A Forest Change Detection Using Auto Regressive Model-based Kernel Fuzzy Clustering: Advanced Study

Madhuri B. Mulik; V. Jayashree; P. N. Kulkarni

Madhuri B. Mulik Department of Electronics and Telecommunication, Sharad Institute of Technology, College of Engineering Ichalkaranji, India.
V. Jayashree Department of Electronics Engineering, DKTE �s College of Engineering, Ichalkaranji, India.
P. N. Kulkarni Department of Electronics and Communication Engineering, Bagalkot, Visvesvaraya Technical University Belgawi, India.

Keywords: Kernel fuzzy C-Means clustering, CAVIAR, vegetative index, satellite data, forest change detection

Abstract

This chapter focuses on the use of satellite images for the forest change detection, forest cover management. In this chapter, the vegetation indices play a major role in extracting the useful information from the satellite images. Also analysis was done on the imagery data from the remote sensing satellites for detecting the changes in the forest over the years 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 Mean Square Error (MSE) and maximal accuracy of 0.0581 and 0.9211.