Fire point detection method for Himawari-8 remote sensing data

A technology of remote sensing data and detection methods, applied in the direction of neural learning methods, instruments, biological neural network models, etc., can solve the problems of low fire detection accuracy, many false positives and missing fire points, threshold algorithm can not be applied, etc., to achieve High accuracy, auxiliary early warning and prevention and control, and easy-to-achieve effects

Active Publication Date: 2021-07-09
NO 54 INST OF CHINA ELECTRONICS SCI & TECH GRP
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Problems solved by technology

However, the fire detection accuracy of these algorithms is low, especially for small-scale fires or low-temperature fires, there are many false alarms and missed fire points, and a high false

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  • Fire point detection method for Himawari-8 remote sensing data
  • Fire point detection method for Himawari-8 remote sensing data
  • Fire point detection method for Himawari-8 remote sensing data

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Embodiment Construction

[0030] The technical solutions of the present invention will be described more clearly and completely below in conjunction with the accompanying drawings and specific embodiments.

[0031] Such as figure 1 As shown, a fire point detection method for Himawari-8 remote sensing data, including the following steps:

[0032] Step 1. Extract the data block.

[0033] Firstly, select the bands of the obtained Himawari-8 satellite remote sensing data. In this example, the bands selected are the 3rd, 4th, 6th, 7th, 14th, and 15th bands, and obtain the SOZ (solar zenith angle) and longitude and latitude data.

[0034] Among them, the 3rd band is the albedo_03 attribute, which is the albedo band, which is more sensitive to elements such as land and clouds; the 4th band is the albedo_04 band, which is the reflectance band, which is more sensitive to elements such as ocean and water; the 6th band is the albedo_06 band , is the reflectance band, which is more sensitive to the detection of...

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Abstract

The invention discloses a fire point detection method of Himawari-8 remote sensing data, and belongs to the technical field of fire point detection of remote sensing data. The method comprises the following steps: preprocessing Himawari-8 satellite remote sensing data to obtain pixels and neighborhoods thereof, and making a data set; building a double-input convolutional neural network model, and inputting the radiation temperature wave band and the reflectivity wave band into the network; training a deep learning model by using the data set, iteratively updating network model parameters according to the change condition of a loss function, and converging to obtain an optimal network model; and applying the obtained model to fire point detection of data in a test set. Compared with a traditional context relative threshold algorithm, the method provided by the invention can automatically carry out fire point judgment, and can effectively improve the accuracy of fire point detection of remote sensing data.

Description

technical field [0001] The invention relates to the technical field of remote sensing data fire point detection, in particular to a fire point detection method for remote sensing data of Himawari-8 (that is, Sunflower-8 meteorological satellite). Background technique [0002] In recent years, fires in forests, farmland, pastures and other places on the earth have become more frequent. Recent serious fire events include the California fire in November 2018, the Amazon forest fire in Brazil in August 2019, and the Australian bushfire that lasted from 2019 to 2010. In addition, fires occur periodically in parts of Africa and Europe every year. [0003] The occurrence of fire may be caused by the natural combustion of objects in the ecological environment, or it may be caused by human deliberation or negligence. Fires will produce a lot of smoke and harmful gases, and release a lot of carbon dioxide, seriously polluting the air environment; at the same time, it will cause dama...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/047G06N3/048G06N3/045G06F18/214
Inventor 帅通王岳环陈金勇徐小刚王士成李子文王港单子力薛辉刘宇
Owner NO 54 INST OF CHINA ELECTRONICS SCI & TECH GRP
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