Orders > Conference proceedings > Atlantic Europe conference on remote imaging and spectroscopy
(from Chapter 4: Clustering and Classification)
| Full Citation and Abstract
|
Title: |
Partial unmixing of multi or hyperspectral images using ICA and fuzzy clustering techniques: Application to vegetation mapping on vineyards |
| |
Author(s): |
Saeid Homayouni, Jean-Pierre Da Costa, Christian Germain, Olivier Lavialle, Gilbert Grenier |
| |
Address: |
LAPS, UMR 5131 CNRS, Universit Bordeaux 1 – ENSEIRB – ENITAB, 351 Cours de la Libration, F33405 Talence cedex, France
saeid.homayouni @ laps.u-bordeaux1.fr, jp-dacosta @ enitab.fr, germain @ tsi.u-bordeaux.fr, Olivier.lavialle @ laps.u-bordeaux1.fr, grenier @ enitab.fr |
| |
Reference: |
AECRIS 2006 Proceedings pp. 115 - 122 |
| |
Abstract/
Summary |
The context of this paper is agricultural remote sensing imagery. Its objective is to propose a blind framework for the partial unmixing of multi or hyperspectral images. This framework relies on a linear mixing model with physical constraints related to the positivity and the dependence of the mixing coefficients. It hinges on three steps. The first step is a non-constrained source separation operation using Independent Component Analysis. Then, a vine-related source is selected by comparing the various sources to a rough vegetation index map. Finally, a fuzzy clustering algorithm is applied to the vine-like source to provide a soft classification of pixels into vine and non-vine pixels, i.e., a vine abundance map. The entire framework is successfully applied to hyperspectral CASI data acquired on vineyards. Results are provided which allows discussing the relevance and the capabilities of the approach. |
| |
|
| |
We welcome your comments about this Article  |
|
