate and evaluate the performance of the RK methodology as compared with standard kriging and log-linear inversion ways. It shows that the RK technique will manufacture more correct water depth estimations than the log-linear inversion methodology due to the account of the spatial pattern of the modeling residuals. The bathymetric grid created from the RK contains abundant additional spatial details regarding the ocean floor morphology than that from the ordinary kriging owing to the incorporation of auxiliary data from multispectral satellite imagery.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Classification Algorithms based Mental Health Prediction using Data Mining ABSTRACT: Mental health reveals a person's emotional, psychological, and social well-being. It has an impact on how a person thinks, feels,
PROJECT TITLE : Convolutional Recurrent Neural Networks for Glucose Prediction ABSTRACT: Blood glucose control is critical for diabetes management. Machine learning techniques are used in current digital therapy approaches for
PROJECT TITLE : Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers ABSTRACT: Diabetes, often known as chronic sickness, is a collection of metabolic illnesses caused by a persistently high blood sugar
PROJECT TITLE : Machine Learning based Rainfall Prediction ABSTRACT: One of the most significant techniques for predicting meteorological conditions in any country is rainfall prediction. For the Indian dataset, this research
PROJECT TITLE : Prediction of Stock Prices using Machine Learning (Regression, Classification) Algorithms ABSTRACT: The stock market is a fascinating field to research. It comes in a variety of shapes and sizes. Many specialists

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry