Suitability of Real-Time Image under Complicated Environment Based on Contourlet in SMN

Judging whether the real-time image under complicated environment is suitable is a challenging problem in scene matching navigation, which contributes to ensure the navigation precision and decrease computational complexity. This paper proposes a novel method for analyzing the suitability of real-time image under complicated environment based on Contourlet by taking advantage of the characteristic of multi-direction and multi-scale of Contourlet, where the complicated environment focus on motion blur, illumination variation, occlusion of cloud and fog. Firstly, real-time image is transformed on 4-layer Contourlet, and the obtained coefficients are parameterized by Generalized Gaussian Distribution, forming a 62 - dimension feature vector. Then the relationship between the feature vector and the objective evaluation index of suitability is trained by support vector machine, to build the prediction model of suitability of real-time image under complicated environment. Finally, experiments are performed on image database picked from Google Earth. The experiments clearly demonstrate that the proposed algorithm is simple but effective for real-time image quality assessment in scene matching navigation.