Foundation cloud picture classification method based on completion local three value model

Abstract

The invention discloses a foundation cloud picture classification method based on a completion local three value model. The method comprises the following steps that the local information of each training sample is decomposed into local difference value vectors and center pixels; each local difference value vector is decomposed into the products of sign vectors and amplitude vectors; the three-value mode coding is adopted for the sign vectors, the amplitude vectors and the center pixels, and in addition, the rotating unchanged consistency characteristics are respectively calculated; the rotating unchanged consistency characteristics are merged to obtain the final characteristic expression of the training samples; the final characteristic expression of the foundation cloud picture is calculated; and on the basis of the final characteristic expression of the foundation cloud picture and the training samples, the nearest adjacent classifier is adopted to obtain the classification results of the tested foundation cloud picture. The foundation cloud picture classification method has the advantages that the local information of images is considered in three aspects of sign, amplitude and center pixels, the local three-value mode is adopted for coding, and the final coding is carried out to obtain the final characteristic expression of the images, so better noise robustness and classification accuracy can be obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a ground-based cloud image classification method based on a complete local ternary model. Background technique [0002] Clouds are the external manifestations of thermodynamic and dynamic processes in the atmosphere. Their generation and evolution are one of the concrete manifestations of the intricate physical processes that occur in the atmosphere. They not only reflect the movement, stability, and water vapor conditions of the atmosphere at that time, but also can predict the future. The trend of weather changes over a certain period of time. Therefore, cloud observation is an important part of meteorological observation, and accurate acquisition of cloud information is of great significance to weather forecasting, national economy, military support and many other fields. At present, cloud observation is mainly done through ground-based observation and satellite remo...

AI Technical Summary

Problems solved by technology

The above classification methods all extract simple texture features from cloud images, which are obviously not suitable for natural texture images such as ground-based cloud images that are easily disturbed by noise. Classification

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