View source for Publications/geraud.04.jasp

{{Publication
| published = true
| date = 2004-09-05
| authors = Thierry Géraud, Jean-Baptiste Mouret
| title = Fast road network extraction in satellite images using mathematical morphology and Markov random fields
| journal = EURASIP Journal on Applied Signal Processing
| number = 16
| volume = 2004
| pages = 2503–2514
| note = Special issue on Nonlinear Signal and Image Processing - Part II
| lrdeprojects = Olena
| abstract = This paper presents a fast method for road network extraction in satellite images. It can be seen as a transposition of the segmentation scheme "watershed transform + region adjacency graph + Markov random fields" to the extraction of curvilinear objects. Many road extractors can be found in the literature which are composed of two stages. The first one acts like a filter that can decide from a local analysis, at every image point, if there is a road or not. The second stage aims at obtaining the road network structure. In the method we propose, we rely on a "potential" image, that isunstructured image data that can be derived from any road extractor filter. In such a potential image, the value assigned to a point is a measure of its likelihood to be located in the middle of a road. A filtering step applied on the potential image relies on the area closing operator followed by the watershed transform to obtain a connected line which encloses the road network. Then a graph describing adjacency relationships between watershed lines is built. Defining Markov random fields upon this graphassociated with an energetic model of road networks, leads to the expression of road network extraction as a global energy minimization problem. This method can easily be adapted to other image processing fields where the recognition of curvilinear structures is involved.
| lrdekeywords = Image
| lrdenewsdate = 2004-09-05
| type = article
| id = geraud.04.jasp
| identifier = doi:http://doi.acm.org/10.1155/S1110865704409093
| bibtex = 
@Article<nowiki>{</nowiki>	  geraud.04.jasp,
  author	= <nowiki>{</nowiki>Thierry G\'eraud and Jean-Baptiste Mouret<nowiki>}</nowiki>,
  title		= <nowiki>{</nowiki>Fast road network extraction in satellite images using
		  mathematical morphology and <nowiki>{</nowiki>M<nowiki>}</nowiki>arkov random fields<nowiki>}</nowiki>,
  journal	= <nowiki>{</nowiki>EURASIP Journal on Applied Signal Processing<nowiki>}</nowiki>,
  year		= 2004,
  number	= 16,
  volume	= 2004,
  pages		= <nowiki>{</nowiki>2503--2514<nowiki>}</nowiki>,
  month		= nov,
  note		= <nowiki>{</nowiki>Special issue on Nonlinear Signal and Image Processing -
		  Part II<nowiki>}</nowiki>,
  doi		= <nowiki>{</nowiki>http://doi.acm.org/10.1155/S1110865704409093<nowiki>}</nowiki>,
  abstract	= <nowiki>{</nowiki>This paper presents a fast method for road network
		  extraction in satellite images. It can be seen as a
		  transposition of the segmentation scheme "watershed
		  transform + region adjacency graph + Markov random fields"
		  to the extraction of curvilinear objects. Many road
		  extractors can be found in the literature which are
		  composed of two stages. The first one acts like a filter
		  that can decide from a local analysis, at every image
		  point, if there is a road or not. The second stage aims at
		  obtaining the road network structure. In the method we
		  propose, we rely on a "potential" image, that is,
		  unstructured image data that can be derived from any road
		  extractor filter. In such a potential image, the value
		  assigned to a point is a measure of its likelihood to be
		  located in the middle of a road. A filtering step applied
		  on the potential image relies on the area closing operator
		  followed by the watershed transform to obtain a connected
		  line which encloses the road network. Then a graph
		  describing adjacency relationships between watershed lines
		  is built. Defining Markov random fields upon this graph,
		  associated with an energetic model of road networks, leads
		  to the expression of road network extraction as a global
		  energy minimization problem. This method can easily be
		  adapted to other image processing fields where the
		  recognition of curvilinear structures is involved.<nowiki>}</nowiki>
<nowiki>}</nowiki>

}}