Extraction of bridges over water from IKONOS panchromatic data
Source: International Journal of Remote Sensing, Volume 28, Number 16, 2007 , pp. 3633-3648(16)
Publisher: Taylor and Francis Ltd
Abstract:Compared to remote sensing images of medium or low spatial resolution, high-resolution remote sensing images can provide observation data containing more detailed information for georesearch. Accordingly, an important issue for current computer and geoscience experts is to develop useful methods or technology to extract information from these high-resolution satellite images. As part of a series of research into object extraction, this paper focuses mainly on the extraction of bridges over water from high-resolution panchromatic satellite images. Since bridges over water are obviously adjacent to water in remote sensing images, this paper proposes a practical knowledge-based bridge extraction method for remote sensing images of high spatial resolution. The steps involved are: water extraction based on Gauss Markov Random Field (GMRF)-Support Vector Machine (SVM) classification methods which use a SVM to classify the image based on textural features expressed by a GMRF; image thinning and removal of fragmented lines; main trunk detection by width; vectorization; and feature expression. Finally, tests are described for two pieces of panchromatic IKONOS satellite images with a 1 m resolution. The experimental results show that the proposed method is suitable for images with a single-peak histogram (contrast between water and land is sharp) or a multi-peak histogram (greyscale value of water is close to that of land).
Document Type: Research Article
Affiliations: 1: NCG, Institute of Remote Sensing Application, CAS, Beijing 100101, China 2: School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China 3: School of Information Engineering, Beijing Institute of Technology, Beijing 100081, China 4: LREIS, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China
Publication date: 2007