IAPRS & SIS, Vol.34, Part 7, "Resource and Environmental Monitoring", Hyderabad, India,2002 
Wavelet based fusion of ASTER (VNIR and SWIR) bands 
for improved soil information extraction 
K. VANI*, S. SANJEEVI** and A. RAVINDRAN* 
*Institute of Remote Sensing, Anna University, Madras-25. India 
**Centre for Geoscience and Engineering, Anna University, Madras-25. India 
Email: vani@annauniv.edu ssanjeevi@annauniv.edu 
KEYWORDS : FUSION, ASTER, SOIL, WAVELET, SWIR 
ABSTRACT 
Image fusion is a technique that has traditionally been attempted to improve the spatial resolution of multispectral data. However, not much 
emphasis has been laid on the improvement of spectral details by fusing two multispectral images with differing spectral ranges. This study 
is an attempt to fuse such images of an agriculturally dominant area, acquired in the VNIR and SWIR regions, by the ASTER (Advanced 
Space-borne Thermal Emission and Reflection Radiometer) sensor on board Terra 1. Optimal combinations were tried out from amongst 9 
bands (3 in VNIR + 6 in SWIR) and image fusion was attempted using Principal Component Analysis and wavelet transformation 
techniques. The resulting images were interpreted to prepare soil maps of the area. An existing soil map was used as the basis for 
comparison of the results. The combination that resulted in maximum contrast amongst the soil types is R=b3,G=b2,B=b1 for the VNIR 
bands and R=b4,G=b6,B=b9 for SWIR bands. It is observed that the fused image obtained by wavelet transformation resulted in exhibition 
of the maximum number (seven) of soil types when compared to the fused image obtained by PCA technique. Fusion using PCA technique 
resulted in enhancing the moist/wet soil types, while the wavelet transformation technique enhanced the saline and alkaline soils. Thus, 
improved soil mapping has been possible due to image fusion, and this study has demonstrated the need for image fusion in the multispectral 
domain, apart from attempting in the spatial domain, to bring out more information about soil types. 
1. INTRODUCTION 
The currently available high spatial resolution images have 
limitations in conveying information about the spectral 
characteristics of certain land cover features. Such limitations, 
however, could be overcome by multisensor image fusion 
techniques. Fusion is defined as combination of multiple images to 
form a new image for a certain application using a certain algorithm 
(Pohl,1998). The enhanced image obtained by image fusion can be 
used for identifying and discriminating land cover features such as 
vegetation types, water, moisture, soil types, concrete, asphalt. 
Much work has been carried out in development of image fusion 
techniques and their applications. Chavez et al. (1991) explain 
about IHS, PCA and HPF methods to merge multi-resolution and 
multi-spectral data (LANDSAT TM and SPOT Panchromatic) and 
compare the methods. The authors observed that the IHS method 
distorted the spectral characteristics of the data the most, followed 
by PCA and HPF methods. Crawford et al.(1999), fused airborne 
polarimetric and interferometric SAR data for mapping coastal 
environments. Neural network and Bayesian pair wise classifiers in 
a multi-resolution framework was utilized and an accurately 
classified map of the coastal test site was obtained. Li and Sheng 
(2000) describe about a fusion scheme based on “a trous” wavelet 
transformation for the fusion of IR and visible images. The authors 
report that the spatial resolution improvement of IR image was 
achieved and the salient information from both visible and IR image 
were preserved. Garguet-Duport et al. (1996) attempted fusion 
using multi-resolution analysis based upon wavelet transform for 
SPOT PAN and SPOT XS data. The authors also found this method 
very useful and particularly well adapted to vegetation analysis. 
From the above examples, it is observed that the wavelet 
transformation technique of image fusion seems to be 
advantageous, compared to the older techniques. 
Conventionally, image fusion has been attempted using high 
resolution panchromatic and low resolution multispectral image set 
or multispectral and radar images. The limitation of such studies is 
that the multispectral information content about the land cover 
features are made available only from one multispectral dataset, the 
other set being panchromatic. In such studies, only spatial details 
are enhanced and not much information about the spectral details is 
conveyed (eg soil, water, and vegetation types). This study attempts 
to fuse two image data sets of differing resolutions, both in the 
multispectral domain (VNIR & SWIR). The highlight of the image 
data used here is that both the multispectral image data sets are 
provided by a single coverage by the same sensor (thus avoiding 
multi date images, varying atmospheric conditions, differing look 
angle and geometry). This study also aims to utilize the results of 
such a fusion for soil mapping in an agriculturally dominant area in 
Tamilnadu. 
Soil spectral signatures result from the presence or absence, as well 
as the position and shape of specific absorption features of its 
constituents. The visible and NIR regions (0.4?m - 1.37m) are 
characterized by broad spectral absorption features (ferrous iron 
absorption feature near 1 ?m). Absorption features at 1.4?m - 
1.9?m are due to unordered arrangement of water molecules in 
soil, features in the 1.8? m - 2.5? m region are due to the presence of 
OH , CO; and SO, molecules and absorption near 1.4? m -2.2?m is 
due to layer silicate structure and moisture. 
     
  
   
  
  
  
   
  
  
   
  
   
  
  
  
  
  
  
  
  
   
   
   
   
   
  
  
   
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