Figure 4 - uploaded by Sheng-Ming Wang
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The 1988 Level II Land Use Map (top left: the base land use map; top right: the land use map produced using SMAP method only; bottom left: the land use map produced using the object{based approach; bottom right: the legend of level II land use map.)
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An object-based approach for producing land use maps will be described in this paper. This approach has been used for integrating Landsat TM data within a GIS context for producing land use maps of urban-rural fringe areas. A contextual image classification method based on the SMAP estimate was used to produce land cover maps which provide knowledg...
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... Object-based image analysis (OBIA or GEOBIA for geospatial object based image analysis) is widely used for detecting changes and making maps from satellite images in land-cover, landuse and forest map studies (Barnsley and Barr 1996, Blaschke 2010, Herold et al. 2003, Platt and Rapoza 2008, Tansey et al. 2009, Wang et al. 1997, Zhou and Troy 2008. As a critical step of GEOBIA, Object-oriented (OO) classification is capable of extracting information of interest for various applications from images at multiple levels (Lang 2008, Marpu et al. 2010, Tansey et al. 2009, Wang et al. 2004). ...
Object-oriented (OO) image analysis provides an efficient way to generate vector-format land-cover and
land-use maps from remotely sensed images. Such image-derived vector maps, however, are generally
presented with congested and twisted polygons with step-like boundaries. They include unclassified
polygons and polygons with geometric conflicts such as unreadable small areas and narrow corridors.
The complex and poorly readable representations usually make such maps not comply well with the
Gestalt principle of cartography. This article describes a framework designed to improve the representation
by resolving these problematic polygons. It presents a polygon similarity model integrating
semantic, geometric and spectral characteristics of the image-derived polygons to eliminate small and
unclassified polygons. In addition, an outward-inward-buffering approach is presented to resolve the
narrow-corridor conflicts of a polygon and improve its overall appearance. A case study demonstrates
that the implementation of the framework reduces the number of the polygons by 32% and the length
of the polygon boundaries by 20%. At the same time, it does not cause distinct changes the distribution
of land-use types (less than 0.05%) and the overall accuracy (decreased only 0.02%) as compared
with the original image-derived land-use maps. We conclude that the presented framework and models
effectively improve the overall representation of image-derived maps without distinct changes in their
semantic characteristics and accuracy.
Object-oriented (OO) image analysis provides an efficient way to generate vector-format land-cover and land-use maps from remotely sensed images. Such image-derived vector maps, however, are generally presented with congested and twisted polygons with step-like boundaries. They include unclassified polygons and polygons with geometric conflicts such as unreadable small areas and narrow corridors. The complex and poorly readable representations usually make such maps not comply well with the Gestalt principle of cartography. This article describes a framework designed to improve the representation by resolving these problematic polygons. It presents a polygon similarity model integrating semantic, geometric and spectral characteristics of the image-derived polygons to eliminate small and unclassified polygons. In addition, an outward-inward-buffering approach is presented to resolve the narrow-corridor conflicts of a polygon and improve its overall appearance. A case study demonstrates that the implementation of the framework reduces the number of the polygons by 32% and the length of the polygon boundaries by 20%. At the same time, it does not cause distinct changes the distribution of land-use types (less than 0.05%) and the overall accuracy (decreased only 0.02%) as compared with the original image-derived land-use maps. We conclude that the presented framework and models effectively improve the overall representation of image-derived maps without distinct changes in their semantic characteristics and accuracy.
