WAVELET-BASED FEATURE-EXTRACTION FROM OCEANOGRAPHIC IMAGES

Features in satellite images of the oceans often have weak edges. Thes e images also have a significant amount of noise, which is either due to the clouds or atmospheric humidity. The presence of noise compounds the problems associated with the detection of features, as the use of any traditional noise removal technique will also result in the remov al of weak edges. Recently, there have been rapid advances in image pr ocessing as a result of the development of the mathematical theory of wavelet transforms. This theory led to multifrequency channel decompos ition of images, which further led to the evolution of important algor ithms for the reconstruction of images at various resolutions from the decompositions. The possibility of analyzing images at various resolu tions can be useful not only in the suppression of noise, but also in the detection of fine features and their classification. This paper pr esents a new computational scheme based on multiresolution decompositi on for extracting the features of interest from the oceanographic imag es by suppressing the noise. The multiresolution analysis from the med ian presented by Starck-Murtagh-Bijaoui [4], [5] is used for the noise suppression.