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.

Freeway traffic state estimation is useful for intelligent traffic guidance, control, and management. The freeway traffic state is featured with rapid and dramatic fluctuations, which presents a strong nonlinear feature. In theory, a particle filter has good performance in solving nonlinear problems. This paper proposes a particle filter based approach to estimate freeway traffic state. The freeway link between the west of Peace Bridge and the west of San Yuan Bridge of the third ring in Beijing is used as the experimental object. According to the traffic characteristics and measurement mode of the link, the second-order validated macroscopic traffic flow model is adopted to set up the link model. The implementation steps of the particle filter for freeway traffic state estimation are described in detail. The estimation error analysis for the experiments proves that the proposed approach has an encouraging estimation performance.

No abstract