The possibility to retrieve ocean wave parameters from SAR images of waves in the nearshore region is explored. Contrarily to the open ocean case, nearshore waters render more complex wave patterns due to interactions with the bottom and obstacles. The basic idea was to use the MPI inversion algorithm in combination with first guess spectra generated by the SWAN model. In order to assess the performance of the algorithm, several experiments were carried out to analyze the inverted spectrum, as well as the degree of influence of the first guess spectrum in the retrieved spectrum. Spatial variations of the wave field in the northwestern coast of Baja California, Mexico, were analyzed by using subimages extracted from ERS-2 image mode products. Wave spectra were retrieved from subimage spectra by inverting Hasselmann's spectral transformation relation, which describes the nonlinear mapping of an ocean wave spectrum onto a SAR image spectrum. The retrieved wave spectra showed a significant improvement with regard to the initial SWAN spectra, displaying more accurate spectral peaks and wave modes not present in the initial spectra. Although a low dependence of the retrieved wave spectra from the first guess was observed, the former one can influence the distribution of the secondary wave systems. It is concluded that information not accounted by the model can be inferred from the SAR image in accordance to the wave imaging theory. These results are encouraging for researchers conducting studies on wave modeling in shallow waters, as well as in data assimilation programs.
Scanning-radiometer imagery commonly displays systematic line stripes originated by differences in the sensibility of individual detectors. Traditionally, this noise has been eliminated by convolution techniques using digital filters. In this work we propose the sue of the wavelet transform as a technique for directional filtering. The method was tested in a Landsat multispectral scanner image having severe band striping. Since its basis functions are localized in the frequency and time domain, the wavelet transform performed a more selective analysis of the data, identifying the noise patterns in the image and allowing their remotion without data degradation.