Comparing Spectral and Object Based Approaches for Classification and Transportation Feature Extraction from High Resolution Multispectral Imagery

An increasing need exists to update older transportation infrastructure, land use/land cover, environmental impact assessment and road network layer maps. Planning and development rely on accurate data layers for new construction and changes in existing routes. Recent developments in commercial satellite products have resulted in a broader range of high quality image data, enabling detailed analysis. This information differs in its characteristic features (e.g. spatial resolution and geolocational accuracy) as well as utility for particular tasks. Transportation features have historically been difficult to accurately identify and structure into coherent networks; prior analyses have demonstrated problems in locating smaller features. Roadways in urban environments are often partly obscured by proximity to land cover or impervious objects. Recent research has focused on object-based methods for classification and different segmentation techniques key to this approach. Software packages such as eCognition have shown encouraging results in assessing spatial and spectral patterns at varied scales in intelligent classification of aerial and satellite imagery. In this study we examine 2.44m QuickBird and 4m Ikonos multispectral imagery for a 7.5' quad (Dead Tiger Creek) near the Mississippi Gulf Coast. Both spectral and object-based approaches are implemented for pre-classification, after which road features are extracted using various techniques. Results are compared based on a raster completeness model developed. Challenges include intricate networks of smaller roads in residential zones and regions of tall/dense tree cover. Observations for these sites will assist in developing a larger-scale analysis plan for the CSX railroad corridor relocation project.