The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
Figure 2. Landsat ETM subscene with clouds and cloud-related 
shadows 
Figure 3. Features partially obscured by cloud shadows 
Another cloud-free image, shown in Figure 4 was used as a 
reference image for the results evaluation and quality analysis. 
This image is another ETM Panchromatic image of the same 
spatial resolution taken in July 2000. The small time difference 
between the two images minimizes the expected temporal 
differences taking into consideration the arid nature of the 
imaged area. 
Figure 4. Cloud-free images-July 2000 Landsat7 ETM 
panchromatic subscene 
3.2 Shadow Detection 
Many algorithms were developed to detect clouds and their 
associated shadows. Concepts such as simple thresholding 
(Shaker et al.,2005) and fuzzy logic (Lissens, et al.,2000) were 
utilized in these algorithms. The wavelet methodology applied 
in this research utilizes clouds-shadow geometric constraints in 
addition to their spectral characteristics (Abdelwahab,2006). In 
this methodology, clouds and shadows are first classified using 
neural network classification process, which mainly depends on 
selecting training sets and perform automatic classification 
based on image spectral properties. Then, the classification 
results are improved by defining the spatial relationship 
between cloud patches and their corresponding shadows. 
A linear mathematical model is assumed and a least squares 
adjustment process is performed to estimate model parameters. 
The model then is used to filter out non-shadow pixels. 
Accuracy assessment results showed 11% and 19% commission 
and omission percentage errors respectively. Most of the errors 
were found to be at the edges of the shadow areas. It is believed 
that this methodology needs further enhancement to overcome 
the high percentage of the omission errors. However, this is 
considered outside the scope of this research which focuses 
mainly on image enhancement in the shadow areas. 
3.3 Image Enhancement using Developed Algorithm 
A histogram shift is applied to the shadow areas in the tested 
image. The shift value is extracted from the difference between 
the median gray level value in the shadow regions and the 
corresponding median in the rest of the image, excluding the 
identified cloud regions. This modification increases the gray 
level values in the shadow areas to approach those in the 
shadow/cloud free areas. The modified image is then 
decomposed using one of the Biorthogonal wavelet families 
Bior(2,2). One to four levels of decomposition were tested. 
A universal threshold denoising algorithm, introduced in 
section 2.3, was applied to attenuate noise in the mage based on 
Dononho,(1992). In this technique, high magnitude detail 
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