Method for extracting and retrieving textural features from ground digital nephograms

Abstract

The invention discloses a method for extracting and retrieving textural features from ground digital nephograms, which comprises an extracting method and a retrieving method. The extracting method comprises the following steps: converting a RGB three-channel ground digital nephogram into single-channel pixel class diagrams; analyzing the pixel class diagrams and establishing co-occurrence matrixes to obtain histogram vectors of the co-occurrence matrixes; merging the histogram vectors of the pixel class co-occurrence matrixes to establish textural feature vectors of the ground digital nephogram; and storing the textural feature vectors into the nephogram database. The retrieving method comprises the following steps: extracting the textural features of the sample nephogram according to the feature extracting method; orderly calculating the similarity between the textural features of the sample nephogram and the textural features of every nephogram in the nephogram database; and displaying the most similar nephograms as retrieval results. The invention can automatically analyze and extract the effective textural features of the ground digital nephogram, and automatically retrieve the result nephograms similar to the sample nephogram from the nephogram 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. On the contrary, 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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