Foundation cloud picture classification method based on heterogeneous feature fusion network

Abstract

The invention discloses a foundation cloud picture classification method based on a heterogeneous feature fusion network. The foundation cloud picture classification method comprises the following steps: (1) preprocessing a plurality of foundation cloud pictures with noise; (2) features of the processed foundation cloud images are extracted respectively, and a manual feature extraction method is combined with Fisher vector coding to obtain a feature vector corresponding to each foundation cloud image; (3) inputting the output of the step (2) into a four-layer full connection layer network, wherein the output of the network is recorded as fc; and (4) after the training set is amplified, training a convolutional neural network model, fusing fc with the deep semantic feature fg obtained by the last pooling layer, and obtaining a classification probability corresponding to each class through a full connection layer. According to the method, the generalization ability of a foundation cloudpicture classification and recognition task can be remarkably improved, the robustness of the model is high, visual information is combined from multiple angles, the cloud shape can be accurately positioned even if noise is manually added, and a good recognition result is obtained.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a ground-based cloud image classification method based on a heterogeneous feature fusion network. Background technique [0002] In the field of meteorological research, comprehensive and consistent observation of clouds is very important. In meteorological forecasting, macroscopic parameters such as cloud amount and cloud type play a crucial role. Classification methods for ground-based cloud images have been extensively studied in recent decades. Traditional cloud classification methods rely on expert experience, the method is unreliable, time-consuming, and to some extent depends on the operator's experience, and the classification results usually have some uncertainty and bias. In addition, human eye observation has gradually become more costly. Therefore, an automatic and accurate cloud classification method is urgently needed. [0003] In recent years, many i...

AI Technical Summary

Problems solved by technology

Traditional cloud classification methods rely on expert experience, the method is unreliable, time-consuming, and to some extent depends on the operator's experience, the classification results usually have some uncertainty and bias

In addition, human eye observation has gradually become more costly

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