Abstract
In this paper, we propose a method employing neural network to classify remote sensing data. Initially, we select training patterns based on the geographical knowledge and then the neural network is trained until the desired classification is attained. After training the neural network, the selected training patterns are again applied to the network. If the classification is not satisfactory, then the sample is deleted from the original training set and new training set is formed to classify the remote sensing data. Once the training is complete, the remote sensing data is applied to the trained network for classification. The experiments on LANDSAT TM data show that this approach produces excellent classification results which are more realistic and noiseless compared with usual Bayesian approach. Finally, the present neural network approach is also powerful to solve a problem of removal of cloud shadows.
