Terrain constraint and deep learning-based morning fog rapid detection method, apparatus and device, and medium

A technology of deep learning and detection methods, applied in neural learning methods, image analysis, climate sustainability, etc., can solve the problems of combination difficulty and large differences in spectral texture characteristics, achieve less human intervention, and meet the real-time detection of morning and evening fog. Requirements, the effect of not easily misdetected

Pending Publication Date: 2022-08-09
CENT SOUTH UNIV
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Problems solved by technology

Combining DEM with traditional fog detection indicators such as R0.86 / R0.64, R0.86 / R1.6 and BT11-BT3.9 improves the accuracy of cloud and haze and clear sky identification, but due to altitude and terrain The scale and spectral texture characteristics of the scale are quite different, and their combination is difficult

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  • Terrain constraint and deep learning-based morning fog rapid detection method, apparatus and device, and medium
  • Terrain constraint and deep learning-based morning fog rapid detection method, apparatus and device, and medium
  • Terrain constraint and deep learning-based morning fog rapid detection method, apparatus and device, and medium

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Embodiment Construction

[0037] The embodiments of the present invention are described in detail below. This embodiment is carried out on the basis of the technical solutions of the present invention, and provides a detailed implementation manner and a specific operation process, and further explains the technical solutions of the present invention.

[0038] This embodiment provides a rapid detection method for morning and twilight fog based on terrain constraints and deep learning, refer to figure 1 shown, including the following steps:

[0039] Step 1: Obtain DEM data of the study area and H8 / AHI data at dawn and dusk;

[0040] The H8 / AHI data is the remote sensing image data acquired by the AHI sensor (Advanced Himawari Imager) carried by the geostationary meteorological satellite H8 (Himawari-8), referred to as H8 / AHI data.

[0041] The definition of morning and evening time: according to the sun elevation angle (the angle between the incident direction of the sun and the ground plane, which is com...

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Abstract

The invention discloses a quick morning and night fog detection method, device and equipment based on terrain constraint and deep learning and a medium. The method comprises the following steps: step 1, acquiring DEM data of a research area and H8/AHI data at morning and night moments; step 2, preprocessing the data obtained in the step 1: carrying out wave band cutting and useless wave band elimination on the H8/AHI data, and then combining the reserved wave band data and DEM data according to channels to obtain fused AHI-DEM data; and step 3, inputting the AHI-DEM data into a pre-trained morning fog detection model based on deep learning, and outputting to obtain a morning fog detection result of the research area. The terrestrial fog detection method can accurately, efficiently and quickly realize terrestrial fog detection at morning and evening.

Description

technical field [0001] The invention relates to the fields of environmental monitoring and weather forecasting, in particular to a method, device, equipment and medium for rapid detection of morning and twilight fog based on terrain constraints and deep learning. Background technique [0002] Fog, a kind of tiny water droplets suspended in the air, is prone to occur at dawn and dusk, which will have an important impact on visibility and seriously threaten traffic safety; air pollutants are also easily dissolved in fog droplets, affecting people's breathing health; In addition, the fog droplets attached to the surface of the transmission line will form a conductive layer, causing a "pollution flashover" phenomenon, threatening the safety of electricity use. Accurate fog detection methods are an important prerequisite for reducing fog loss and impact. [0003] Traditional terrestrial fog detection mainly relies on the data of meteorological observation stations. Due to the li...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/62G06N3/08G06N3/04
CPCG06T7/0002G06T7/62G06N3/08G06T2207/10024G06T2207/20081G06N3/045Y02A90/10
Inventor 马慧云刘增伟冉印泽李亚楠冯徽徽
Owner CENT SOUTH UNIV
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