Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A hybrid level set based approach for surface water delineation using landsat-8 multispectral images Subject The topic of the resource Landsat8 Multispectral Images; Level Sets; Modified Normalized Water Index; Surface Water Delineation Description An account of the resource The detection and delineation of surface water is a crucial step in change detection studies on water bodies using satellite images. Single band methods, spectral index methods, classification using machine learning and spectral un-mixing methods are the widely used strategies for surface water mapping from multi-spectral images. Level set theory based algorithms have been successfully employed in image segmentation problems and are proven to be effective. This study presents a hybrid level set theory based segmentation algorithm which is a combination of edge based and region based approaches to detect and delineate surface water bodies in Landsat 8 images. Level set algorithms were applied in combination with Modified Normalized Difference Water Index (MNDWI) to further improve the delineation accuracy. Robustness of the proposed approach was established by successfully applying the algorithm to delineate water bodies of different sizes, ranging from 0.5 km2 to 298 km2 in surface area. The proposed algorithm was also compared with established machine learning based delineation methods and found to be faster than the algorithms those produced comparable delineation outputs. As the ground truth was not available for accuracy measurement, the output image of the proposed method was compared with the outputs of the machine learning algorithms using Pearsons correlation co-efficient, Structural Similarity Index (SSIM) and Dice Similarity Index. The proposed algorithm was subsequently applied to multi-temporal Landsat data for water body change detection and analysis. 2021, International Association of Engineers. All rights reserved. Creator An entity primarily responsible for making the resource Bijeesh T.V.; Narasimhamurthy K.N. Source A related resource from which the described resource is derived Engineering Letters, Vol-29, No. 2, pp. 624-633. Publisher An entity responsible for making the resource available International Association of Engineers Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106871269&amp;partnerID=40&amp;md5=cc7213057e80f83573e407d26df0c35c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106871269&amp;partnerID=40&amp;md5=cc7213057e80f83573e407d26df0c35c</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 1816093X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bijeesh T.V., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Narasimhamurthy K.N., Department of Civil Engineering, CHRIST (Deemed to be University), Bangalore, India