Research On Content-based Satellite Cloud Image Data Mining Technolgy

The satellite cloud image which can reflect the characteristics and changing process of cloud system has become a very important reference factor to give the flood and drought forecast. The cloud image archive is a huge information resource. It is a difficult and time-consuming work to interpret these images manually. Although some artificial intelligence techniques can automatically accomplish the data analysis, they can't abstract the latent knowledge that embedded in the data, for these techniques strictly followed some predefined rules. As the Frontiers of the Discipline, the image mining (IM) technology provides the theory and methods of abstracting unaware, potential, comprehensible and useful information and knowledge from the cloud image archives. Three kinds of image mining tasks facing mixed data sets consisting of cloud image and rainfall data is designed in this thesis, and the acquired knowledge is much valuable for cloud image content understanding and rainfall forecasting. The primary work includes the following aspects.(1)In the research of the cloud image preprocessing, a new nonlinear adapted de-noising algorithm is proposed. Compared with the classical median filter, the proposed algorithm shows a better performance in term of eliminating the salty and pepper noise. The algorithm can preserve the non-noise pixel from being changed. Therefore, it is guaranteed that the pixel information truly reflects the state of cloud. The cloud image which includes some labeling objects such as longitude, latitude and placename, may influence the feature extracting. Considering that most of the labeled objects are line objects, a TV based labeling objects eliminating algorithm is proposed. The method improves the discretization by introducing the weight strategy. Experiments show that the improved algorithm efficiently eliminates the objects at the same time preserving the neighborhood information.(2)In the research of the ROI extracting method, the thesis develop a weighted clustering method based on the cloud image histogram. The algorithm fulfills the representative cloud domain extraction. In order to adapt to the sample distribution in the feature space, we focus on the improving of the clustering algorithm strategy. There are three strategies: The first strategy is focused on the improvements on the clustering number self-confirming method. A genetic algorithm confirming the optimized clustering number combing the evaluating index is proposed. This strategy improves the automatic level. The second strategy is focused on the improvements on the similarity measurements. A link-distance similarity measurement is proposed. Different from Euclidian distance, the link-distance is not sensitive to the data distribution. The third strategy is focused on the clustering mechanism. The Semi-supervised method which can overcome the blindness brought by the single clustering also can avoid the requirement of many training samples for the classification, is proposed. Finally, to reduce the time spending, the histogram of cloud image is used as the clustering object instead of the origin cloud image pixels.(3)In the research of cloud classification, three algorithms are proposed corresponding to the feature extraction, the feature selection and the classification model. To deal with the irregularity of cloud domain, the'basic round description model'is proposed to describe the cloud domain. The shape features are extracted based on this model and combined with the color and texture to form a comprehensive candidate feature sets. Also, a'BP-IPSO'model that adopts pso optimizing algorithm as learning algorithm instead of the bp neural network algorithm is proposed. The BP-IPSO model deals with the problems of slow convergence speed, easily falling into local minimum, and sensitive to the initial value. To efficiently fuse multiple features under the original classification mechanism, we developed a multiple features combination classification model. The local decisions made by child classification model are used to form the final decision by the voting method. Compared with the single classification model, the combination classification model acquires higher accuracy.(4)In the research of association rules mining based on the cloud-rainfall mixture data, four kinds of parameters which can reflecting the cloud state, is introduced. These state parameters take the relation between the grayscale of cloud image and the temperature of cloud top as the basis. Cloud state parameters and rainfall constitute the uniform mixture data through the synchronizing of time and space. To implement the transform of numerical attribute, a numerical attribute partition algorithm based on the clustering technology is proposed. The algorithm deals with the problem that the equal-depth partition method is sensitive to the data skew. To improve the processing efficient of large scale cloud-rainfall mixture dataset, a'Two-step Association Rules Mining based on the data partition'algorithm is proposed. Firstly, the algorithm partitions the database into several independent intervals. The global candidate itemsets are generated by the local large itemsets in each interval. Finally, we designed the'tidlists'data structure for the support count. These strategies improve the efficiency of the method by reducing the database scanning. It is proved that the method executes much more efficiently than Apriori method when the support threshold is low.