A NOVEL ALGORITHM FOR AUTOMATIC SHIP AND OIL SPILL DETECTION BASED ON TIME-FREQUENCY METHODS

A novel method for automatic ship detection based on the Wavelet Transform (WT) has been recently presented. The results obtained point out the potential of the use of a multiresolution time-frequency framework for the analysis of SAR imagery. On the one hand, this paper aims at reviewing the algorithm for automatic spot detection on speckled images. On the other hand, a novel algorithm for the automatic extraction of linear features, also based on time- frequency methods will be presented, justified and tested. This approach is shown to be useful for a reliable automatic coastline extraction and for oil spill detection. It has already been shown that, as it can perceive a structure in the context of its surroundings, the human vision can manage non stationarities in a convenient way, overcoming existing automatic algorithms in extracting features in a complex scene: for example, the human eye is superior in observing a slick in the context of the surrounding sea and, surprisingly, some vessels undetected by conventional techniques are visible by eye. Nevertheless, since manual counting is a slow and unpractical process lacking of reproducibility, a computerised scheme is much more desired. Since multiresolution processing is able to model the operation of the human vision, it seems interesting to axe the interpretation of SAR images by means of time-frequency methods and, in particular, by means of the wavelets tools. Signal processing with wavelets is just one among other time-frequency methods but it presents clear advantages. The Short Time Fourier Transform and the Wigner-Ville transform are not always suitable for transient phenomena and the WT holds the key for the successes of detection / estimation in non stationary environment. Moreover, wavelet tools are specially well suited for their use on the processing of natural scenes since they are well adapted to analyze multifractal properties. ABSTRACT: