A Comparison of Object-Based with Pixel-Based Land Cover Change Detection in the Baltimore Metropolitan Area using Multitemporal High Resolution Remote Sensing Data

This paper presents the methods and results of two post-classification change detection approaches, using multitemporal high-spatial resolution Emerge aerial imagery in the Gwynns Falls watershed, which includes portions of Baltimore City and Baltimore County, Maryland, USA. The results indicated that the object-based approach provides a better means for change detection than a traditional 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.