Land use Land Cover Mapping using Modified Ant Colony Optimization Technique
Bhagavat. D. Jadhav1, Chandrama. G. Thoart2, Ajay. N. Paithane3, Pravin M. Ghate4
1Bhagavat D. Jadhav*, Department of Electronics and Telecommunication Engineering, JSPM’s Rajarshi Shahu College of Engineering, Savitribai Phule Pune University, Pune , Maharashtra, India.
2Chandrama G. Thorat, Department of Computer Engineering, Pune, Govt. College of Engineering, Savitribai Phule Pune University, Pune , Maharashtra, India.
3Ajay N. Paithane, Department of Electronics and Telecommunication Engineering,, JSPM’s Rajarshi Shahu College of Engineering, Savitribai Phule Pune University, Pune , Maharashtra, India.
4PravinM. Ghate, Department of Electronics and Telecommunication Engineering,, JSPM’s Rajarshi Shahu College of Engineering, Savitribai Phule , Pune University, Pune , Maharashtra, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 27, 2020. | Manuscript published on February 10, 2020. | PP: 100-106 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1132029420/2020©BEIESP | DOI: 10.35940/ijitee.D1132.029420
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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: Land use Land cover classification is an important aspect for managing natural resources and monitoring environmental changes. Urban expansion becomes one of the major challenges for the administrator. The LANDSAT 8 images are processed using the open source GRASS (Geographic Resource Analysis Support System). Unsupervised classification technique based on Ant Colony Optimization (ACO) algorithm has been modified and proposed as Modified Ant Colony Optimization (MACO) for LULC classification. In order to improve the classification accuracy of the proposed algorithm, we have combined spatial, spectral and texture features to extract more information of homogeneous land surface. The classification accuracy of the proposed algorithm has been compared with other unsupervised classification methods such as k-means, ISODATA and ACO algorithms. The overall classification accuracy of proposed unsupervised MACO algorithm has been increased by 11.24 %, 8.24% for open space and water bodies class, respectively as compared to ACO algorithm.
Keywords: Remote Sensing, Image Enhancement, Modified ant Colony Optimization, Classification Accuracy
Scope of the Article: Classification