18 October 2005 Classification of remote sensing imagery with high spatial resolution
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Classification of high resolution remote sensing data from urban areas is investigated. The main challenge with the classification of high resolution remote sensing image data is that spatial information is extremely important in the classification. Therefore, classification methods for such data need to take that into account. Here, a method based on mathematical morphology is used in order to preprocess the image data. 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 of is performed on two data sets from urban areas; one panchromatic and one hyperspectral. These data sets have different characteristcs and need different treatments by the morphological approach. The approach can be directly applied on the panchromatic data. However, some feature extraction needs to be done on the hyperspectral data before the approach is applied. Both principal and independent components are considered here for that purpose. 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 pancrhomatic data and the conventional maximum likelhood method based on the Gaussian assumption in the case of the case of hyperspectral. Different types of feature extraction methods are considered in the classification process.
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Mathieu Fauvel, Mathieu Fauvel, Jon Aevar Palmason, Jon Aevar Palmason, Jon Atli Atli Benediktsson, Jon Atli Atli Benediktsson, Jocelyn Chanussot, Jocelyn Chanussot, Johannes R. Sveinsson, Johannes R. Sveinsson, } "Classification of remote sensing imagery with high spatial resolution", Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 598201 (18 October 2005); doi: 10.1117/12.637224; https://doi.org/10.1117/12.637224

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