Urban road extraction from optical remotely sensed imagery

Szu Chi Peng, Hsuan Ren

Research output: Contribution to conferencePaperpeer-review

Abstract

With the development of the city, the road has changed frequently. Road extraction from optical remotely sensed images is an economic and efficient way to obtain and update road networks. This paper presents a method to extract the urban road from optical remotely sensed images. Our proposed method includes the following three steps. First, an unsupervised K-means Clustering classification is applied to classify the images into two categories: road and non-road. Followed by road's homogeneous property to remove noise and improve road extraction accuracy. Finally, the shape features of road is adopted to remove other manmade objects, including buildings, parking lots and other objects which has similar spectral feature to roads. The main road networks are generated after these steps. One application of this algorithm is to construct the road network from satellite images and compare to old city map, to detect the change by urbanization.

Original languageEnglish
StatePublished - 2014
Event35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014 - Nay Pyi Taw, Myanmar
Duration: 27 Oct 201431 Oct 2014

Conference

Conference35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014
Country/TerritoryMyanmar
CityNay Pyi Taw
Period27/10/1431/10/14

Keywords

  • Hyperspectral images
  • K-means clustering
  • Road extraction
  • Shape feature

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