{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/78781"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/78781","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Building detection in SAR imagery","abstract":"Current techniques for building detection in Synthetic Aperture Radar (SAR) imagery can be computationally expensive and/or enforce stringent requirements for data acquisition. I present two techniques that are effective and efficient at determining an approximate building location. This approximate location can be used to extract a portion of the SAR image to then perform a more robust detection. The proposed techniques assume that for the desired image, bright lines and shadows (SAR artifact effects) are approximately labeled. These labels are enhanced and utilized to locate buildings, only if the related bright lines and shadows can be grouped. In order to find which of the bright lines and shadows are related, all of the bright lines are connected to all of the shadows. This allows the problem to be solved from a connected graph viewpoint, where the nodes are the bright lines and shadows and the arcs are the connections between bright lines and shadows. For the first technique (simple graph grouping), constraints based on angle of depression and the relationship between connected bright lines and shadows are applied to remove unrelated arcs. The second technique (weighted graph grouping) calculates weights for the connections and then performs a series of increasingly relaxed hard and soft thresholds. This thresholding results in groups of bright lines and shadows produced from various initial threshold levels. These different groups will be labeled and interpreted according to their initial thresholds. Once the related bright lines and shadows are grouped, their locations are combined to provide an approximate building location. Experimental results demonstrate the outcome of the two techniques. The two techniques are compared and discussed.","abstract_html":"Current techniques for building detection in Synthetic Aperture Radar (SAR) imagery can be computationally expensive and/or enforce stringent requirements for data acquisition. I present two techniques that are effective and efficient at determining an approximate building location. This approximate location can be used to extract a portion of the SAR image to then perform a more robust detection. The proposed techniques assume that for the desired image, bright lines and shadows (SAR artifact effects) are approximately labeled. These labels are enhanced and utilized to locate buildings, only if the related bright lines and shadows can be grouped. In order to find which of the bright lines and shadows are related, all of the bright lines are connected to all of the shadows. This allows the problem to be solved from a connected graph viewpoint, where the nodes are the bright lines and shadows and the arcs are the connections between bright lines and shadows. For the first technique (simple graph grouping), constraints based on angle of depression and the relationship between connected bright lines and shadows are applied to remove unrelated arcs. The second technique (weighted graph grouping) calculates weights for the connections and then performs a series of increasingly relaxed hard and soft thresholds. This thresholding results in groups of bright lines and shadows produced from various initial threshold levels. These different groups will be labeled and interpreted according to their initial thresholds. Once the related bright lines and shadows are grouped, their locations are combined to provide an approximate building location. Experimental results demonstrate the outcome of the two techniques. The two techniques are compared and discussed.","abstract_has_math":false,"creators":["Steinbach, Ryan Matthew"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-07-22T22:46:02Z","date_published":"2015-07-22T22:46:02Z","updated_at":"2026-07-22T22:26:12Z","subjects":["Synthetic Aperture Radar (SAR)","Building Detection","Synthetic Aperture Radar (SAR) artifact effects","shadows","bright lines"],"languages":["en"],"rights":["Copyright 2015 Ryan Matthew Steinbach"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/78781","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Steinbach, Ryan Matthew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-07-22T22:46:02Z","2017-07-23T09:15:32Z","2015-05","2015-04-27","2015-5"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Synthetic Aperture Radar (SAR)","Building Detection","Synthetic Aperture Radar (SAR) artifact effects","shadows","bright lines"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Ryan Matthew Steinbach"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/78781"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Current techniques for building detection in Synthetic Aperture Radar (SAR) imagery can be computationally expensive and/or enforce stringent requirements for data acquisition. I present two techniques that are effective and efficient at determining an approximate building location. This approximate location can be used to extract a portion of the SAR image to then perform a more robust detection. The proposed techniques assume that for the desired image, bright lines and shadows (SAR artifact effects) are approximately labeled. These labels are enhanced and utilized to locate buildings, only if the related bright lines and shadows can be grouped. In order to find which of the bright lines and shadows are related, all of the bright lines are connected to all of the shadows. This allows the problem to be solved from a connected graph viewpoint, where the nodes are the bright lines and shadows and the arcs are the connections between bright lines and shadows. For the first technique (simple graph grouping), constraints based on angle of depression and the relationship between connected bright lines and shadows are applied to remove unrelated arcs. The second technique (weighted graph grouping) calculates weights for the connections and then performs a series of increasingly relaxed hard and soft thresholds. This thresholding results in groups of bright lines and shadows produced from various initial threshold levels. These different groups will be labeled and interpreted according to their initial thresholds. Once the related bright lines and shadows are grouped, their locations are combined to provide an approximate building location. Experimental results demonstrate the outcome of the two techniques. The two techniques are compared and discussed.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-05-01","The student, Ryan Steinbach, accepted the attached license on 2015-04-24 at 13:17.","The student, Ryan Steinbach, submitted this Thesis for approval on 2015-04-24 at 13:20.","This Thesis was approved for publication on 2015-04-27 at 17:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8069 on 2015-07-22 at 14:26:24","Made available in DSpace on 2015-07-22T22:46:02Z (GMT). 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I present two techniques that are effective and efficient at determining an approximate building location. This approximate location can be used to extract a portion of the SAR image to then perform a more robust detection. The proposed techniques assume that for the desired image, bright lines and shadows (SAR artifact effects) are approximately labeled. These labels are enhanced and utilized to locate buildings, only if the related bright lines and shadows can be grouped. In order to find which of the bright lines and shadows are related, all of the bright lines are connected to all of the shadows. This allows the problem to be solved from a connected graph viewpoint, where the nodes are the bright lines and shadows and the arcs are the connections between bright lines and shadows. For the first technique (simple graph grouping), constraints based on angle of depression and the relationship between connected bright lines and shadows are applied to remove unrelated arcs. The second technique (weighted graph grouping) calculates weights for the connections and then performs a series of increasingly relaxed hard and soft thresholds. This thresholding results in groups of bright lines and shadows produced from various initial threshold levels. These different groups will be labeled and interpreted according to their initial thresholds. Once the related bright lines and shadows are grouped, their locations are combined to provide an approximate building location. Experimental results demonstrate the outcome of the two techniques. The two techniques are compared and discussed.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-05-01","The student, Ryan Steinbach, accepted the attached license on 2015-04-24 at 13:17.","The student, Ryan Steinbach, submitted this Thesis for approval on 2015-04-24 at 13:20.","This Thesis was approved for publication on 2015-04-27 at 17:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8069 on 2015-07-22 at 14:26:24","Made available in DSpace on 2015-07-22T22:46:02Z (GMT). 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