Summary
Information on land use classes of urban areas is very important for their management and planning. Satellite remote sensing data can provide regular and up to date information on urban areas. Although satellite technology has changed significantly, a robust methodology in exploiting fruitfully satellite imagery in urban areas is under investigation. This paper gives a literature review of image analysis techniques used for the classification of medium and high resolution satellite imagery in urban areas. In addition three kernel based classification techniques to process very high resolution imagery, are considered that integrate texture and spatial context properties for urban land use classes. The Evidence Based Interpretation Algorithm, a backpropagation neural network algorithm and the Kernel Reclassification algorithm. A summary of their use on Indian Remote Sensing (IRS) satellite imagery over the city of Athens is presented. Although some of these techniques return rather satisfactory results, the need for the development of more advanced interpretation methods, which integrate human reasoning in object identification is considered as indispensable, as the spatial resolution of the data is continuously increasing attaining the one of high altitude aerial photography (e.g. IKONOS system).
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© 1999 Springer-Verlag Berlin · Heidelberg
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Kontoes, C.C. (1999). Image Analysis Techniques for Urban Land Use Classification. The Use of Kernel Based Approaches to Process Very High Resolution Satellite Imagery. In: Kanellopoulos, I., Wilkinson, G.G., Moons, T. (eds) Machine Vision and Advanced Image Processing in Remote Sensing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60105-7_11
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DOI: https://doi.org/10.1007/978-3-642-60105-7_11
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