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Remote Sens. 2014, 6(11), 11372-11390; doi:10.3390/rs61111372

Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA

Julie Ann Wrigley Global Institute of Sustainability, Arizona State University, Tempe, AZ 85287, USA
Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USA
Author to whom correspondence should be addressed.
Received: 16 July 2014 / Revised: 15 October 2014 / Accepted: 24 October 2014 / Published: 14 November 2014
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km2 land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods. View Full-Text
Keywords: object-based image analysis; OBIA; orthophoto; land cover classification; urban landscape mapping; Indiana object-based image analysis; OBIA; orthophoto; land cover classification; urban landscape mapping; Indiana

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, X.; Shao, G. Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA. Remote Sens. 2014, 6, 11372-11390.

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