Self-organizing Neural Networks in Feature Extraction

Due to large datavolumes when remote sensing or other kind of images are used, there is need for methods to decrease the volume of data. Methods for decreasing the feature dimension, in other words number of channels, are called feature selection and feature extraction. In the feature selection, important channels are selected using some search technique and these channels are used for current problem. In the feature extraction, original channels are transformed to lower dimensional channels and these are used for problem. Widely used feature extraction method is Karhunen-Löwe transformation. In this study Karhunen-Löwe transformation is compared to transformationmade by Kohonen self-organizing feature map. Tests made using artificially generated datasets show that the differences between compared methods are small.