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16 December 1992 Multispectral image classification using a mixture density model
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Abstract
This paper describes a multispectral image classification technique. This technique involves two steps. First, we describe the underlying distribution of the pixel intensity vectors for the entire scene as a mixture of multivariate Gaussian distributions. We then use this mixture decomposition and a small number of labeled pixels to estimate the proportion of a mixture component that is comprised of a certain class, which enables us to use a Bayes-type decision rule to classify each pixel in the scene. Results of applying this technique to three-band SPOT data are presented. Comparisons with results obtained from a maximum likelihood classifier are also presented.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sylvia S. Shen and Brian D. Horblit "Multispectral image classification using a mixture density model", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130835
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