Integrating artificial neural network and classical methods for unsupervised classification of optical remote sensing data

Abstract

A novel system named unsupervised multiple classifier system (UMCS) for unsupervised classification of optical remote sensing data is presented. The system is based on integrating two or more individual classifiers. A new dynamic selection-based method is developed for integrating the decisions of the individual classifiers. It is based on competition distance arranged in a table named class-distance map (CDM) associated to each individual classifier. These maps are derived from the class-to-class-distance measures which represent the distances between each class and the remaining classes for each individual classifier. Three individual classifiers are used for the development of the system, K-means and K-medians clustering of the classical approach and Kohonen network of the artificial neural network approach. The system is applied to ETM + images of an area North to Mosul dam in northern part of Iraq. To show the significance of increasing the number of individual classifiers, the application covered three modes, UMCS, UMCS#, and UMCS*. In UMCS, K-means and Kohonen are used as individual classifiers. In UMCS#, K-medians and Kohonen are used as individual classifiers. In UMCS*, K-means, K-medians and Kohonen are used as individual classifiers. The performance of the system for the three modes is evaluated by comparing the outputs of individual classifiers to the outputs of UMCSs using test data extracted by visual interpretation of color composite images. The evaluation has shown that the performance of the system with all three modes outrages the performance of the individual classifiers. However, the improvement in the class and average accuracy for UMCS* was significant compared to the improvements made by UMCS, and UMCS#. For UMCS*, the accuracy of all classes were improved over the accuracy achieved by each of the individual classifiers and the average improvements reached (4.27, 3.70, and 6.41%) over the average accuracy achieved by K-means, K-medians and Kohonen respectively. These improvements correspond to areas of 3.37, 2.92 and 5.1 km2 respectively. While the average improvements achieved by UMCS and UMCS#, respectively, compared to their individual classifiers were (0.77 and 2.79%) and (0.829 and 2.92%) which correspond to (0.61 and 2.2 km2) and (0.65 and 2.3 km2) respectively. © 2012 Tahir.