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• W2133705055 abstract "Photo interpretation by human experts has been the major data source for urban planning and monitoring applications. However, images collected from a space based sensor which combines reasonably good spectral and spatial resolution, could provide an useful tool for automatic monitoring of urban area changes. With this aim, in this paper, a data fusion technique, based on a RGB - HIS transformation, has been adopted for combining high spatial resolution panchromatic satellite with multi-spectral low resolution IKONOS II images. Moreover , textural information, which characterizes urban area, has been extracted with the use of a filter for the edge extraction. An MLP classifier has been trained to produce a labelled image with great accuracy in test even if a limited training set has been used. For a photo interpreter, the results reveal a good feasibility of the classified image for monitoring the presence of changes in urban areas, useful for a cartographic updating. different years. The percentage of correctly classified objects was 89%, whereas the percentage of correct object changes was equal to 83 %, with a false alarm rate of 5%. In (3) a parallelepiped supervised classification algorithm is used to obtain a land cover map characterized by seven classes from two pan-sharpened multi-spectral images at 1m resolution. Overall accuracy values of 75-83% are obtained on test data by using a PS-MS / RGB band composition and an RGB / NIR band composition, respectively. In (4) a neuro-fuzzy classifier based on a set of IF- Then-rules is compared with a Back Propagation neural network and a Maximum Likelihood (ML) classifier to produce a land cover map from IKONOS data on the Korean peninsula, characterized by mixed composition areas. The neuro-fuzzy classifier was more accurate than other classifiers on mixed composition areas, whereas the maximum likelihood performed better on areas such as roads. As input features, the authors considered the four multi-spectral band at low spatial resolution. The usefulness of IKONOS imagery for classification of urban and suburban scenes was also investigated in (5), where a hierarchical fuzzy classification techniques is proposed to improve the classification accuracy obtained by a ML traditional classifier when using only the MS bands of IKONOS data. The ML performance of about 81% was in fact increased to 88%, by using as input to the hierarchical classifier some textural features, extracted from the PAN band, beside the ML classified image. The objective of this work was twofold. First, to validate the feasibility of the RGB-HSI data fusion approach to assimilate the information derived from IKONOS high spatial / low spectral resolution data and the spectral information from low spatial / high spectral resolution. Second, to fully exploit the contextual information of IKONOS PAN data for the automatic classification of urban settlements. Two areas in Southern Italy, characterized by a different typical landscape, were selected for the study. The first is a country area with little villages, tourist facilities and isolated holiday houses" @default.
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• W2133705055 date "2004-07-08" @default.
• W2133705055 modified "2023-09-26" @default.
• W2133705055 title "Extraction of urban settlements by an automatic approach on high resolution remote sensed data" @default.
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• W2133705055 doi "https://doi.org/10.1109/igarss.2003.1294311" @default.
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