Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification
Vignesh Janarthanan1, A.Viswanathan2, M. Umamaheswari3
1Vignesh Janarthanan, Associate Professor, Department of Computer Science and Engineering, Malla Reddy Institute of Technology & Science, Hyderabad.
2A.Viswanathan,Associate Professor Department of Computer Science and Engineering, Malla Reddy Institute of Technology & Science, Hyderabad.
3M. Umamaheswari, Assistant Professor Department of Computer Science and Engineering, K.S.R. College of Engineering, Tiruchengode.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1630-1634 | Volume-8 Issue-4, November 2019. | Retrieval Number: C4642098319/2019©BEIESP | DOI: 10.35940/ijrte.C4642.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Since Remote sensing images are used for a variety of applications, classification of such images is essential for extracting information. However, due to the lack of sufficient training examples, classification of remote sensing images is more complex. Also, in traditional method of classification, the statistical procedure uses only the gray value of the digital images to extract information. Neural network (NN) has a better impact in this domain. The proposed work uses an integrated NN and cuckoo Optimisation Algorithm (COA) for classification. The NN picks, organise and constructs the data to be trained into a network. The network is then trained and tested. The COA is combined with NN to aid the task of classification and to calculate the cost function. The optimization algorithm provides an appropriate feature suitable for the neural network classifier to produce final output. This hybridized technique presents the effective advantages of the neural network and further investigate the classification of the multispectral remote sensible images. The performance and error rate of the system is better when compared with other classical methods. The observed results illustrate the effectiveness of the COA to optimize the cost function. As training parameters in Neural network is found to be NP hard, utilization of COA involve its responsibility in optimizing the learning rate of NN.
Keywords: Neural Network, Cuckoo Optimisation Algorithm, classification, cost function.
Scope of the Article: Classification.