{"id":{"repo_id":"freiburg-diss","oai_identifier":"oai:freidok.uni-freiburg.de:753"},"canonical_url":"https://search.dev.ndltd.org/etd/freiburg-diss/oai:freidok.uni-freiburg.de:753","repository":{"repo_id":"freiburg-diss","name":"University of Freiburg","base_url":"https://freidok.uni-freiburg.de/oai/oai2.php"},"display":{"title":"Automatic land-cover-classification derived from high-resolution Ikonos satellite image in the urban atlantic forest in Rio de Janeiro, Brasil by means of an objects-oriented approach","abstract":"The city of Rio de Janeiro carried out a Land-cover forest classification with visual interpretation using SPOT data. This work produced a compatible thematic map in the scale 1:50,000. The scale of these maps permit to have a global vision of the land change cover but unfortunately do not correspond with the geographic information system of the city, which works with a scale of 1:10,000. The city searched for options to make this work automatically and quickly to get information for planning and to propose solutions.
In order to solve this problem high resolution satellite data and automatic classification of Land-cover classes are needed. Consequently, images as IKONOS need to be used to produce a classification, with a scale corresponding to the GIS of the city.
Pixel based classification with high resolution data show some problems because the level of information in the data produce a lot of incorrect classified pixels. The solution to perform this classification uses the new approach that makes one “pre-classification”, which transforms the pixel information in objects as well as the feature in the vector representation. To carry out the segmentation and classification processes, oriented objects analysis are used.","abstract_html":"The city of Rio de Janeiro carried out a Land-cover forest classification with visual interpretation using SPOT data. This work produced a compatible thematic map in the scale 1:50,000. The scale of these maps permit to have a global vision of the land change cover but unfortunately do not correspond with the geographic information system of the city, which works with a scale of 1:10,000. The city searched for options to make this work automatically and quickly to get information for planning and to propose solutions. <br>In order to solve this problem high resolution satellite data and automatic classification of Land-cover classes are needed. Consequently, images as IKONOS need to be used to produce a classification, with a scale corresponding to the GIS of the city. <br>Pixel based classification with high resolution data show some problems because the level of information in the data produce a lot of incorrect classified pixels. The solution to perform this classification uses the new approach that makes one “pre-classification”, which transforms the pixel information in objects as well as the feature in the vector representation. To carry out the segmentation and classification processes, oriented objects analysis are used.","abstract_has_math":false,"creators":["Guanaes Rego, Luiz Felipe"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Koch, Barbara"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T02:21:58Z","subjects":["Ikonos","Automatische Klassifizierung","Küstenregenwald","Brasilien","Automatic Classification","Atlantic Forest"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://freidok.uni-freiburg.de/data/753","outbound_label":"Repository record","outbound_source":"source_url"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koch, Barbara"]},{"key":"dc:creator","label":"Author","values":["Guanaes Rego, Luiz Felipe"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["DoctoralThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ikonos","Automatische Klassifizierung","Küstenregenwald","Brasilien","Automatic Classification","Atlantic Forest"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The city of Rio de Janeiro carried out a Land-cover forest classification with visual interpretation using SPOT data. This work produced a compatible thematic map in the scale 1:50,000. The scale of these maps permit to have a global vision of the land change cover but unfortunately do not correspond with the geographic information system of the city, which works with a scale of 1:10,000. The city searched for options to make this work automatically and quickly to get information for planning and to propose solutions.
In order to solve this problem high resolution satellite data and automatic classification of Land-cover classes are needed. Consequently, images as IKONOS need to be used to produce a classification, with a scale corresponding to the GIS of the city.
Pixel based classification with high resolution data show some problems because the level of information in the data produce a lot of incorrect classified pixels. The solution to perform this classification uses the new approach that makes one “pre-classification”, which transforms the pixel information in objects as well as the feature in the vector representation. To carry out the segmentation and classification processes, oriented objects analysis are used."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Automatic land-cover-classification derived from high-resolution Ikonos satellite image in the urban atlantic forest in Rio de Janeiro, Brasil by means of an objects-oriented approach","Automatische Land-Cover-Klassifizierung von Ikonos Satellitenbildern mit hoher Auflösung des urbanen atlantischen Küstenregenwaldes in Rio de Janeiro/Brasilien, anhand eines objekt basierten Segmentierungsansatzes"]}]}],"canonical_facts":{"dc:contributor":["Koch, Barbara"],"dc:creator":["Guanaes Rego, Luiz Felipe"],"dc:description.abstract":["The city of Rio de Janeiro carried out a Land-cover forest classification with visual interpretation using SPOT data. This work produced a compatible thematic map in the scale 1:50,000. The scale of these maps permit to have a global vision of the land change cover but unfortunately do not correspond with the geographic information system of the city, which works with a scale of 1:10,000. The city searched for options to make this work automatically and quickly to get information for planning and to propose solutions.
In order to solve this problem high resolution satellite data and automatic classification of Land-cover classes are needed. Consequently, images as IKONOS need to be used to produce a classification, with a scale corresponding to the GIS of the city.
Pixel based classification with high resolution data show some problems because the level of information in the data produce a lot of incorrect classified pixels. The solution to perform this classification uses the new approach that makes one “pre-classification”, which transforms the pixel information in objects as well as the feature in the vector representation. To carry out the segmentation and classification processes, oriented objects analysis are used."],"dc:format.medium":["application/pdf"],"dc:subject":["Ikonos","Automatische Klassifizierung","Küstenregenwald","Brasilien","Automatic Classification","Atlantic Forest"],"dc:title":["Automatic land-cover-classification derived from high-resolution Ikonos satellite image in the urban atlantic forest in Rio de Janeiro, Brasil by means of an objects-oriented approach","Automatische Land-Cover-Klassifizierung von Ikonos Satellitenbildern mit hoher Auflösung des urbanen atlantischen Küstenregenwaldes in Rio de Janeiro/Brasilien, anhand eines objekt basierten Segmentierungsansatzes"],"dc:type":["DoctoralThesis"]},"updated_at":"2026-07-24T02:21:58Z"}