LULC classification of landsat-7 ETM+ image from rugged terrain using TC, CA and SOFM neural network

In this paper, CA transformation was introduced, instead of PCA transformation, and integrated with Kohonen Self Organization Feature Map (SOFM) ANN for Landsat ETM+ data classification. The methodology mainly included three steps as follows: First, the non-Lambertian Minnaert topographic correction algorithm was used to remove the topographic effects of the ETM+ image after atmospheric correction from the test site. Second, the ETM+ image after topographic correction was transformed using the CA algorithm. Then, the SOFM ANN analysis was applied to the CA first two components selected to perform Land Use/Land Cover (LULC) classification. And the results suggested that the proposed approach is more effective for LULC classification of ETM+ image than the approach based on PCA for the test site, and also showed that topographic correction is necessary for Landsat ETM+ images from rugged terrain and helpful to improve the classification accuracy.