A Forest Change Detection using auto Regressive Model-Based Kernel Fuzzy Clustering
Madhuri Mulik1, V. Jayashree2, P . N. Kulkarni3
1Ms. Madhuri B. Mulik, Ph.D Scholar, Department of E&TC Engineering, Sharad Institute of Technology College of Engineering, Yadrav (Ichalkaranji), India.
2Dr. V. Jayashree, Professor (PG), Department of Electronics, DKTE’s College of Textile and Engineering, Ichalakaranji, India.
3Dr. P. N. Kulkarni, Professor & Head, Department of Electronics and Communication Engineering, Bagalkot, India.
Manuscript received on 15 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 43-47 | Volume-9 Issue-1S6 December 2019 | Retrieval Number: A10091291S619/19©BEIESP | DOI: 10.35940/ijeat.A1009.1291S619
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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: Satellite images are used for applications related to the forest change detection, forest cover management, and so on as remote sensing provides the rich source of information for change detection. In this paper, the vegetation indices play a major role in extracting the useful information from the satellite images and the commonly employed indices. This paper analyzes the imagery data from the remote sensing satellites for detecting the changes in the forest over the year’s 2007-2017 using the pixel-based Bhattacharya distance. The indices from the satellite images are fed to the automatic segmentation model using the proposed Kernel Fuzzy Auto regressive (KFAR) model, which is the modified Kernel Fuzzy C-Means (KFCM) Clustering algorithm with the Conditional Autoregressive Value at Risk (CAVIAR). The forest change detection using the pixel-based Bhattacharya distance follows the segmentation, and the experimentation reveals that the proposed method acquired the minimal MSE and maximal accuracy of 0.0581 and 0.9211.
Keywords: Kernel Fuzzy C-Means Clustering, CAVIAR, Vegetative Index, Satellite Data, Forest Change Detection.
Scope of the Article: Clustering