論文

2017年12月1日

Tree-based supervised feature extraction method based on self-dual attribute profiles

International Geoscience and Remote Sensing Symposium (IGARSS)
  • Gabriele Cavallaro
  • ,
  • Mauro Dalla Mura
  • ,
  • Morris Riedel
  • ,
  • Jon Atli Benediktsson

2017-July
開始ページ
775
終了ページ
778
記述言語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/IGARSS.2017.8127067

© 2017 IEEE. Self-Dual Attribute Profiles (SDAPs) have proven to be an effective method for extracting spatial features able to improve scene classification of remote sensing images with very high spatial resolution. An SDAP is a multilevel decomposition of an image obtained with a sequence of transformations performed by attribute filters over the Tree of Shapes (ToS). One of the main issues with this technique is the identification of the filter thresholds generating a SDAP composed of features that should be relevant for the classification problem. This paper proposes a tree-based supervised feature extraction strategy, which is based on Fisher's linear discriminant analysis relying on the available class information. The exploitation of the ToS structure in the threshold selection procedure allows one to avoid any prior full image filtering, as in other related techniques. Furthermore, the ToS automates and optimizes the whole process by decreasing the computational time and overcoming the conventional selection procedure based on trial and error attempts. The proposed automatic spatial feature extraction technique has been tested in the classification of a very high resolution image proving its effectiveness with respect to a conventional selection strategy.

リンク情報
DOI
https://doi.org/10.1109/IGARSS.2017.8127067
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85041856818&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85041856818&origin=inward

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