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Proceedings Paper

A high-resolution index for vegetation extraction in IKONOS images
Author(s): M. Chikr El-Mezouar; N. Taleb; K. Kpalma; J. Ronsin
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Paper Abstract

In monitoring vegetation change and urban planning, the measure and the mapping of the green vegetation over the Earth play an important role. The normalized difference vegetation index (NDVI) is the most popular approach to generate vegetation maps for remote sensing imagery. Unfortunately, the NDVI generates low resolution vegetation maps. Highresolution imagery, such as IKONOS imagery, can be used to overcome this weakness leading to better classification accuracy. Hence, it is important to derive a vegetation index providing the high-resolution data. Various scientific researchers have proposed methods based on high-resolution vegetation indices. These methods use image fusion to generate high-resolution vegetation maps. IKONOS produces high-resolution panchromatic (Pan) images and low-resolution multispectral (MS) images. Generally, for the image fusion purpose, the conventional linear interpolation bicubic scheme is used to resize the low-resolution images. This scheme fails around edges and consequently produces blurred edges and annoying artefacts in interpolated images. This study presents a new index that provides high-resolution vegetation maps for IKONOS imagery. This vegetation index (HRNDVI: High Resolution NDVI) is based on a new derived formula including the high-resolution information. We use an artefact free image interpolation method to upsample the MS images so that they have the same size as that of the Pan images. The HRNDVI is then computed by using the resampled MS and the Pan images. The proposed vegetation index takes the advantage of the high spatial resolution information of Pan images to generate artefact free vegetation maps. Visual analysis demonstrates that this index is promising and performs well in vegetation extraction and visualisation.

Paper Details

Date Published: 22 October 2010
PDF: 9 pages
Proc. SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, 78242A (22 October 2010); doi: 10.1117/12.866187
Show Author Affiliations
M. Chikr El-Mezouar, Univ. de Djillali Liabes (Algeria)
Institut National des Sciences Appliquées de Rennes (France)
N. Taleb, Univ. de Djillali Liabes (Algeria)
K. Kpalma, Institut National des Sciences Appliquées de Rennes (France)
J. Ronsin, Institut National des Sciences Appliquées de Rennes (France)


Published in SPIE Proceedings Vol. 7824:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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