Since a low-illumination image will become a pseudo fog map after inversion, and the concentration of this pseudo fog map is decided by illumination rather than depth of field, a low-illumination image enhancement method based on physical model was proposed, which provided a fast and accurate method to estimate the transmittance. Firstly, dark channel prior was used to estimate atmospheric light value of pseudo fog map and the transmittance was estimated according to the illumination. Secondly, the image without fog was restored based on the atmospheric scattering mode. Finally, the enhanced image was obtained by inversing the image without fog. Furthermore, the clear image was got by making detail compensation on the enhanced image. A large number of experiments show that the proposed algorithm is faster and performs well without losing information compared with the existing algorithms including the enhancement algorithms based on dark channel prior, defogging techniques and the multi-scale Retinex with color restoration, meanwhile it can improve the efficiency of image analysis and recognition system.

Updating road information database in the sustainability, a road alignment identification model based on LVQ-Boosting approach was proposed, which utilized a large number of path tracking trajectory data generated by car GPS for capturing quickly changes in road information. The approach further improved the generalization ability of LVQ and obtained a classifier with strong classification performance through employing weak classification algorithm. Thus, the purposes for identifying automatically the features of the road alignment and fast grouping the road feature type were implemented. The experimental results show that the approach has high efficiency and accuracy of the road alignment identification.