An automatic bridge detection technique for multispectral images

D. Chaudhuri, Ashok Samal

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

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.

Original languageEnglish (US)
Article number4579753
Pages (from-to)2720-2727
Number of pages8
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume46
Issue number9
DOIs
StatePublished - Sep 2008

Keywords

  • Classification
  • Feature extraction
  • Graph theory
  • Multiseed clustering
  • Pattern recognition
  • Remote sensing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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