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21 November 1995 Texture segmentation in remote sensing images by means of packet wavelets and fuzzy clustering
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One of the most difficult and important problems encountered in the automatic digitizing of graphical topographic maps is the identification of different kinds of features. Textures are an important spatial feature useful for identifying objects or regions of interest in a remote sensing image. This work presents a wavelet based algorithm combined with a fuzzy C-means classifier. A single image is preprocessed by a wavelet packed algorithm and divided in subimages, different representation of the same scene. The development of this transform is motivated by the observation that a large class of natural textures can be modeled as a quasi- periodic signal, whose dominant frequencies are located in the middle frequency channels. The subband images are then processed by an envelope signal estimation in order to provide a method for features extraction: different textures have different 'energy' in the detail subband; this energy can be seen as a magnitude of oscillation of wavelet coefficients for each subband. The image can now be seen as a multiband representation of the same scene and this can be considered a multi-dimensional data clustering problem. Fuzzy C-means algorithm is so applied to the image as to have a very efficient fuzzy segmentation of the different textures.
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A. Mecocci, Paolo Gamba, Andrea Marazzi, and Mauro Barni "Texture segmentation in remote sensing images by means of packet wavelets and fuzzy clustering", Proc. SPIE 2584, Synthetic Aperture Radar and Passive Microwave Sensing, (21 November 1995);

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