Abstract
Spatial digital image analysis plays an important role in information decision support systems, especially for regions frequently affected by hurricanes and tropical storms. For aerial and satellite imaging based pattern recognition, it is unavoidable for these images to be affected by various uncertainties, such as atmospheric medium dispersion. Image denoising is thus necessary to remove noise and retain important digital image signatures. The linear denoising approach is suitable for slow variation noise cases. However, the spatial object recognition problem is essentially nonlinear. Being a nonlinear wavelet based technique, wavelet decomposition is effective for denoising blurred spatial images. The digital image is split into four subbands, representing approximation and three details (high frequency features) in the horizontal, vertical and diagonal directions. The proposed soft thresholding wavelet decomposition is simple and efficient for noise reduction. To further identify the individual targets, a nonlinear K-means clustering based segmentation approach is proposed for image object recognition. Selected spatial images were taken across hurricane affected Louisiana areas. In addition for the evaluation of this integration approach via qualitative observation, quantitative measures are proposed on the basis of information theory. Discrete entropy, discrete energy and mutual information are applied for accurate decision support.
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Ye, Z., Mohamadian, H. (2012). Digital Image Processing for Spatial Object Recognition via Integration of Nonlinear Wavelet-Based Denoising and Clustering-Based Segmentation. In: Yeh, A., Shi, W., Leung, Y., Zhou, C. (eds) Advances in Spatial Data Handling and GIS. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25926-5_11
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DOI: https://doi.org/10.1007/978-3-642-25926-5_11
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