Classification of remote sensing imagery with high spatial resolution

Classification of high resolution remote sensing data from urban areas is investigated. The main challenge in classification of high resolution remote sensing image data is to involve local spatial information in the classification process. Here, a method based on mathematical morphology is used in order to preprocess the image data using spatial operators. The approach is based on building a morphological profile by a composition of geodesic opening and closing operations of different sizes. In the paper, the classification is performed on two data sets from urban areas; one panchromatic and one hyperspectral. These data sets have different characteriscs and need different treatments by the morphological approach. The approach can directly be applied on the panchromatic data. However, some feature extraction needs to be done on the hyperspectral data before the approach can be applied. Both principal and independent components are considered here for such feature extraction. A neural network approach is used for the classification of the morphological profiles and its performance in terms of accuracies is compared to the classification of a fuzzy possibilistic approach in the case of the panchromatic data and the conventional maximum likelhood method based on the Gaussian assumption in the case of the case of hyperspectral data. Also, different types of feature extraction methods are considered in the classification process.