NDLI: An Automatic Bridge Detection Technique for Multispectral Images

Content Provider	IEEE Xplore Digital Library
Author	Chaudhuri, D. Samal, A.
Copyright Year	1980
Abstract	Extraction of features from images has been a goal of researchers since the early days of remote sensing. While significant progress has been made in several applications, much remains to be done in the area of accurate identification of high-level features such as buildings and roads. This paper presents an approach for detecting bridges over water bodies from multispectral imagery. The multispectral image is first classified into eight land-cover types using a majority-must-be-granted logic based on the multiseed supervised classification technique. The classified image is then categorized into a trilevel image: water, concrete, and background. Bridges are then recognized in this trilevel image by using a knowledge-based approach that exploits the spatial arrangement of bridges and their surroundings using a five-step approach. A river extraction module identifies the rivers using a recursive scanning technique and geometric constraints. Using a neighborhood operator and the knowledge of the spatial dimensions of a typical bridge, we identify the possible bridge pixels. These potential bridge pixels are then grouped into possible bridge segments based on their connectivity and geometric properties. Finally, these bridge segments are verified on the basis of directional water index along different directions and their connectivity with the road segments. The approach proposed in this paper has been implemented and tested with images from the IRS-1C/1-D satellite that has a spatial resolution of 23.5 23.5 m. The results show that this approach is both efficient and effective in extracting bridges.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	2720
Ending Page	2727
Page Count	8
File Size	521000
File Format	PDF
ISSN	01962892
Volume Number	46
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bridges Multispectral imaging Image segmentation Roads Rivers Feature extraction Remote sensing Logic Concrete Image recognition remote sensing Classification feature extraction graph theory multiseed clustering pattern recognition
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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