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Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery |
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| Affiliation: | 1. Department of Electronics Engineering, G.H. Raisoni College of Engineering, Nagpur, India;2. S.B. Jain Institute of Technology, Management & Research, Nagpur, India;1. Intelligent Computing Group, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia;2. Intelligent Systems Laboratory, University of Bristol, Bristol BS8 1UB, United Kingdom;1. National Research Council (CNR), Institute of Cognitive Sciences and Technologies, Via Gaifami 18, 95028 Catania, Italy;2. School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex UB8 3PH, United Kingdom;3. Department of Computing Engineering, University of La Laguna, 38271 Santa Cruz de Tenerife, Spain;1. School of Business, Central South University, Changsha 410083, China;2. School of Management, Qingdao Technological University, Qingdao 266520, China;3. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China |
| Abstract: | The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one. |
| Keywords: | Land cover classification Multiobjective optimization (MOO) Neural network Particle swarm optimization Remote sensing imagery |
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