Land Cover/Land Use Classification of Urban Areas

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 CART (Classification and Regression Trees) based algorithm was developed to select a subset of features for each class within the classification scheme at hand. The selected subset of features is not attached to any specific classifier. Any classifier capable of representing possible skewed and multi-modal feature distributions can be employed, such as multi-layer perceptron (MLP) or k-nearest neighbor (k-NN). The paper reports experiments in land cover/land use classification with the Landsat TM and ERS-1 SAR images gathered over the city of Lisbon to show the potentials of the proposed method.