Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/17512
Title: | Using object-based hierarchical classification to extract land use land cover classes from high resolution satellite imagery in a complex urban area | Contributor(s): | Gholoobi, Mohsen (author); Kumar, Lalit (author) | Publication Date: | 2015 | Open Access: | Yes | DOI: | 10.1117/1.JRS.9.096052 | Handle Link: | https://hdl.handle.net/1959.11/17512 | Abstract: | Producing land use and land cover (LULC) maps, particularly in complex urban areas, is one of the most important necessities in civil management programs and is an important research topic in satellite image analysis. High-resolution satellite images provide more opportunities for cost-benefit production of such information. This paper proposes a hierarchical LULC classification based on image objects that are created from multiresolution segmentation. A rule-based strategy is used to implement a step-by-step object-based land cover classification on a pan-sharpened IKONOS image taken from a complex urban region in Shiraz, Iran. A new spatial geometrical analysis for the reclassification of unclassified land cover objects is also utilized. After the initial classification, an object-based land use classification is implemented based on the land cover results and using conceptual, spatial, and geometrical modeling of the relationships between land use elements. Overall classification accuracy was 89 and 87% for land cover and land use approaches, respectively. In the best unclassified object analysis, ∼70% of unclassified objects were reclassified correctly. The hierarchical methodology proposed here results in fewer unclassified objects since a multistage classification process is utilized rather than the traditional one-pass classification. | Publication Type: | Journal Article | Source of Publication: | Journal of Applied Remote Sensing, 9(1), p. 1-15 | Publisher: | International Society for Optical Engineering (SPIE) | Place of Publication: | United States of America | ISSN: | 1931-3195 | Fields of Research (FoR) 2008: | 090903 Geospatial Information Systems 090905 Photogrammetry and Remote Sensing |
Fields of Research (FoR) 2020: | 401302 Geospatial information systems and geospatial data modelling 401304 Photogrammetry and remote sensing |
Socio-Economic Objective (SEO) 2008: | 960610 Urban Land Evaluation 960911 Urban and Industrial Land Management 960604 Environmental Management Systems |
Socio-Economic Objective (SEO) 2020: | 180603 Evaluation, allocation, and impacts of land use 189999 Other environmental management not elsewhere classified |
Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article School of Environmental and Rural Science |
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