Abstract
This paper considers the task of unsupervised classification of hyperspectral data within a Gaussian mixture modeling framework. Different from the traditional clustering techniques that face serious conceptual problems in complex situations, the Gaussian mixture modeling approach provides a means of solving both simple and complex classification tasks as well as a way to substantiate results. Despite its theoretical advantages, in practice, the approach is rarely used for remote sensing because of objective difficulties in its implementation, especially for hyperspectral data. To show how these difficulties can be successfully overcome we present here a method for Gaussian mixture‐based unsupervised classification of hyperspectral data. The method contains a new approach for spectral feature extraction that reduces the data dimensionality while quantitatively guaranteeing the safety of the relevant information. The unsupervised learning procedure we propose is aimed at finding the number of classes that delivers a local maximum to the user's confidence in subsequent labeling the pixels. The class parameters are determined via the Expectation‐Maximization algorithm, starting from the initial values selected systematically by the learning procedure. Decisions on class membership for the pixels are proposed to be made on the basis of the lower confidence bounds of the posterior probability estimates. This allows users to provide a probabilistic guarantee of the results for each separate pixel. The potential capabilities of the method are illustrated by its application to the GER 63 channel scanner hyperspectral data of the Naan area, central Israel. A comparison of the method with one of the traditional techniques we have performed confirms the advantages of the presented method, even though just a simplified version of the method has been employed.
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