Ocean frontal surface refined identification method, system and device, terminal and application

Abstract

The invention belongs to the technical field of ocean structure or phenomenon identification and extraction, and discloses a refined automatic identification method, system and device for an ocean frontal surface, a terminal and an application; a deep learning model is established by using the thought of an artificial intelligence convolutional neural network, and a good identification effect is obtained. The method comprises the steps: performing gradient calculation on daily sea temperature data to generate a gradient map; inputting the generated gradient map into a deep learning model, fully learning the features of the ocean front through feature coding and feature decoding, outputting a plurality of types of pixel-level ocean front recognition results, and finally establishing a high-precision ocean front recognition deep learning model. According to the method, information such as pixel-level positions, categories, shapes and trends of various ocean frontal surfaces in the offshore area of China can be automatically recognized, and analysis of spatial and temporal characteristics and evolution modes of various frontal surfaces by ocean scholars is promoted; meanwhile, the method can be extensively applied to the field of refined identification of other types of ocean frontal surfaces such as salinity front and chlorophyll front.

Description

Technical field [0001] The present invention belongs to marine structure or phenomenon identification and extraction technology, and in particular, the present invention relates to a refined refined identification method, system, equipment, terminal and application. Background technique [0002] At present: The ocean front refers to the narrow transition zone between two or several kinds of water bodies in the nature. It is the jumper belt of marine environment parameters. It is important for marine fisheries, underwater sound, ship sailing safety. Significance, while analyzing the evolution of marine experts to analyze the evolution of marine phenomena. In the study of ocean front, identifying the ocean front is its main job, so the improvement of the ocean front recognition algorithm is very important. The mainstream ocean front recognition method is based on a gradient threshold method (grayscale gradient threshold method), first calculate gradient information based on the gra...

AI Technical Summary

Problems solved by technology

To identify ocean fronts based on the gradient method, it is necessary to determine a threshold based on the relevant experience of experts to determine which places in the generated gradient map are fronts and which are not. Ocean fronts are dynamically changing, which makes each ocean front area Both identification and analysis need to find a suitable threshold, which is a time-consuming and labor-intensive process

[0005] (2) The generalization ability of the gradient threshold method is poor

The threshold that the traditional gradient threshold method is suitable for some pictures may not be suitable for other pictures. A threshold can only identify a single-scale ocean front, so it also needs to invest more manpower and time to adjust the threshold, which cannot be achieved. automated identification

[0006] (3) Unstable recognition accuracy

At present, there is no uniform standard for the recognition accuracy of ocean front recognition methods based on gradient threshold method and edge detection method, and in the gradient threshold method, according to the different thresholds, the recognition accuracy of fronts is also very different; the image edge detection algorithm is also Only single-scale frontal information can be extracted

[0007] (4) The existing artificial intelligence-based ocean front recognition algorithm can only judge whether there is an ocean front in the pixel block area, which does not achieve pixel-level accuracy; and it is only for the binary classification recognition of the existence of the ocean front, and cannot Identify different types of fronts; in actual research, the type of ocean fronts is also very important, and there are significant differences in the temporal and spatial characteristics and physical mechanisms of different types of ocean fronts. Therefore, oceanographers are studying the characteristics and evolution of ocean fronts , it is often necessary to analyze the situation of a certain type of front

[0009] (1) The setting of the gradient threshold requires the experience of marine experts, and different thresholds are often required for different ocean fronts in China's offshore waters. The same threshold is used without distinction, and it is difficult to describe the multi-scale frontal characteristics of a specific sea area. It makes the setting of the gradient threshold difficult and difficult to quantify and deal with uniformly

Gradient threshold method depends on the selection of its threshold, and the recognition accuracy will be greatly affected

[0010] (2) In recent years, although the emerging methods based on deep neural networks can reduce the dependence on threshold settings, they can only achieve pixel-level recognition accuracy, and it is difficult to achieve higher-precision pixel-level recognition

[0011] (3) The classification of ocean fronts is determined based on years of research by marine experts, and it is difficult to quantify directly and uniformly. Moreover, the shape and position of ocean fronts will show different characteristics with the change of seasons. There will also be certain changes in shape and position, and it is difficult to give accurate and quantitative classification indicators for the same type of ocean front

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