Shadow removal on digital satellite images using wavelet transforms
Shadow detection and removal in real scene images is always a challenging but yet intriguing problem. Shadows cause hindrance to correct feature extraction of image features like buildings ,towers etc. in urban areas it may also cause false color tone and shape distortion of objects, which degrades the quality of images. Hence, it is important to segment shadow regions and restore their information for image interpretation. Shadows are generated by a local and relative absence of light. Shadows are, first of all, a local decrease in the amount of light that reaches a surface. Secondly, they are a local change in the amount of light rejected by a surface toward the observer. Most shadow detection and segmentation methods are based on image analysis. This paper presents a novel algoritm for automatic shadow detection and removing shadows using HSV color model, contour segmentation and wavelet transform based on a threshold determined by wavelet coefficients in complex urban color remote sensing images for solving problems caused by shadows. In the proposed algoritm shadows are detected using S and V components which is invariant to shadow i.e., it conveys the spectral and color characteristics of image features, regardless of variations in scene illumination condition and means of wavelet coefficients. The multi-resolution property of the wavelet transform leads into four different bands without the loss of spatial information. Once the shadows are detected they are classified and a non shadow area around each shadow termed as buffer area is estimated using contour segmentation. Experiments show that the new algoritm can accurately detect shadows from urban high-resolution remote sensing images and can effectively restore shadows with a rate of over 85%. The proposed algoritm can be used for further object recognition and thematic processing of scanner images.
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