ABSTRACT
The application of remote sensory images in crop monitoring has been increasing in the recent years due to its high classification accuracy. In this paper, a novel parallel classification methodology is proposed using a new clustering and classification concept. A novel neural network model with the Bs-Lion training algorithm is developed by integrating the Bayesian regularization training with the Lion Algorithm. Here, two levels of parallel processing are performed, namely parallel WLI-Fuzzy clustering and parallel BS-Lion neural network classification. The experimentation of the proposed parallel methodology is carried out using satellite images obtained from the Indian remote sensing satellite IRS-P6. The performance of the proposed system is compared with the existing techniques using validation measures accuracy, sensitivity and specificity. The experimentations resulted in promising results with an accuracy of 0.8994, sensitivity of 0.8682 and specificity of 0.8739, which favour the performance of the proposed parallel architecture in the classification.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
P. S. Ramesh obtained B.E. & M.E in Computer Science & Engineering from Anna University. He is currently doing Ph.D. in Anna University. His research interests are Image Processing and VLSI Design. He has more than 13 Publications in various International Journals and Conferences.
Dr. S. Letitia obtained B.E. & M.E. in Electronics and Communication Engineering from Anna University. She is currently working as Associate Professor in Thanthai Periyar Government Institute of Technology, Anna University. Her research interests are Communication, Signal processing, Image processing and VLSI Design. She has more than 6 Publications in various International Journals and Conferences.