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)orcid 
Publication Date: 2015
Open Access: Yes
DOI: 10.1117/1.JRS.9.096052Open Access Link
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: SPIE: International Society for Optical Engineering
Place of Publication: United States of America
ISSN: 1931-3195
Field of Research (FOR): 090903 Geospatial Information Systems
090905 Photogrammetry and Remote Sensing
Socio-Economic Objective (SEO): 960610 Urban Land Evaluation
960911 Urban and Industrial Land Management
960604 Environmental Management Systems
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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School of Environmental and Rural Science

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