The Hybridization of Neural Network and Particle Swarm Optimization for Natural Terrain Feature Extraction
Sakshi Dhingra1, Dharminder Kumar2
1Sakshi Dhingra*, Guru Jambheshwar University of Science and Technology, Haryana, India.
2Prof. Dharminder Kumar, Professor, Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hissar, Haryana, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 3776-3782 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4822119119/2019©BEIESP | DOI: 10.35940/ijitee.A4822.119119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: The optimization of various soft computing and metaheuristic techniques can be ameliorated in a global area network, Swarm intelligence. In this research, a hybrid algorithm of neural network and particle swarm optimization has been presented for remote sensing applications. The terrain features of the land in a remote sensing image have been classified using these algorithms. Remote sensing basically deals with the processing and interpretation of satellite images without any physical contact to that particular region. In addition, the geospatial characteristics of the data also recorded during image classification. The hybrid concept used in this research, the implementation of algorithm in this paper based on the neurons network to find the best solution, which is further resolved using the Particle Swarm Optimization approach, an optimization technique. The proposed algorithm easily classifies the terrain features with higher efficiency and kappa coefficient value. The results show that 94.36% accuracy attained from the proposed technique. The overall accuracy improved by 5.24 % and 14.93% and kappa coefficient enhancement of 6.97 % and 18.99 % in comparison to existing studies.
Keywords: Remote Sensing, Satellite Images, Particle Swarm Optimization, kappa coefficient
Scope of the Article: Discrete Optimization