View source for Publications/cavallaro.16.igarss

{{Publication
| published = true
| date = 2016-04-12
| authors = Gabriele Cavallaro, Mauro Dalla Mura, Edwin Carlinet, Thierry Géraud, Nicola Falco, Jón Atli Benediktsson
| title = Region-Based Classification of Remote Sensing Images with the Morphological Tree of Shapes
| booktitle = Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
| pages = 5087 to 5090
| address = Beijing, China
| abstract = Satellite image classification is a key task used in remote sensing for the automatic interpretation of a large amount of information. Today there exist many types of classification algorithms using advanced image processing methods enhancing the classification accuracy rate. One of the best state-of-the-art methods which improves significantly the classification of complex scenes relies on Self-Dual Attribute Profiles (SDAPs). In this approachthe underlying representation of an image is the Tree of Shapes, which encodes the inclusion of connected components of the image. The SDAP computes for each pixel a vector of attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Instead of performing a pixel-wise classification on features extracted from the Tree of Shapes, it is proposed to directly classify its nodes. Extending a specific interactive segmentation algorithm enables it to deal with the multi-class classification problem. The method does not involve any statistical learning and it is based entirely on morphological information related to the tree. Consequently, a very simple and effective region-based classifier relying on basic attributes is presented.
| lrdekeywords = Image
| lrdepaper = https://www.lrde.epita.fr/dload/papers/cavallaro.16.igarss.pdf
| lrdenewsdate = 2016-04-12
| type = inproceedings
| id = cavallaro.16.igarss
| bibtex = 
@InProceedings<nowiki>{</nowiki>	  cavallaro.16.igarss,
  author	= <nowiki>{</nowiki>Gabriele Cavallaro and Mauro <nowiki>{</nowiki>Dalla Mura<nowiki>}</nowiki> and Edwin
		  Carlinet and Thierry G\'eraud and Nicola Falco and J\'on
		  Atli Benediktsson<nowiki>}</nowiki>,
  title		= <nowiki>{</nowiki>Region-Based Classification of Remote Sensing Images with
		  the Morphological Tree of Shapes<nowiki>}</nowiki>,
  booktitle	= <nowiki>{</nowiki>Proceedings of the IEEE International Geoscience and
		  Remote Sensing Symposium (IGARSS)<nowiki>}</nowiki>,
  year		= <nowiki>{</nowiki>2016<nowiki>}</nowiki>,
  month		= jul,
  pages		= <nowiki>{</nowiki>5087--5090<nowiki>}</nowiki>,
  address	= <nowiki>{</nowiki>Beijing, China<nowiki>}</nowiki>,
  abstract	= <nowiki>{</nowiki>Satellite image classification is a key task used in
		  remote sensing for the automatic interpretation of a large
		  amount of information. Today there exist many types of
		  classification algorithms using advanced image processing
		  methods enhancing the classification accuracy rate. One of
		  the best state-of-the-art methods which improves
		  significantly the classification of complex scenes relies
		  on Self-Dual Attribute Profiles (SDAPs). In this approach,
		  the underlying representation of an image is the Tree of
		  Shapes, which encodes the inclusion of connected components
		  of the image. The SDAP computes for each pixel a vector of
		  attributes providing a local multiscale representation of
		  the information and hence leading to a fine description of
		  the local structures of the image. Instead of performing a
		  pixel-wise classification on features extracted from the
		  Tree of Shapes, it is proposed to directly classify its
		  nodes. Extending a specific interactive segmentation
		  algorithm enables it to deal with the multi-class
		  classification problem. The method does not involve any
		  statistical learning and it is based entirely on
		  morphological information related to the tree.
		  Consequently, a very simple and effective region-based
		  classifier relying on basic attributes is presented.<nowiki>}</nowiki>
<nowiki>}</nowiki>

}}