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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