Abstract
This thesis addresses the topic of semi-automated extraction of urban road networks from high-resolution satellite imagery. Research on this topic is mainly motivated by the use geographic information systems in transportation (GIS-T), and the need for reliable data acquisition methods and to update GIS-T databases. To this end, 1-m spatial resolution IKONOS imagery provides a new data source to collect the spatial models of citywide road networks. In this thesis, a novel methodology of a semi-automated road extraction using high-resolution satellite imagery over urban areas is developed. The main objective of this research is to extract urban road networks from a single IKONOS image. To detect the road features from a highly complex scene, a multiscale analysis of the optimal image was performed. To extract roads and their networks, the knowledge of road geometry is exploited in an interactive environment. The key advantage of the developed method is the full employment of a human and a computer's abilities for fast and precise road extraction from high-resolution satellite imagery. The results show that the presented method enables reliable road extraction over urban areas. The potential applications exemplified in case studies indicate that the high-resolution satellite imagery offers an efficient and precise source for geographic and transportation databases. Based on this research, the limitations and future work for the prototype system are discussed.
Highlights
1.1 MotivationFrom a practical point of view, research on automatic road extraction in urban areas is mainly motivated by the use of geographic information systems in transportation (GIS-T) and the need for data acquisition and update for GIS-T databases
By employing change detection technique provided by ERDAS Imagine 8.5, the difference between the Principal components analysis (PCA) image and Decorrelation stretch (DS)-PCA image can be visually examined
The green areas are where the digital numbers (DN) in DSPCA image is larger than the DN in PCA image; the red areas are where the DN in DS-PCA image is smaller than the DN in PCA image; black areas are of no change
Summary
From a practical point of view, research on automatic road extraction in urban areas is mainly motivated by the use of geographic information systems in transportation (GIS-T) and the need for data acquisition and update for GIS-T databases. The new generation o f high-resolution imaging satellites (e.g., IKONOS, QuickBird) have presented more details and precision on urban man-made features (e.g., roads, buildings). Many existing approaches, working on highresolution satellite imagery, are based on the algorithms developed for lower resolution satellite imagery following a downgrading preprocessing to reduce the image spatial resolution. This compromises the accuracy of the results and the full exploitation o f high-resolution data. Extracting roads and their networks from high-resolution satellite imagery and taking advantage of most data spectra should be a new research topic in remote sensing
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