A new algorithm based on wavelets and neural networks is proposed for discriminating oil leaks using synthetic aperture radar (SAR) images. Utilizing the advantages of wavelets and neural networks, the algorithm is speedy and effective to distinguish oil embedded in both sea clutter and land clutter. The iterative algorithm uses a wavelet feature extractor and two unsupervised neural classifiers. The first stage classifier can divide the pixels in the SAR image into sea water, land and oil clusters. In the second stage, the classifier extracts oil pixels from previous oil cluster until matching the characteristics of the oil template. Using our proposed algorithm, the oil cluster will be formed automatically, provided the desired oil template is defined in advance.

In order to observe temporal distribution of sea clutter, radar echoes were taken from high sea state 7 at a fixed azimuth angle of 317. It is shown that the sea-clutter amplitudes obey the Weibull distribution at a grazing angle of 3.9, and obey both the Weibull distribution and K-distribution at grazing angles of 7.5 and 61.4. As the grazing angle increases, the shape parameters of Weibull distribution and K-distribution increase with both the distributions themselves tending to be closer to the Rayleigh distribution.

A novel algorithm associated with fractal preprocessors, wavelet feature extractors and unsupervised neural classifiers is proposed for detecting radar targets embedded in sea ice and sea clutter. Utilizing the advantages of fractals, wavelets and neural networks, the algorithm is suitable for real-time and automatic applications. Fractal preprocessor can increase 10 dB signal-to-clutter ratios (S/C) for radar images by using fractal error. Fractal error will make easy to detect radar targets embedded in high clutter environments. Wavelet feature extractors with a high speed computing architecture, can extract enough information for classifying radar targets and clutter, and improve signal-to-clutter ratios. Wavelet feature extractors can also provide flexible combinations for feature vectors at different clutter environments. The unsupervised neural classifier has a parallel operation architecture easily applied to hardware, and a low computational load algorithm without manual interventions during learning stage. We modified the unsupervised competitive learning algorithm to be applicable for detecting small radar targets by introducing an asymmetry neighborhood factor. The asymmetry neighborhood factor can provide a protective learning to prevent interference from clutter and improve the learning effects of radar targets. The small radar targets in Millimeter wave (MMW) and X-band radar images have been successfully discriminated by our proposed algorithm. The effective, efficient, high noise immunity characteristics for our proposed algorithm have been demonstrated to be suitable for automatic and real time applications.

We observed the log normal, log-Weibull and K-distributed sea-clutter from high sea state 7 with an X-band radar for grazing angles between 3.1 and 17.5. To determine the sea-clutter amplitude statistics, we introduced the Akaike Information Criterion (AIC), which is more rigorous fit of the distribution to the data than the least-squares method.