The Research Of Ground Nephogram Recognition Algorithm

In atmospheric radiation transfer, clouds play an important role, the shape of clouds, the distribution of clouds, the quantity of clouds and changes marked the atmospheric movement conditions. For the change of weather and climate, cloud plays a crucial role, to a certain extent, cloud changes reflect the future trend of the changes in the weather, so fast and accurate identification of cloud type classification has important significance. Compared with the traditional large-scale satellite images, the distribution characteristics of large scale, ground observations smaller range of visible light cloud, can reflect the size of the local distribution of information, arrangement and distribution of high and low clouds, etc., and the more rich texture information, is helpful for people to adopt the appropriate means of its texture features for classification and thus be short-term, small-scale weather forecasts. In recent years, the analysis of ground nephogram become the hot topics in the study of cloud of short-term weather forecasting research, cloud nephogram accurate recognition is an important research topic in the field of weather, and the main factors influencing the cloud image recognition effect involves two aspects:feature extraction and recognition method.This article will from the above of two aspects for the ground nephogram for further analysis. First of all, in terms of feature extraction, the image texture feature extraction of usually is used more local features or global features. Global features from overall describe the images, all pixels in an image to calculate the characteristic; Local features is used to describe the local details of the image and can eliminate screening, rotation, etc. Given the global features and local characteristics with different characteristic, foundation, the author of this paper only rely on the shortage of the single feature cloud image classification and recognition, explore the way of fusion global features and local features, in order to improve the ground nephogram classification accuracy and robustness. Specific to the current popular methods of texture feature, gray level co-occurrence matrix (GLCM) and the method if LBP (Local binary patterns) to extract image texture characteristics of their LBP fusion algorithm, to make up for a single texture feature can cloud one attribute description foundation of defects. Relative to a single texture feature method, the experiment results show that characteristics of the proposed fusion method can effectively improve the accuracy of image classification the ground nephogram.Secondly, in view of the ground nephogram classification problem, this paper summarizes the predecessors’foundation cloud image classification methods to focus on the diversity of weak classifiers integration of research, this paper proposes a k-means algorithm based selective integration cloud nephogram image recognition algorithm of neural network, and the algorithm based on BP neural network integration model, using k-means clustering algorithm to choose part of a diversity of individuals neural network integration, establish the foundation cloud classification model. Compared with traditional algorithm and integrated learning algorithm in pattern recognition is used for the foundation of cloud classification identification, the experimental results show that the proposed build selective integrated classification algorithm is used to cloud image recognition, has higher classification accuracy.Finally, using MATLAB tools to simulation algorithm, and based on the results of the simulation study, the design and implementation of automatic identification foundation cloud classification system, the system includes a cloud preprocessing, feature extraction and cloud identification and other major functions.