DMCA
Fusion of Vegetation Indices Using Continuous Belief Functions and Cautious-Adaptive Combination Rule
| Venue: | IEEE Transactions on Geoscience and Remote Sensing |
| Citations: | 2 - 0 self |
BibTeX
@ARTICLE{Kallel_fusionof,
author = {Abdelaziz Kallel and Sylvie Le Hégarat-mascle and Laurence Hubert-moy and Catherine Ottlé},
title = {Fusion of Vegetation Indices Using Continuous Belief Functions and Cautious-Adaptive Combination Rule},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
year = {},
pages = {1499--1513}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract—The goal of this paper is to propose a methodology based on vegetation index fusion to provide an accurate estimation of the fraction of vegetation cover (fCover). Because of the partial and imprecise nature of remote-sensing data, we opt for the evi-dential framework that allows us to handle such kind of informa-tion. The defined fCover belief functions are continuous with the interval [0, 1] as a discernment space. Since the vegetation indices are not independent (e.g., perpendicular vegetation index and weighted difference vegetation index are linearly linked), we define a new combination rule called “cautious adaptive ” to handle the partial “nondistinctness ” between the sources (vegetation indices). In this rule, the “nondistinctness ” is modeled by a factor varying from zero (distinct sources) to one (totally correlated sources), and the fusion rule varies accordingly from the conjunctive rule to the cautious one. In terms of results, both in the cases of simulated data and actual data, we show the interest of the combination of two or three vegetation indices to improve either the accuracy of fCover estimation or its robustness. Index Terms—Belief functions, combination rule, evidence theory, fraction of vegetation cover (fCover), vegetation index fusion. I.
Keyphrases
cautious-adaptive combination rule vegetation index vegetation index fusion vegetation cover actual data conjunctive rule accurate estimation evidence theory partial nondistinctness remote-sensing data discernment space fcover estimation new combination rule fusion rule varies simulated data defined fcover belief function perpendicular vegetation index imprecise nature difference vegetation index cautious adaptive combination rule evi-dential framework cautious one distinct source index term belief function
