Non-stationary nature of the SAR image is a major obstacle to the automatic interpretation of SAR sea ice image. Incidence angle effect is one of the main factors leading to instability in the sea ice image features. Wide observation with automatic interpretation of SAR sea ice image,incidence angle effect on sea ice region, considering the speckle noise, the incidence angle effect and uncertainties, to the region through the pixel and then to large-scale regional a way, the merger of the incident angle effect correction on the scale of the regional clustering, class and regional operating combination of up to improve the segmentation algorithm for non-stationary adaptability.

No abstract

We established a deep-sea internal wave detection model based on the nonlinear Schrödinger (NLS) equation and Synthetic Aperture Radar (SAR) images, and applied the model to the Malin Shelf edge, located at UK Continental Shelf, west of Scotland, to retrieve internal wave parameters. We selected the SAR images of internal waves at Malin Shelf edge, combined NLS equation with the action spectrum balance equation and Bragg scattering model, retrieved the amplitudes and phase velocities of the internal waves at Malin Shelf edge, and compared these data with those retrieved by the model based on KdV equation and those observed at the same period. The results show that the error between the data retrieved by our model and the measured data is very small, while the difference between the data retrieved by the detection model based on KdV equation and the measured data is significant. In addition, the phase velocities, calculated in our model and the model based on KdV equation, are both close to the measured data. Consequently, our model is valid and more accurate for the parameter inversion of internal waves in deep-sea area.