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Comparison of Visual Features for Image-Based Visibility Detection


The image-based visibility detection methods have been one of active researching issues in surface meteorological observation. The visual feature extraction is the basis of these methods, and its effectiveness has become a key factor in accurately estimating visibility. In this study, we compare and analyse the effectiveness of various visual features in visibility detection from three aspects, namely visibility sensitivity, environmental variables robustness and object depth sensitivity in multi-scene, including three traditional visual features such as Local Binary Patterns (LBP), Histograms of Oriented Gradients (HOG), and contrast as well as three deep-learned features extracted from the Neural Image Assessment (NIMA) and VGG-16 networks. Then, the Support Vector Regression (SVR) models, which are used to map visual features to visibility, are also trained respectively based on the Region of Interest (ROI) and the whole image of each scene. The experiment results show that compared to traditional visual features, deep-learned features exhibit better performance in both feature analysis and model training. In particular, NIMA, with lower dimensionality, achieves the best fitting effect and therefore is found to show good application prospects in visibility detection.