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14 December 1999 Integration of spatial and spectral information in unsupervised classification for multispectral and hyperspectral data
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Abstract
Unsupervised classification algorithms are techniques to extract information from Remote Sensing imagery based on machine calculation without prior knowledge of labeled samples. Most of current unsupervised algorithms only use the spectral response as information. The clustering algorithms that takes into consideration the spatial information have a trade off between being accurate and time consuming, or being fast and losing relevant details in the spatial mapping. This paper will present an unsupervised classification system developed to extract information from multispectral and hyperspectral data as well, considering the spectral response, hyperdimensional data characteristics, and the spatial context of the pixel that will be classified. This algorithm constructs local spatial neighborhoods in order to measure their degrees of homogeneity. It resembles the supervised version of the ECHO classifier. An advantage of this mechanism is that the mathematical developments to estimate the degrees of homogeneity enable implementations based on statistical pattern recognition. This clustering algorithm is fast and its results have shown superiority in recognizing objects in multispectral and hyperspectral data over other known mechanism.
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Luis O. Jimenez-Rodriguez and Jorge Rivera-Medina "Integration of spatial and spectral information in unsupervised classification for multispectral and hyperspectral data", Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999); https://doi.org/10.1117/12.373270
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