Summary
Modern optical Very High Resolution (VHR) sensors boost the resolution of satellite imagery up to 1 pixel/m at nadir and higher. It is believed that the appearance of recognisable (man-made) structures and texture will drastically increase the number of data products and therefore also the number of end users. The potential role - and typical problems - of a selected set of image analysis tools for the pre-processing of VHR products is discussed.
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References
M. Antonini, M. Barlaud, P. Mathieu and I. Daubechies, “Image coding using wavelet transform”, IEEE Transactions on Image Processing, vol. 1, pp. 205–220, 1993.
P. Boekaerts, E. Nyssen and J. Cornelis, “Autoadaptive monospectral cloud identification in METEOSAT satellite images”, European Symposium on Satellite Remote Sensing, Conference on Image and Signal Processing for Remote Sensing IL, EOS/SPIE vol. 2579, pp. 259–271, 1995.
P. Boekaerts, E. Nyssen and J. Cornelis, “Autoadaptive scene identification in multispectral satellite data”, 5th Symposium on Remote Sensing: A Valuable Source of Information, NATO AGARD/SPP CP-582, pp. 211–218, October 1996.
P. Boekaerts, E. Nyssen and J. Cornelis, “A comparative study of topological feature maps versus conventional clustering for (multi-spectral) scene identification in METEOSAT imagery”, in Neurocomputation in remote sensing image analysisi, I. Kanellopoulos, G.G. Wilkinson, F. Roli and J. Austin eds., pp. 231–241, Springer Verlag, Berlin, 1997.
T. Chang and C.-C.J. Kuo, “Texture analysis and classification with treestructured wavelet transform”, IEEE Transactions on Image Processing, vol. 2, no. 4, pp. 429–441, 1993.
FUSETUTOR: Multi-media Tutorial on Remote Sensing Image and Data Fusion, Western European Satellite Centre, Madrid.
S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation”, IEEE Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989.
M. Unser, “Texture classification and segmentation using wavelet frames”, IEEE Transactions on Image Processing, vol. 4, no. 11, pp. 1549–1560, 1995.
Y.M. Zhu and R. Goutte, “Analysis and comparison of space/spatial frequency and multiscale methods for feature segmentation”, Optical Engineering, vol. 34, no. 1, pp. 269–282, 1995.
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© 1999 Springer-Verlag Berlin · Heidelberg
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Boekaerts, P., Christopoulos, V., Munteanu, A., Cornelis, J. (1999). Potential Role of Very High Resolution Optical Satellite Image Pre-Processing for Product Extraction. 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_9
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DOI: https://doi.org/10.1007/978-3-642-60105-7_9
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