Surface water detection and delineation using remote sensing images: a review of methods and algorithms

Title

Surface water detection and delineation using remote sensing images: a review of methods and algorithms

Creator

Bijeesh T.V.; Narasimhamurthy K.N.

Description

Multispectral and hyperspectral images captured by remote sensing satellites or airborne sensors contain abundant information that can be used to study and analyze objects of interest on the surface of earth and their properties. The potential of remotely sensed images for studying natural resources like water has been studied by researchers over the past many years. As water is an important natural resource that needs to be conserved, such studies have been of great interest to the scientific community. By employing appropriate digital image processing techniques on images taken from remote sensing satellites or airborne sensors, an effective system can be developed to study the quantitative and qualitative changes happening to surface water bodies over a period of time. Surface water detection and mapping is a crucial and necessary step in such studies and different automated and semi-automated methods have been developed over the years for mapping water in remotely sensed images. Remote sensing sensors capture images at multiple bands corresponding to different wavelength ranges in the EM spectrum. Digital image processing based techniques for water mapping falls predominantly into four categories; (i) single band based methods, (ii) spectral index based methods, (iii) machine learning based methods and (iv) spectral mixture analysis based methods. This paper presents a review of techniques, methods, algorithms and the sensors/satellites that have been developed and experimented with to perform surface water body detection and delineation from remote sensing images. 2020, Springer Nature Switzerland AG.

Source

Sustainable Water Resources Management, Vol-6, No. 4

Date

2020-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Machine learning; Spectral index; Spectral mixture analysis; Surface water detection

Coverage

Bijeesh T.V., Christ University (Deemed), Bangalore, India; Narasimhamurthy K.N., Christ University (Deemed), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23635037

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s40899-020-00425-4

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088117292&doi=10.1007%2fs40899-020-00425-4&partnerID=40&md5=b1333c917a66629300c9c5eec005b708