Multi-modal foundation cloud atlas identification method based on depth tensor fusion

Abstract

The embodiment of the invention discloses a multi-modal foundation cloud picture recognition method based on depth tensor fusion, and the method comprises the steps: carrying out preprocessing of an input foundation cloud sample, and obtaining the input of a depth tensor fusion network; transferring the input to a deep tensor fusion network training model, and training to obtain a deep tensor fusion network; extracting fusion feature representation of each input foundation cloud sample; training a support vector machine classifier to obtain a foundation cloud classification model; and obtaining fusion feature representation of a test input foundation cloud sample, and inputting the fusion feature representation into the foundation cloud classification model to obtain a classification result. The method has the capability of learning the foundation cloud visual information and the multi-modal information in a combined manner; according to the method, the visual information and the multi-modal information can be fused at the tensor level, the space content of the visual information can be kept, the complementary information of the visual information and the multi-modal information isfully utilized, the correlation between the visual information and the multi-modal information is effectively mined, fusion features with higher discrimination are extracted, and the accuracy of foundation cloud classification is improved.

Description

technical field [0001] The invention belongs to the technical fields of pattern classification, meteorological science and artificial intelligence, and in particular relates to a multi-mode ground-based cloud image recognition method based on deep tensor fusion. Background technique [0002] Ground-based cloud classification has important implications for understanding weather conditions. Traditional ground-based cloud automatic classification methods mainly extract artificially defined features of ground-based cloud images, such as texture, structure, and color features. However, these artificially defined features are difficult to apply to large-scale databases. [0003] In recent years, convolutional neural networks (CNNs) have achieved remarkable results in wireless sensor networks, computer vision, remote sensing and other fields. These convolutional neural network-based methods can learn features autonomously according to the data distribution. In view of this featur...

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Problems solved by technology

[0006] The purpose of the present invention is to solve the difficult problem of ground-based cloud classification. For this reason, the present invention provides a multi-modal ground-based cloud image recognition method based on depth tensor fusion

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