Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery. Issue 13 (3rd July 2018)

• Record Type: Journal Article
• Authors: Misra, Ankita; Vojinovic, Zoran; Ramakrishnan, Balaji; Luijendijk, Arjen; Ranasinghe, Roshanka
• Is Part Of: International journal of remote sensing. Volume 39:Issue 13(2018)
• Page Start: 4431
• Page End: 4450
• Publication Date: 2018-07-03
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2017.1421796
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Satellite imagery along with image processing techniques prove to be efficient tools for bathymetry retrieval as they provide time and cost-effective alternatives to traditional methods of water depth estimation. In this article, a nonlinear machine learning technique of Support Vector Machine (SVM) is used to derive shallow water bathymetry data along Sint Maarten Island and Ameland Inlet, The Netherlands, by combining echo-sounding measurements and the reflectance of blue, green, or red bands of Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and Landsat 8 Operational Land Imager (OLI) imagery with 30 m spatial resolution. In the analysis, 80% of data points of the echo-sounding measurements are used for training and the remaining 20% data points are used for testing. The model utilizes the radial basis kernel function (nonlinear) and the other training factors such as the smoothing parameter, penalty parameter C, and insensitivity zone ε are selected and tuned based on the learning (i.e. training) process. The overall errors during test phases for Sint Maarten Island (1–15 m) and Ameland Inlet (1.00–3.50 m) are 8.26% and 14.43%, respectively, reflecting that the model produces significant estimations for the shallow depths ranges, considered in this study. The results obtained are also compared statistically with those estimated from the widely used linear transform model and ratio transform model, which establish a linear relationship between the water depth and band reflectances. Based on the results, it is evident that SVM provides a comparable or better performance for shallow depth ranges and can be used effectively for deriving accurate and updated medium resolution bathymetric maps.