05-05-2016 00:35

This paper presents an automatic remotely sensed system that is designed to classify dust, clouds, water and vegetation features from red sea area. Thus provides the system to make the test and classification process without retraining again. This system can rebuild the architecture of the neural network (NN) according to a linear combination among the number of epochs, the number of neurons, training functions, activation functions, and the number of hidden layers. The proposed system is trained on the features of the provided images using 13 training functions, and is designed to find the best networks that has the ability to have the best classification on data is not included in the training data. This system shows an excellent classification of test data that is collected from the training data. The performances of the best three training functionsare%99.82, %99.64 and %99.28for test data that is not included in the training data. Although, the proposed system was trained on data selected only from one image, this system shows correctly classification of the features in the all images. The designed system can be carried out on remotely sensed images for classifying other features. This system was applied on several sub-images to classify the specified features. The correct performance of classifying the features from the sub-images was calculated by applying the proposed system on some small sections that were selected from contiguous areas contained the features.