A hierarchical methodology framework for multisource data fusion in vegetation classification

Abstract

This Letter presents a new methodological framework for a hierarchical data fusion system for vegetation classification using multi-sensor and multitemporal remotely sensed imagery. The uniqueness of the approach is that the overall structure of the fusion system is built upon a hierarchy of vegetation canopy attributes that can be remotely detected by sensors. The framework consists of two key components: an automated multisource image registration system and a hierarchical model for multi-sensor and multi-temporal data fusion.