Skip to Main Content
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
PDF: View PDF (801 KB)
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.
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, firstname.lastname@example.org if you notice any errors which make this publication unusable.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
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
- Using the spatial and spectral precision of satellite imagery to predict wildlife occurrence patterns.
- Urban cover mapping using digital, high-resolution aerial imagery
- Object-oriented classification of forest structure from light detection and ranging data for stand mapping
XML: View XML