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Proceedings Paper

Partially supervised hierarchical clustering of SAR and multispectral imagery for urban areas monitoring
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Paper Abstract

In some key operational domains, users are not specially interested in obtaining an exhaustive map with all the thematic classes present in an area of interest, but rather in identifying accurately a single class of interest. In this paper, we present a novel partially supervised classification technique that faces this interesting practical and methodological problem. We have adopted a two-stage classification scheme based on an unsupervised approach, which allows us to introduce supervised information about the class of interest without an additional sample labeling. The first stage of the process consists in an initial clustering of the image using the Self-Organizing Map algorithm. The second stage consists in a partially supervised hierarchical joint of clusters. We modify the employed criterion of similarity by introducing fuzzy membership functions that make use of the supervised information. The method is tested on urban monitoring, where the objective is to produce an automatic classification of 'Urban/Non-Urban' by using optical and radar data (Landsat TM and 35-days interferometric pairs of ERS2 SAR). We compare classification accuracy of the proposed method to its parametric version, which uses the Expectation-Maximization algorithm. The good performance confirms the validity of the proposed approach: 90% classification accuracy using supervised information only in the coherence map.

Paper Details

Date Published: 10 November 2004
PDF: 12 pages
Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); doi: 10.1117/12.565276
Show Author Affiliations
Luis Gomez-Chova, Univ. de Valencia (Spain)
Diego Fernandez-Prieto, European Space Agency (Italy)
Javier Calpe, Univ. de Valencia (Spain)
Emilio Soria, Univ. de Valencia (Spain)
Joan Vila, Univ. de Valencia (Spain)
Gustavo Camps-Valls, Univ. de Valencia (Spain)


Published in SPIE Proceedings Vol. 5573:
Image and Signal Processing for Remote Sensing X
Lorenzo Bruzzone, Editor(s)

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