{"id":{"repo_id":"unsw","oai_identifier":"oai:unsworks.library.unsw.edu.au:1959.4/41497"},"canonical_url":"https://search.dev.ndltd.org/etd/unsw/oai:unsworks.library.unsw.edu.au:1959.4/41497","repository":{"repo_id":"unsw","name":"University of New South Wales","base_url":"https://unsworks.unsw.edu.au/oai/provider"},"display":{"title":"A laboratory study of the 'shoreline' detected in video imagery","abstract":"A controlled laboratory experiment was undertaken to simulate varying swash zone characteristics and sensor-target geometry found in digital images collected by ARGUS coastal imaging systems. Using a hyperspectral sensor, reflectance data were integrated over the respective red, blue and green wavelengths corresponding to a standard ARGUS video imaging sensor. The dominant swash zone parameters affecting shoreline detection were found to be the presence or absence of surface foam, site-specific sediment characteristics (especially colour), and water depth. Winter versus summer solar elevation and the sensor zenith were also found to affect the cross-shore location of the detected waterline. With this new information, site- and time-specific corrections can be applied to coastal digital imagery, to improve the confidence of shoreline detection.","abstract_html":"A controlled laboratory experiment was undertaken to simulate varying swash zone characteristics and sensor-target geometry found in digital images collected by ARGUS coastal imaging systems. Using a hyperspectral sensor, reflectance data were integrated over the respective red, blue and green wavelengths corresponding to a standard ARGUS video imaging sensor. The dominant swash zone parameters affecting shoreline detection were found to be the presence or absence of surface foam, site-specific sediment characteristics (especially colour), and water depth. Winter versus summer solar elevation and the sensor zenith were also found to affect the cross-shore location of the detected waterline. With this new information, site- and time-specific corrections can be applied to coastal digital imagery, to improve the confidence of shoreline detection.","abstract_has_math":false,"creators":["Howard, Elizabeth Helen"],"institution":"UNSW, Sydney","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008","date_published":"2008","updated_at":"2026-07-24T05:34:44Z","subjects":["Image analysis","Video","ARGUS","Shoreline"],"languages":["EN"],"rights":["open access","CC BY-NC-ND 3.0","free_to_read"],"rights_urls":["https://purl.org/coar/access_right/c_abf2","https://creativecommons.org/licenses/by-nc-nd/3.0/au/"],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.26190/unsworks/18759"],"render_values":[{"text":"https://doi.org/10.26190/unsworks/18759","href":"https://doi.org/10.26190/unsworks/18759","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/1959.4/41497","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Howard, Elizabeth Helen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2008"]},{"key":"dc:publisher","label":"Institution","values":["UNSW, Sydney"]},{"key":"dc:type","label":"Dc Type","values":["master thesis","http://purl.org/coar/resource_type/c_bdcc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Image analysis","Video","ARGUS","Shoreline"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["EN"]},{"key":"dc:rights","label":"Dc Rights","values":["open access","https://purl.org/coar/access_right/c_abf2","CC BY-NC-ND 3.0","https://creativecommons.org/licenses/by-nc-nd/3.0/au/","free_to_read"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/1959.4/41497","https://unsworks.unsw.edu.au/bitstreams/afd42c0e-4d71-44a5-8e3e-7700b2e561d2/download","https://doi.org/10.26190/unsworks/18759"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A controlled laboratory experiment was undertaken to simulate varying swash zone characteristics and sensor-target geometry found in digital images collected by ARGUS coastal imaging systems. Using a hyperspectral sensor, reflectance data were integrated over the respective red, blue and green wavelengths corresponding to a standard ARGUS video imaging sensor. The dominant swash zone parameters affecting shoreline detection were found to be the presence or absence of surface foam, site-specific sediment characteristics (especially colour), and water depth. Winter versus summer solar elevation and the sensor zenith were also found to affect the cross-shore location of the detected waterline. With this new information, site- and time-specific corrections can be applied to coastal digital imagery, to improve the confidence of shoreline detection."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A laboratory study of the 'shoreline' detected in video imagery"]}]}],"canonical_facts":{"dc:creator":["Howard, Elizabeth Helen"],"dc:date":["2008"],"dc:description":["A controlled laboratory experiment was undertaken to simulate varying swash zone characteristics and sensor-target geometry found in digital images collected by ARGUS coastal imaging systems. Using a hyperspectral sensor, reflectance data were integrated over the respective red, blue and green wavelengths corresponding to a standard ARGUS video imaging sensor. The dominant swash zone parameters affecting shoreline detection were found to be the presence or absence of surface foam, site-specific sediment characteristics (especially colour), and water depth. Winter versus summer solar elevation and the sensor zenith were also found to affect the cross-shore location of the detected waterline. With this new information, site- and time-specific corrections can be applied to coastal digital imagery, to improve the confidence of shoreline detection."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/1959.4/41497","https://unsworks.unsw.edu.au/bitstreams/afd42c0e-4d71-44a5-8e3e-7700b2e561d2/download","https://doi.org/10.26190/unsworks/18759"],"dc:language":["EN"],"dc:publisher":["UNSW, Sydney"],"dc:rights":["open access","https://purl.org/coar/access_right/c_abf2","CC BY-NC-ND 3.0","https://creativecommons.org/licenses/by-nc-nd/3.0/au/","free_to_read"],"dc:subject":["Image analysis","Video","ARGUS","Shoreline"],"dc:title":["A laboratory study of the 'shoreline' detected in video imagery"],"dc:type":["master thesis","http://purl.org/coar/resource_type/c_bdcc"]},"updated_at":"2026-07-24T05:34:44Z"}