993 
a Signal and Images Processing Laboratory, Electronics and Computer Science Faculty, U. S. T. H. B., P. O. Box 32, El 
- Alia, Bab - Ezzouar, 16111, Algiers, Algeria - s_chitroub@hotmail.com 
b Al Madinah Remote Sensing Center, AlMadinah RD, Riaydh Alkhabra City, P.O.Box 242 Saudi Arabia - 
ssulltan@hotmail. com 
KEY WORDS: Vegetation analysis, PCA, Independent component analysis, Neural network, Change detection 
ABSTRACT: 
The seasonal analysis of vegetation can be considered as looking for fundamental redundant information and detecting, at the same 
time, the natural changes of the vegetative cover undergone by the observed scene. From the statistical point of view, the redundant 
information can be quantified by the correlation coefficients between the multi-temporal images while the natural changes can be 
considered as the mutual information between the transition zones of the observed scene. For detecting and emerging the zones of 
transition and preserving at the same time the zones of vegetation temporal evolution stability, it is interesting to create new images 
in which the correlation between the images is vanished and the mutual information is minimized. To reach such purpose, we have 
developed a new approach for seasonal vegetation analysis based on a new statistical multi-variate method called independent 
component analysis (ICA). 
1. INTRODUCTION 
Multispectral image processing is a promising tool for the 
analysis of vegetation in remote sensing imagery, particularly in 
areas with low vegetation cover (Chen, 1998a ; Chen, 1998b ; 
Gracia and Ustin, 2001 ; Shabanov et al., 2001; Kogan et al., 
2003; Frank and Mentz, 2003). Seasonal vegetation analysis in 
the absence of land cover change is much more challenging 
than land cover analysis. A variety of multispectral vegetation 
indices have been developed in order to detect these changes 
(Chen, 1998b ; Kogan et al., 2003; Frank and Mentz, 2003). 
However, these indices are insufficient for seasonal vegetation 
analysis, especially when the number of spectral bands is 
important, in which make full use of the available spectral 
images becomes impossible (Frank and Mentz, 2003). In 
addition, the developed vegetation indices are limited in the 
detection of low vegetation cover because of varying 
background signals (Frank and Mentz, 2003). 
The seasonal analysis of vegetation can be considered as 
looking for fundamental redundant information, which exists 
between the multi-temporal remote sensing data (acquired for 
the same scene) and detecting and emerging, at the same time, 
the natural changes of the vegetative cover undergone by the 
observed scene. The redundant information characterizes the 
stability in the vegetation evolution in the areas that are not 
undergone to the natural changes across the time. The natural 
changes, however, characterize the transitions across the time 
between the states of the natural change zones of the scene. 
From the statistical point of view, the redundant information 
can be quantified by the correlation coefficients between the 
multi-temporal images while the natural changes can be 
considered as the mutual information between the transition 
zones. For emerging the transition zones and preserving at the 
same time the zones of vegetation temporal evolution stability, 
it is interesting to create new images in which the correlation 
between these images is vanished and the mutual information is 
minimized. To reach such purpose, we have developed a new 
approach of seasonal vegetation analysis based on a new 
statistical multivariate method called independent component 
analysis (ICA). 
ICA is a useful extension of standard Principal Component 
Analysis (PCA) (Chitroub, et al., 2001 ; Chitroub, et al., 2004 ; 
Karhunen and Joutsensalo, 1994). As the name implies, ICA is 
to find the transformation such that the resulting components 
are as statistically independent from each other as possible. It 
takes into account of higher order statistical properties and its 
components are mutually independent with respect to these 
higher order statistics, thus making ICA more truly independent 
that PCA (Cardoso, 1999 ; Lee et al., 1999). The ICA model is 
very suitable for neural network realization (Hyvarinen, 1999 ; 
Lee et al., 2000). Most application of ICA so far has been on 
Blind Signal Separation (BSS) of unknown source signals from 
their linear mixture for which ICA obviously is useful. The use 
of ICA for images has been much limited. We believe that ICA 
can be useful in general in image and signal processing 
(Chitroub, et al., 2004). In this paper, we will demonstrate some 
potential advantages of ICA in remote sensing study. We are 
concerned with the seasonal analysis of vegetation. The 
remainder of this paper is organized as follows. The proposed 
model is exposed in detail in section 2. Experiments performed 
on the multi-temporal Landsat-TM images (they cover 
AlQassim region in Saudi Arabia), are given and commented in 
section 3. We conclude the paper in the last section. 
2. ICA - BASED METHOD FOR SEASONAL 
VEGETATION ANALYSIS 
In this paper, we demonstrate the usefulness of ICA for 
seasonal vegetation analysis. For that, a PCA-ICA neural