Surface segmentation and environment change analysis using band ratio phenology index method – supervised aspect. Issue 9 (26th May 2020)

• Record Type: Journal Article
• Authors: Sivabalan, K.R.; Ramaraj, E.
• Is Part Of: IET image processing. Volume 14:Issue 9(2020)
• Page Start: 1813
• Page End: 1821
• Publication Date: 2020-05-26
• Subjects: ecology -- vegetation -- image classification -- geophysical image processing -- learning (artificial intelligence) -- global warming -- irrigation -- remote sensing; global warming analysis -- satellites -- multispectral imagery -- principle remote sensing applications -- innovative method -- physical characteristics -- weather forecasting -- ecology assessment -- irrigation management -- seasonal changes -- seasonal change analysis -- supervised image classification -- band ratio phenology index method -- BRPI -- seasonal impact -- classification exactness -- surface segmentation -- supervised aspect -- escalating field -- vegetation assessment -- coastal studies; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6526
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Remote sensing is an escalating field that helps to monitor the earth in different perspectives like vegetation assessment, coastal studies, global warming analysis etc. Presently many satellites are orbiting the earth for taking multispectral imagery, which is working behind the principle remote sensing applications. Though there are mechanisms for image classification still innovative method is required to detect and monitor the physical characteristics of the environment. Weather forecasting, ecology assessment and irrigation management are relying upon the seasonal changes. This research study concentrates on seasonal change analysis by supervised image classification called Band Ratio Phenology Index (BRPI) method. This BRPI has helped to learn seasonal impact on the environment for the last six years. Confusion Matrix, Overall Accuracy, and Kappa Coefficient are the quality measures used to legitimise the classification exactness.