A Texture Feature Extraction and Retrieval Method of Ground-Based Digital Cloud Image

Abstract

The invention discloses a texture feature extraction and retrieval method of a ground-based digital cloud image, including an extraction method and a retrieval method. The extraction method includes: converting a ground-based digital cloud image with three channels of color RGB into a single-channel pixel category map; analyzing the pixel category Figure and establish the co-occurrence matrix to obtain the histogram vector of the co-occurrence matrix; combine the histogram vectors of the co-occurrence matrix of multiple pixel categories to construct the texture feature vector of the ground-based digital cloud image; save the texture feature vector to the cloud image database. The retrieval includes: extracting the texture features of the sample cloud image according to the above-mentioned feature extraction method; sequentially calculating the similarity between the texture features of the sample cloud image and the texture features of each cloud image in the cloud image database; The results show. The invention can automatically analyze and extract the effective texture features of the ground-based digital cloud image, and automatically retrieve the result cloud image similar to the sample cloud image from the cloud image database.

Description

technical field [0001] The invention relates to the fields of meteorological detection technology, digital image processing, image retrieval and pattern recognition, in particular to a texture feature analysis and retrieval method of ground-based digital cloud images. Background technique [0002] In the Earth's atmospheric energy balance, clouds play a very significant role in regulating and are an important factor affecting climate change. On the other hand, the generation, development and evolution of clouds not only reflect the stability, movement and water vapor conditions of the atmosphere at that time, but also one of the important signs foretelling future weather changes. Therefore, cloud observation plays an extremely important role. At present, cloud observations mainly include space-based observations (that is, satellite remote sensing) and ground-based observations. Significant progress has been made in the feature analysis and automatic processing of satellite...

AI Technical Summary

Problems solved by technology

Although the ground-based digital cloud image can be automatically obtained, and the cloud amount can basically be automatically calculated, however, the cloud shape analysis of the ground-based digital cloud image still mainly relies on manual analysis by experienced observers

Obviously, there are many defects in manual analysis: firstly, observers must be very familiar with complex observation rules and be able to use them proficiently; Analysis of ground-based cloud images at different times may produce different feature descriptions; in addition, the flow of forecasters and insufficient continuity of observations will also affect the accuracy of observations, and the same ground-based cloud image is often analyzed by different observers. generate different feature descriptions

Obviously, when the scale of the cloud image database reaches a certain level, the above two methods are not competent

Manually browsing a large-scale cloud image database is time-consuming, labor-intensive, and inefficient; although keyword-based retrieval is relatively efficient, it cannot be ignored that the premise of this method is that each cloud image in the cloud image database has the correct key Words are associated with it, and as far as the current technology is concerned, the machine cannot automatically add the correct keywords to the cloud map, but skilled observers with professional knowledge need to manually add the keyword description of the cloud map

Similarly, manually adding cloud image keywords is time-consuming and labor-intensive. Moreover, the added keywords are highly subjective and inconsistencies are not uncommon.

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