Automated interpretation of sub-pixel vegetation from IRS LISS-II images

Abstract

Satellite sensor data are important for monitoring and assessment of natural resources. As vegetation is one of the most valuable natural resources, automated interpretation of vegetative cover from satellite images is prerequisite for various applications and decision processes. This paper defines a system that classifies as well as interprets vegetation from satellite images automatically. The system applies a knowledge-based approach wherein features are represented by linguistic variables in terms of their fuzzy labels. The accuracy of the system has been found to be more than 95% for hard class and more than 85% in the case of sub-pixel classification. Thus, it can be concluded that the approach adopted can be utilized in developing any automated image understanding system.

Acknowledgments

The author expresses sincere gratitude to Dr Partha Sarathi Roy, Dean, IIRS, Dehradun INDIA for his help in the present study. The author also wish to express his sincere appreciation to the reviewers for their critical suggestions and valuable improvements.

Notes

^*

^†where G, R and IR are the brightness values in the green (band 2), red (band 3) and infrared (band 4) bands, respectively, of IRS-IA LISS II data.