WAVELET ENHANCEMENT OF CLOUD-RELATED SHADOW AREAS IN SINGLE 
LANDSAT SATELLITE IMAGERY 
A. Abd-Elrahman 3 , M. Elhabiby b 
a School of Forest Resources and Conservation — Geomatics, University of Florida - aamr@ufl.edu 
b Department of Geomatics Engineering, University of Calgary - mmelhabi@ucalgary.ca 
Commission VII, WG VII/6 
KEY WORDS: Wavelet, image fusion, shadow, frequency decomposition, Landsat 
ABSTRACT: 
Cloud-related shadows represent areas with low illumination conditions that affect remote sensing image quality. In this research, a 
wavelet-based image sharpening algorithm was developed to enhance shadow areas independently using the defected cloudy image 
information. The developed algorithm is applied locally by boosting the image high frequency content in the shadow areas using the 
defected image de-noised wavelet coefficients. Image entropy was used as a measure to determine the change in wavelet coefficient 
values. The developed algorithm was tested on the panchromatic band of a November 2001 Landsat 7 ETM satellite subscenes. 
Another cloud-free image was used as reference image for results evaluation and quality analysis. Several discrete wavelet 
decomposition levels were tested. Quantitative measures were used to evaluate the obtained results. Visual and quantitative analysis 
of the results revealed that the ability to enhance details under shadow areas increased with the increase in the number of wavelet 
decomposition levels. Two or three wavelet decomposition levels were found to be sufficient for enhancing image quality in the 
shadow areas while avoiding introduced artifacts. Conclusions and recommendations are given with respect to the suitability, 
accuracy and efficiency of this method. 
1. INTRODUCTION 
Small cloud patches are very common in the images taken in 
many parts of the world. This problem of clouds and cloud- 
associated shadows are widely spread with approximately 66% 
of the earth surface is covered by clouds throughout the year 
(Belward and Valenzuela, 1991). Although these cloud areas 
can easily be detected due to their high reflectance, the shadows 
caused by these clouds represent areas with low illumination 
conditions that are harder to detect but have the potential for 
enhancement. Cloud-related shadow removal is normally 
handled by first detecting the cloud and shadow areas. Then, 
image intensities in the shadow regions are adjusted to enhance 
the image quality. Different methodologies are developed and 
implemented for shadow detection that utilizes geometric 
constraints in addition to the image spectral characteristics 
(Abdelwahab,2006; Simpson and Stitt, 1998). 
Some of these methods identifies and removes the image 
illumination variations using surface reflectance and variations 
constraints (Finlayson et al.,2002; Marini and Rizzi,2000). Such 
methods were implemented mostly on high spatial resolution 
imagery and suffer costly computational overhead in addition to 
shadow edge processing problems. Other methods utilized 
overlapping imagery to detect occlusion and remove shadows 
(Zhou et al.,2003). Fusion techniques were also used to account 
for cloud and shadow defects on certain image using different 
cloud/shadow free images (Berbar and Gaber,2004; Wang and 
Ono,1999). Although these methods are widely used, they 
require another cloud free image taken at different time to 
compensate for defects in the cloud and shadow regions. 
Temporal image variations represent a major source of error in 
the output enhanced image. 
Shadow restoration from a single image can be looked at as an 
intensity adjustment process. Simply adjusting the brightness 
and contrast of the image cannot remove the shadow effect. The 
fact that shadows smooth brightness variations reduces the 
shadow removal process to an image sharpening process 
applied locally to the shadow area. Image sharpening can be 
done in the spatial or frequency domains (Haralick and 
Shapiro, 1992). One of the methods used in the frequency 
domain is the wavelet. The frequency domain provides the 
flexibility to model frequency content differently at each 
wavelet decomposition level. Wavelet analysis was used for 
image sharpening in applications that range from mammogram 
image sharpening (Bouyahia et al.,2004) to enhancement of 
scanned images (Barkener,2002). 
In this research, wavelet sharpening will be used to boost the 
high frequency content in shadow areas. Only the cloudy image 
will be used for such analysis. In the following sections, the 
methodology implemented in this research will be described 
followed by a numerical example. Experiment results, analysis 
and final conclusions are then presented. 
2. BACKGROUND AND METHODOLOGY 
2.1 Discrete Wavelet Transform 
Wavelet decomposition and reconstruction was extensively 
used in image fusion and compression applications. Discrete 
wavelet transform will be utilized in this research paper to 
enhance cloud-related shadow areas by adjusting their 
frequency content. 
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