Urban building extraction through object-based image classification assisted by digital surface model and zoning map

This study develops an object-based image classification methodology for urban land covers classification, using very high resolution aerial images, elevation data and city zoning maps. Logically structured classification rules based on spectral, spatial and contextual features of the segmented objects are first created and tested over a small urban area. The same rule set is then transferred and tested on two similar images covering larger urban areas. The land cover classification results through the transferability of the rule set prove the effectiveness of the methodology and produce satisfactory classification results with an overall accuracy of 91% as against 96% that was achieved over the small representative training area. The classification methodology based on the integrated use of multiple data produces satisfactory land cover classification. Its transferability considerably reduces both the processing time and the analyst’s efforts.

[1]  Zoltan Szantoi,et al.  Socioeconomic Factors and Urban Tree Cover Policies in a Subtropical Urban Forest , 2012 .

[2]  F. Canters,et al.  Improving Pixel-based VHR Land-cover Classifications of Urban Areas with Post-classification Techniques , 2007 .

[3]  Zoltan Szantoi,et al.  International Journal of Applied Earth Observation and Geoinformation a Tool for Rapid Post-hurricane Urban Tree Debris Estimates Using High Resolution Aerial Imagery , 2022 .

[4]  Tung Fung,et al.  Object‐oriented classification for urban land cover mapping with ASTER imagery , 2007 .

[5]  Diane M. Styers,et al.  Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data , 2011, Remote. Sens..

[6]  Leena Matikainen,et al.  Segment-Based Land Cover Mapping of a Suburban Area - Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points , 2011, Remote. Sens..

[7]  T. Blaschke,et al.  Object‐based land‐cover classification for the Phoenix metropolitan area: optimization vs. transportability , 2008 .

[8]  P. Gong,et al.  Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .

[9]  Ming Zhong,et al.  Object-Based Classification of Urban Areas Using VHR Imagery and Height Points Ancillary Data , 2012, Remote. Sens..

[10]  Qihao Weng,et al.  Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends , 2012 .

[11]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[12]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[13]  A. Troy,et al.  An object‐oriented approach for analysing and characterizing urban landscape at the parcel level , 2008 .

[14]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[15]  Curt H. Davis,et al.  A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..

[16]  Fernando Bação,et al.  Self-organizing Maps as Substitutes for K-Means Clustering , 2005, International Conference on Computational Science.

[17]  Liangpei Zhang,et al.  Building Change Detection From Multitemporal High-Resolution Remotely Sensed Images Based on a Morphological Building Index , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Fei Yuan,et al.  Land‐cover change and environmental impact analysis in the Greater Mankato area of Minnesota using remote sensing and GIS modelling , 2008 .

[19]  Jie Shan,et al.  Object-Based Data Integration and Classification for High-Resolution Coastal Mapping , 2009 .

[20]  Ruiliang Pu,et al.  Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery , 2011 .

[21]  William J. Emery,et al.  A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification , 2009 .

[22]  Chad Hendrix,et al.  A Comparison of Urban Mapping Methods Using High-Resolution Digital Imagery , 2003 .

[23]  Hongsheng Zhang,et al.  Improving the impervious surface estimation with combined use of optical and SAR remote sensing images , 2014 .

[24]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[25]  William J. Emery,et al.  Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[26]  D. Stow,et al.  Object‐based classification of residential land use within Accra, Ghana based on QuickBird satellite data , 2007, International journal of remote sensing.

[27]  M. Antrop.  Why landscapes of the past are important for the future , 2005 .

[28]  Eléonore Wolff,et al.  Urban land cover multi‐level region‐based classification of VHR data by selecting relevant features , 2006 .

[29]  Weifeng Li,et al.  Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery , 2014, Remote. Sens..

[30]  Jixian Zhang.  Multi-source remote sensing data fusion: status and trends , 2010 .

[31]  Martin Herold,et al.  Spectral resolution requirements for mapping urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..

[32]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[33]  May Yuan,et al.  Categorizing natural disaster damage assessment using satellite-based geospatial techniques , 2008 .

[34]  S. Goetz,et al.  IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region , 2003 .