Aerial image classification using fuzzy miner

Abstract

Aerial image classification is a method to classify and identify the objects on digital maps. Color, edge, shape, and texture have been extracted in order to classify objects on the aerial images. These feature attributes can be obtained directly from aerial images. However the complexity of data and number of rule based may be over information, which it can be reduced by the data mining techniques. In this research, we focus on the use of Fuzzy Logic for pattern classification. The attribute classifications have followed by the design and the implementation of its corresponding tool (Fuzzy Miner). Finally, the context of Fuzzy Miner is identified and to classify for its improvement are formulated. Extensive tests are performed to demonstrate the performance of Fuzzy Miner and compared with a performance Fuzzy C-Mean classifier. The results showed that, Fuzzy Miner has the best outcomes while Fuzzy C-Mean has the second rank outcomes.