Content-based Image Retrieval And Recognition For Ground-based Cloud Image

The regulation of cloud is an important climate change factors in the energy balance of earth's atmosphere. On the one hand, the formation, development and evolution of cloud reflect the degree of the stability, movement and moisture conditions of the atmosphere. On the other hand, cloud is one of the important signs for indicating weather changes. Therefore, the cloud observation has an important role. But the retrieval and recognition of cloud still depend on visual observation, and that has become a bottleneck of the automation of weather analysis and climate studies. First, it takes a great cost for observation; second, because of observers'subjective factors, observation results are somewhat inconsistent. Therefore, it has become an important research topic to automatically observe and analyze cloud. In this paper, we will study the texture feature of ground-based cloud image. The main contributions of this thesis include the followings:1) We present a new texture feature--context-sensitive texture feature, which based on the physical characteristics of cloud. This feature combines local binary pattern which reflects the local characteristics of image and Aura matrix which reflects the global characteristics of image, so it reflects the essential characteristics of cloud better.2) We use the context-sensitive texture feature for ground-based image retrieval, and verify the advantage of this feature.3) We use the context-sensitive texture feature for ground-based image recognize, and verify the advantage of this feature.4) We design and implement a platform for ground-based image retrieval and recognize, which includes five functions--batch image feature extraction, a single image feature extraction, cloud retrieval, training classifiers and cloud recognition.