Satellite image classification using Genetic Algorithm trained radial basis function neural network, application to the detection of flooded areas

In this paper, a semi supervised method for classification of satellite images based on Genetic Algorithm (GA) and Radial Basis Function Neural Network (RBFNN) is proposed. Satellite image classification problem has two major concerns to be addressed. The first issue is mixed pixel problem and the second issue is handling large amount of data present in these images. RBFNN function is an efficient network with a large set of tunable parameters. This network is able to generalize the results and is immune to noise. A RBFNN has learning ability and can appropriately react to unseen data. This makes the network a good choice for satellite images. The efficiency of RBFNN is greatly influenced by the learning algorithm and seed point selection. Therefore, in this paper spectral indices are used for seed selection and GA is used to train the network. The proposed method is used to classify the Landsat 8 OLI images of Dongting Lake in South China. The application of this method is shown for detection of flooded area over this region. The performance of the proposed method was analyzed and compared with three existing methods and the error matrix was computed to test the performance of the method. The method yields high producer's accuracy, consumer's accuracy and kappa coefficient value which indicated that the proposed classifier is highly effective and efficient. (C) 2016 Elsevier Inc. All rights reserved.