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Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing dataAuthor(s): Weiqi Zhou; Austin Troy; Morgan Grove
Source: Sensors. 8: 1613-1636.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionAccurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the Gwynns Falls watershed from 1999 to 2004. The Gwynns Falls watershed includes portions of Baltimore City and Baltimore County, Maryland, USA. An object-based approach was first applied to implement the land cover classification separately for each of the two years. The overall accuracies of the classification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. Following the classification, we conducted a comparison of two different land cover change detection methods: traditional (i.e., pixel-based) post-classification comparison and object-based post-classification comparison. The results from our analyses indicated that an object-based approach provides a better means for change detection than a pixel based method because it provides an effective way to incorporate spatial information and expert knowledge into the change detection process. The overall accuracy of the change map produced by the object-based method was 90.0%, with Kappa statistic of 0.854, whereas the overall accuracy and Kappa statistic of that by the pixel-based method were 81.3% and 0.712, respectively.
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CitationZhou, Weiqi; Troy, Austin; Grove, Morgan. 2008. Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors. 8: 1613-1636.
Keywordsobject-based image analysis, post-classification change detection, high-spatial resolution image, urban landscape, Baltimore, LTER
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