Improvements to Expectation-Maximization Approach for Unsupervised Classification of Remote Sensing Data

Thales Sehn KortingLuciano Vieira DutraLeila Maria Garcia FonsecaGuaraci ErthalFelipe Castro da Silva

In statistical pattern recognition, mixture models allow a formal approach to unsupervised learning. This work aims to present a modification of the Expectation-Maximization clustering method applied to remote sensing images. The stability of its convergence has been increased by supplying the results of the well-known K-Means algorithm, as seed points. Hence, the accuracy has been improved by applying cluster validity measures to each configuration, varying the initial number of clusters. High-resolution urban scenes has been tested, and we show a comparison to supervised classification results. Performance tests were also realized, showing the improvements of our proposal, in comparison to the original one.

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