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This paper proposes a method for remote sensing based land cover/land use classification of urban areas. The method consists of the following four main stages: feature extraction, feature coding, feature selection and classification. In the feature extraction stage, statistical, textural and Gabor features are computed within local image windows of different sizes and orientations to provide a wide variety of potential features for the classification. Then the features are encoded and normalized by means of the Self-Organizing Map algorithm. For feature selection a classification and regression trees (CART) based algorithm was developed to select a subset of features for each class within the classification scheme at hand.

Additional information

Authors: HEIKKONEN J, Laboratory of Computational Engineering, Helsinki University of Technology (FI);VARFIS A, JRC Ispra (IT)
Bibliographic Reference: Article: International Journal of Pattern Recognition and Artificial Intelligence (1998)
Record Number: 199810423 / Last updated on: 1998-03-31
Original language: en
Available languages: en
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