A hierarchical data fusion framework for vegetation classification from multisource remotely sensed imagery

Author

Dai, Xiaolong ; Khorram, Siamak

Author_Institution

Center for Earth Obs., North Carolina State Univ., Raleigh, NC, USA

Volume

1

fYear

1998

fDate

6-10 Jul 1998

Firstpage

180

Abstract

This paper presents a methodological framework for a hierarchical data fusion system for vegetation classification using multisensor and multitemporal satellite imagery. The uniqueness of the approach is that the overall structure of the fusion system is built upon a hierarchy of remotely sensible attributes of vegetation canopy. This approach also produces classified products that are comprised of a series of important and direct terrestrial variables for ecological modeling with rigorous capabilities across spatial and temporal scales. The framework is mainly consisted of two components: automated image registration and hierarchical model for multisource data fusion

Keywords

geophysical signal processing; geophysical techniques; image classification; remote sensing; sensor fusion; automated image registration; geophysical measurement technique; hierarchical data fusion framework; hierarchical model; image classification; land surface; multisource data fusion; multisource remotely sensed imagery; multitemporal data; remote sensing; terrain mapping; vegetation classification; vegetation mapping; Biological system modeling; Classification tree analysis; Earth; Image registration; Image sensors; Logic; Neural networks; Satellites; Sensor fusion; Vegetation mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International

Conference_Location

Seattle, WA

Print_ISBN

0-7803-4403-0

Type

conf

DOI

10.1109/IGARSS.1998.702845

Filename

702845