{"id":{"repo_id":"lsu-thes","oai_identifier":"oai:repository.lsu.edu:gradschool_dissertations-2455"},"canonical_url":"https://search.dev.ndltd.org/etd/lsu-thes/oai:repository.lsu.edu:gradschool_dissertations-2455","repository":{"repo_id":"lsu-thes","name":"Lousiana State University","base_url":"https://repository.lsu.edu/do/oai/"},"display":{"title":"An object-based image analysis approach for detecting urban impervious surfaces","abstract":"Impervious surfaces are manmade surfaces which are highly resistant to infiltration of water. Previous attempts to classify impervious surfaces from high spatial resolution imagery with pixel-based techniques have proven to be unsuitable for automated classification because of its high spectral variability and complex land covers in urban areas. Accurate and rapid classification of impervious surfaces would help in emergency management after extreme events like flooding, earthquakes, fires, tsunami, and hurricanes, by providing quick estimates and updated maps for emergency response. The objectives of this study were to: (1) compare classification accuracy between pixel-based and OBIA methods, (2) examine whether the object-based image analysis (OBIA) could better detect urban impervious surfaces, and (3) develop an automated, generalized OBIA classification method for impervious surfaces.
This study analyzed urban impervious surfaces using a 1-meter spatial resolution, four band Digital Orthophoto Quarter Quad (DOQQ) aerial imagery of downtown New Orleans, Louisiana taken as part of post Hurricane Katrina and Rita dataset. The study compared the traditional pixel-based classification with four variations of the rule-based OBIA approach for classification accuracy. A four-class classification scheme was used for the analysis, including impervious surfaces, vegetation, shadow, and water. The results show that OBIA accuracy ranges from 85.33% through 91.41% compared with 80.67% classification accuracy from using the pixel-based approach. OBIA rule-based method 4 utilizing a multi-resolution segmentation approach and derived spectral indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the Spectral Shape Index (SSI) was the best method, yielding a 91.41% classification accuracy. OBIA rule-based method 4 can be automated and generalized for multiple study areas. A test of the segmentation parameters show that parameter values of scale ≤ 20, color/shape ranging from 0.1 - 0.3, and compactness/smoothness ranging from 0.4 - 0.6 yielded the highest classification accuracies. These results show that the developed OBIA method was accurate, generalizable, and capable of automation for the classification of urban impervious surfaces.","abstract_html":"Impervious surfaces are manmade surfaces which are highly resistant to infiltration of water. Previous attempts to classify impervious surfaces from high spatial resolution imagery with pixel-based techniques have proven to be unsuitable for automated classification because of its high spectral variability and complex land covers in urban areas. Accurate and rapid classification of impervious surfaces would help in emergency management after extreme events like flooding, earthquakes, fires, tsunami, and hurricanes, by providing quick estimates and updated maps for emergency response. The objectives of this study were to: (1) compare classification accuracy between pixel-based and OBIA methods, (2) examine whether the object-based image analysis (OBIA) could better detect urban impervious surfaces, and (3) develop an automated, generalized OBIA classification method for impervious surfaces. <br><br> This study analyzed urban impervious surfaces using a 1-meter spatial resolution, four band Digital Orthophoto Quarter Quad (DOQQ) aerial imagery of downtown New Orleans, Louisiana taken as part of post Hurricane Katrina and Rita dataset. The study compared the traditional pixel-based classification with four variations of the rule-based OBIA approach for classification accuracy. A four-class classification scheme was used for the analysis, including impervious surfaces, vegetation, shadow, and water. The results show that OBIA accuracy ranges from 85.33% through 91.41% compared with 80.67% classification accuracy from using the pixel-based approach. OBIA rule-based method 4 utilizing a multi-resolution segmentation approach and derived spectral indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the Spectral Shape Index (SSI) was the best method, yielding a 91.41% classification accuracy. OBIA rule-based method 4 can be automated and generalized for multiple study areas. A test of the segmentation parameters show that parameter values of scale ≤ 20, color/shape ranging from 0.1 - 0.3, and compactness/smoothness ranging from 0.4 - 0.6 yielded the highest classification accuracies. These results show that the developed OBIA method was accurate, generalizable, and capable of automation for the classification of urban impervious surfaces.","abstract_has_math":false,"creators":["Kulkarni, Amit"],"institution":"Geography and Anthropology","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Social and Behavioral Sciences","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-01-01T08:00:00Z","date_published":"2012-01-01T08:00:00Z","updated_at":"2026-07-24T02:59:31Z","subjects":["OBIA","remote sensing","impervious surface"],"languages":[],"rights":["unrestricted","Release the entire work immediately for access worldwide."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["etd-09042012-140648","https://repository.lsu.edu/gradschool_dissertations/1456"],"render_values":[{"text":"etd-09042012-140648","href":null,"code":true},{"text":"https://repository.lsu.edu/gradschool_dissertations/1456","href":"https://repository.lsu.edu/gradschool_dissertations/1456","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.31390/gradschool_dissertations.1456","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Kulkarni, Amit"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012-05-15"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-05-12T23:12:11Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Social and Behavioral Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Geography and Anthropology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["OBIA","remote sensing","impervious surface"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","Release the entire work immediately for access worldwide."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["etd-09042012-140648","10.31390/gradschool_dissertations.1456","https://repository.lsu.edu/gradschool_dissertations/1456"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Impervious surfaces are manmade surfaces which are highly resistant to infiltration of water. Previous attempts to classify impervious surfaces from high spatial resolution imagery with pixel-based techniques have proven to be unsuitable for automated classification because of its high spectral variability and complex land covers in urban areas. Accurate and rapid classification of impervious surfaces would help in emergency management after extreme events like flooding, earthquakes, fires, tsunami, and hurricanes, by providing quick estimates and updated maps for emergency response. The objectives of this study were to: (1) compare classification accuracy between pixel-based and OBIA methods, (2) examine whether the object-based image analysis (OBIA) could better detect urban impervious surfaces, and (3) develop an automated, generalized OBIA classification method for impervious surfaces.
This study analyzed urban impervious surfaces using a 1-meter spatial resolution, four band Digital Orthophoto Quarter Quad (DOQQ) aerial imagery of downtown New Orleans, Louisiana taken as part of post Hurricane Katrina and Rita dataset. The study compared the traditional pixel-based classification with four variations of the rule-based OBIA approach for classification accuracy. A four-class classification scheme was used for the analysis, including impervious surfaces, vegetation, shadow, and water. The results show that OBIA accuracy ranges from 85.33% through 91.41% compared with 80.67% classification accuracy from using the pixel-based approach. OBIA rule-based method 4 utilizing a multi-resolution segmentation approach and derived spectral indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the Spectral Shape Index (SSI) was the best method, yielding a 91.41% classification accuracy. OBIA rule-based method 4 can be automated and generalized for multiple study areas. A test of the segmentation parameters show that parameter values of scale ≤ 20, color/shape ranging from 0.1 - 0.3, and compactness/smoothness ranging from 0.4 - 0.6 yielded the highest classification accuracies. These results show that the developed OBIA method was accurate, generalizable, and capable of automation for the classification of urban impervious surfaces."]},{"key":"dc:title","label":"Title","values":["An object-based image analysis approach for detecting urban impervious surfaces"]}]}],"canonical_facts":{"dc:creator":["Kulkarni, Amit"],"dc:date":["2012-05-15"],"dc:date.available":["2022-05-12T23:12:11Z"],"dc:description.abstract":["Impervious surfaces are manmade surfaces which are highly resistant to infiltration of water. Previous attempts to classify impervious surfaces from high spatial resolution imagery with pixel-based techniques have proven to be unsuitable for automated classification because of its high spectral variability and complex land covers in urban areas. Accurate and rapid classification of impervious surfaces would help in emergency management after extreme events like flooding, earthquakes, fires, tsunami, and hurricanes, by providing quick estimates and updated maps for emergency response. The objectives of this study were to: (1) compare classification accuracy between pixel-based and OBIA methods, (2) examine whether the object-based image analysis (OBIA) could better detect urban impervious surfaces, and (3) develop an automated, generalized OBIA classification method for impervious surfaces.
This study analyzed urban impervious surfaces using a 1-meter spatial resolution, four band Digital Orthophoto Quarter Quad (DOQQ) aerial imagery of downtown New Orleans, Louisiana taken as part of post Hurricane Katrina and Rita dataset. The study compared the traditional pixel-based classification with four variations of the rule-based OBIA approach for classification accuracy. A four-class classification scheme was used for the analysis, including impervious surfaces, vegetation, shadow, and water. The results show that OBIA accuracy ranges from 85.33% through 91.41% compared with 80.67% classification accuracy from using the pixel-based approach. OBIA rule-based method 4 utilizing a multi-resolution segmentation approach and derived spectral indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the Spectral Shape Index (SSI) was the best method, yielding a 91.41% classification accuracy. OBIA rule-based method 4 can be automated and generalized for multiple study areas. A test of the segmentation parameters show that parameter values of scale ≤ 20, color/shape ranging from 0.1 - 0.3, and compactness/smoothness ranging from 0.4 - 0.6 yielded the highest classification accuracies. These results show that the developed OBIA method was accurate, generalizable, and capable of automation for the classification of urban impervious surfaces."],"dc:identifier":["etd-09042012-140648","10.31390/gradschool_dissertations.1456","https://repository.lsu.edu/gradschool_dissertations/1456"],"dc:rights":["unrestricted","Release the entire work immediately for access worldwide."],"dc:subject":["OBIA","remote sensing","impervious surface"],"dc:title":["An object-based image analysis approach for detecting urban impervious surfaces"],"thesis:degree_discipline":["Social and Behavioral Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"],"thesis:institution_name":["Geography and Anthropology"]},"updated_at":"2026-07-24T02:59:31Z"}