Abstract
Unsupervised classification of remote sensing images is typically based on a mixture model, where the distribution of the entire data is modeled as a weighted sum of the class-component densities (Duda et al. 2000). When the class-component densities are assumed to be multivariate Gaussian, the mixture model is known as the Gaussian mixture model. The K-means and the ISODATA algorithms that are widely used in remote sensing are based on the Gaussian mixture model. These Gaussian mixture model based classification algorithms often perform unsatisfactorily. This stems from the Gaussian distribution assumption for the class-component densities. Gaussianity is only an assumption, rather than a demonstrable property of natural spectral classes, and has been widely accepted due to its analytical tractability and mathematical simplicity. However, if a class happens to be multimodal, it is no longer appropriate to model the class with a multivariate Gaussian distribution. Therefore, the use of the Gaussian mixture model in such cases may lead to unsatisfactory performance.
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Shah, C.A. (2004). Hyperspectral Classification Using ICA Based Mixture Model. In: Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05605-9_10
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DOI: https://doi.org/10.1007/978-3-662-05605-9_10
Publisher Name: Springer, Berlin, Heidelberg
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