CN112834040A - Ground thermal anomaly identification algorithm based on stationary satellite - Google Patents
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Abstract
The invention provides a geostationary satellite-based ground thermal anomaly identification algorithm, which comprises the following steps of: acquiring a pixel brightness DN value and an observation geometric angle data set of a middle infrared channel of a stationary satellite, firstly converting the DN value into a light intensity I, and further converting the light intensity into a radiation bright temperature; observing the surface temperature of the irradiated bright temperature by a satellite, and absorbing atmospheric water vapor and ozone in an infrared bandInfluence is collected; by utilizing the advantage of high time resolution of the static satellite observation to improve the signal-to-noise ratio, the noise signal reduction is directly proportional to ^ N after repeated observation for a plurality of times (the number of times is N)
(ii) a N continuous observation values of a time neighborhood, and the infrared brightness temperature, observation angle, simulated temperature, pressure intensity, wind speed, wind direction, relative humidity, accumulated precipitation, total amount of atmospheric water vapor columns, total amount of ozone columns and earth surface coverage type observed by a stationary satellite in a 5-by-5 grid point area in the space field are obtained. The algorithm provided by the invention is a high-precision thermal anomaly identification algorithm capable of realizing low signal-to-noise ratio and low spatial resolution.
Description
Technical Field
The invention relates to the technical field of satellite thermal anomaly remote sensing monitoring, in particular to a ground thermal anomaly identification algorithm based on a stationary satellite.
Background
The satellite heat anomaly remote sensing can provide real-time monitoring of forest fires, straw burning, factory heat sources and the like. The existing ground thermal anomaly monitoring based on satellite remote sensing is derived from polar orbit satellites which transit once every day or multiple days, and real-time dynamic monitoring with higher time resolution cannot be realized. The high-orbit synchronous satellite is limited by the signal-to-noise ratio and observation angle of the satellite, and the key performance indexes of the conventional thermal anomaly physical inversion algorithm are relatively limited. Therefore, a satellite thermal anomaly monitoring algorithm capable of achieving high time resolution and high precision is needed.
Disclosure of Invention
The invention mainly aims to provide a high-precision thermal anomaly identification algorithm which is suitable for the characteristics of a static (high orbit) satellite and can realize low signal-to-noise ratio and low spatial resolution.
In order to achieve the purpose, the invention adopts the technical scheme that:
the ground thermal anomaly identification algorithm based on the geostationary satellite comprises the following steps:
s1, obtaining pixel brightness DN value and observation geometric angle data set of the middle infrared channel of the stationary satellite, firstly converting DN value into light intensity I,
s2, converting the light intensity into a radiation bright temperature:
wherein alpha and beta are calibration constants, h is a Planck constant, c is an optical speed, k is a Boltzmann constant, and lambda is a central wavelength;
s3, observing and obtaining an earth surface vegetation index NDVI and an earth surface vegetation water content NDWI under the weekly average by using a geostationary satellite;
s4, use the static toiletHigh temporal resolution advantage of star observation to improve signal-to-noise ratio, multiple (N) repetitions of observation with noise signal reduction proportional to ℃ -
；
S5, acquiring N continuous observation values of a time neighborhood, infrared bright temperature observed by a geostationary satellite in a 5 x 5 grid point area in a space field, observation angle, simulated temperature, pressure intensity, wind speed, wind direction, relative humidity, accumulated precipitation, total amount of an atmospheric water vapor column, total amount of an ozone column and a ground surface coverage type;
s6, the model adopts a deep space-time convolution neural network model, and the model structure is as follows:
wherein
The intensity of the fire radiation simulated for the model,
the satellite infrared channel brightness Temperature is soz, the sun zenith angle is raa, the relative azimuth angle is RH, the earth surface relative humidity is RH, the Temperature is the earth surface Temperature, the Wind speed and the Wind direction are Wind, O3The total amount of the ozone column, the Water vapor column, the Precipitation, the NDVI, the NDWI, the DEM and the Land _ use are respectively the total amount of the ozone column, the Water vapor column, the Precipitation, the cumulative Precipitation in five days, the normalized vegetation index, the normalized Water index, the elevation and the Land _ use;
wherein Xinput_layerAs an input layer, Xoutput_layerFor the output layer, Ϝ is the transfer function and θ is the training parameter.
Preferably, in the step S3, it is considered that the brightness temperature of the satellite-observed radiation is affected by the absorption of the surface temperature, the atmospheric water vapor and the ozone in the infrared band; obtaining data such as temperature, pressure, wind speed, wind direction, relative humidity, accumulated precipitation, total amount of a water vapor column, ozone content and the like through WRF-Chem simulation, and re-pointing the data to the same coordinate scale; and using the surface elevation data in view of the satellite observations in relation to the terrain; and to account for different types of surface thermal anomalies: forest fire, grassland fire, straw combustion, factory heat source, explosion and the like adopt high-precision ground surface type data which serve as potential indication basis of thermal anomaly and provide judgment basis for infrared channel radiation bright temperature.
Preferably, in step S4, the space-time background feature and the sudden feature of the thermal anomaly are extracted by using a deep space-time convolution space-time network model, taking into account the signal-to-noise ratio problem of the geostationary satellite observation, not using a simple averaging method, and taking into account the transients that the observed light temperature may be affected by various transient effects and the occurrence of the thermal anomaly.
Preferably, in step S5, the space neighborhood and the time scalar are obtained: surface elevation, surface type, NDVI, NDWI data; the label data adopts a surface heat abnormal fire intensity data set observed by polar orbit satellites such as MODIS, VIIRS and the like.
Preferably, in step S6, more specifically, for each sub-module:
Xoutput_layer(t,t-1,...,t-k)＝ConvLSTM(Xinput_layer(t,t-1,...,t-k))
different time axis lengths k are used for different output layers, k is equal to N, i.e. the input data sequence length, for output layers before the final output layer, and k is equal to 0 for the final output layer.
Loss function:
wherein
In order to predict the result for the model,
is a sample label.
Compared with the prior art, the invention has the following beneficial effects: according to the high-resolution ground thermal anomaly identification algorithm based on the geostationary satellite, a mid-infrared channel of the geostationary satellite is used for observing a surface thermal anomaly fire intensity data set, a built deep space-time convolution neural network model is used, and a sample label is compared with a model prediction result, so that a conclusion is rapidly drawn.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a block diagram of the spatiotemporal residual error of FIG. 1 according to the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
The ground thermal anomaly identification algorithm based on the geostationary satellite comprises the following steps:
s1, obtaining pixel brightness DN value and observation geometric angle data set of the middle infrared channel of the stationary satellite, firstly converting DN value into light intensity I,
s2, converting the light intensity into a radiation bright temperature:
wherein alpha and beta are calibration constants, h is a Planck constant, c is an optical speed, k is a Boltzmann constant, and lambda is a central wavelength;
s3, observing and obtaining an earth surface vegetation index NDVI and an earth surface vegetation water content NDWI under the weekly average by using a geostationary satellite;
s4, repeating observation for multiple times (N times) by using the advantage of high time resolution of the geostationary satellite observation to improve the signal-to-noise ratioThe decrease in noise signal is proportional to ℃
；
S5, acquiring N continuous observation values of a time neighborhood, infrared bright temperature observed by a geostationary satellite in a 5 x 5 grid point area in a space field, observation angle, simulated temperature, pressure intensity, wind speed, wind direction, relative humidity, accumulated precipitation, total amount of an atmospheric water vapor column, total amount of an ozone column and a ground surface coverage type;
s6, the model adopts a deep space-time convolution neural network model, and the model structure is as follows:
wherein
The intensity of the fire radiation simulated for the model,
the satellite infrared channel brightness Temperature is soz, the sun zenith angle is raa, the relative azimuth angle is RH, the earth surface relative humidity is RH, the Temperature is the earth surface Temperature, the Wind speed and the Wind direction are Wind, O3The total amount of the ozone column, the Water vapor column, the Precipitation, the NDVI, the NDWI, the DEM and the Land _ use are respectively the total amount of the ozone column, the Water vapor column, the Precipitation, the cumulative Precipitation in five days, the normalized vegetation index, the normalized Water index, the elevation and the Land _ use;
wherein Xinput_layerAs an input layer, Xoutput_layerFor the output layer, Ϝ is the transfer function and θ is the training parameter.
In this embodiment, in step S3, it is considered that the brightness temperature of the satellite-observed radiation is affected by the absorption of the surface temperature, the atmospheric water vapor and the ozone in the infrared band; obtaining data such as temperature, pressure, wind speed, wind direction, relative humidity, accumulated precipitation, total amount of a water vapor column, ozone content and the like through WRF-Chem simulation, and re-pointing the data to the same coordinate scale; and using the surface elevation data in view of the satellite observations in relation to the terrain; and to account for different types of surface thermal anomalies: forest fire, grassland fire, straw combustion, factory heat source, explosion and the like adopt high-precision ground surface type data which serve as potential indication basis of thermal anomaly and provide judgment basis for infrared channel radiation bright temperature.
In this embodiment, in step S4, the space-time background feature and the bursty feature of the thermal anomaly are extracted by using a deep space-time convolution space-time network model, taking into account the signal-to-noise ratio problem of the geostationary satellite observation, not by using a simple averaging method, but by considering the transients that the observed brightness and temperature may be affected by various transient effects and the occurrence of the thermal anomaly.
In this embodiment, step S5 obtains the spatial neighborhood and the time scalar: surface elevation, surface type, NDVI, NDWI data; the label data adopts a surface heat abnormal fire intensity data set observed by polar orbit satellites such as MODIS, VIIRS and the like.
In the present embodiment, more specifically, in step S6, for each sub-module:
Xoutput_layer(t,t-1,...,t-k)＝ConvLSTM(Xinput_layer(t,t-1,...,t-k))
different time axis lengths k are used for different output layers, k is equal to N, i.e. the input data sequence length, for output layers before the final output layer, and k is equal to 0 for the final output layer.
Loss function:
wherein
In order to predict the result for the model,
is a sample label.
It should be noted that, in practical application, the network model can be used to train historical data of previous years, and the generated training model can be directly used on daily radiation observation data; the obtained thermal abnormal pixels can be divided into types such as forest fire, straw burning, factory heat sources and the like according to the types of the earth surface, data is updated about every 10 minutes, and the calculation time is less than 2 minutes.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. The ground thermal anomaly identification algorithm based on the geostationary satellite is characterized in that: the method comprises the following steps:
s1, obtaining pixel brightness DN value and observation geometric angle data set of the middle infrared channel of the stationary satellite, firstly converting DN value into light intensity I,
I＝α*DN+β
s2, converting the light intensity into a radiation bright temperature:
wherein alpha and beta are calibration constants, h is a Planck constant, c is an optical speed, k is a Boltzmann constant, and lambda is a central wavelength;
s3, observing and obtaining an earth surface vegetation index NDVI and an earth surface vegetation water content NDWI under the weekly average by using a geostationary satellite;
s4, using the advantage of high time resolution of geostationary satellite observation to improve signal-to-noise ratio, repeating the observation for multiple times (N times) to reduce noiseThan in
S5, acquiring N continuous observation values of a time neighborhood, infrared bright temperature observed by a geostationary satellite in a 5 x 5 grid point area in a space field, observation angle, simulated temperature, pressure intensity, wind speed, wind direction, relative humidity, accumulated precipitation, total amount of an atmospheric water vapor column, total amount of an ozone column and a ground surface coverage type;
s6, the model adopts a deep space-time convolution neural network model, and the model structure is as follows:
ypredict＝f(Tb1,Tb2,...,TbN,soz,raa,O3,RH,Temperature,
Wind,Water,Precipitation,NVDI,NDWI,Land_Use,DEM)
wherein y ispredictIntensity of fire radiation, T, simulated for the modelb1,Tb2,...,TbNThe satellite infrared channel brightness Temperature is soz, the sun zenith angle is raa, the relative azimuth angle is RH, the earth surface relative humidity is RH, the Temperature is the earth surface Temperature, the Wind speed and the Wind direction are Wind, O3The total amount of the ozone column, the Water vapor column, the Precipitation, the NDVI, the NDWI, the DEM and the Land _ use are respectively the total amount of the ozone column, the Water vapor column, the Precipitation, the cumulative Precipitation in five days, the normalized vegetation index, the normalized Water index, the elevation and the Land _ use;
Xoutput_layer＝Xinput_layer+F(Xinput_layer,Θ)
wherein Xinput_layerAs an input layer, Xoutput_layerFor the output layer, F is the transfer function and θ is the training parameter.
2. The geostationary satellite based terrestrial thermal anomaly identification algorithm of claim 1, wherein: in the step S3, the influence of the earth surface temperature, the atmospheric water vapor and the absorption of ozone in an infrared band on the satellite observation radiation brightness temperature is considered; obtaining data such as temperature, pressure, wind speed, wind direction, relative humidity, accumulated precipitation, total amount of a water vapor column, ozone content and the like through WRF-Chem simulation, and re-pointing the data to the same coordinate scale; and using the surface elevation data in view of the satellite observations in relation to the terrain; and to account for different types of surface thermal anomalies: forest fire, grassland fire, straw combustion, factory heat source, explosion and the like adopt high-precision ground surface type data which serve as potential indication basis of thermal anomaly and provide judgment basis for infrared channel radiation bright temperature.
3. The geostationary satellite based terrestrial thermal anomaly identification algorithm of claim 1, wherein: in the step S4, considering the problem of signal-to-noise ratio of the geostationary satellite observation, not adopting a simple averaging method, considering that the observed brightness and temperature may be affected by various transient effects and the transient of thermal anomaly occurrence, a deep space-time convolution space-time network model is adopted to extract space-time background features and burst features of thermal anomaly.
4. The geostationary satellite based terrestrial thermal anomaly identification algorithm of claim 1, wherein: in step S5, the space neighborhood and the time scalar are obtained: surface elevation, surface type, NDVI, NDWI data; the label data adopts a surface heat abnormal fire intensity data set observed by polar orbit satellites such as MODIS, VIIRS and the like.
5. The geostationary satellite based terrestrial thermal anomaly identification algorithm of claim 1, wherein: more specifically, in step S6, for each sub-module:
Xoutput_layer(t,t-1,...,t-k)＝ConvLSTM (Xinput_layer(t,t-1,...,t-k))
different time axis lengths k are used for different output layers, k is equal to N, i.e. the input data sequence length, for output layers before the final output layer, and k is equal to 0 for the final output layer.
Loss function:
wherein
In order to predict the result for the model,
is a sample label.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115615938A (en) * | 2022-12-14 | 2023-01-17 | 天津中科谱光信息技术有限公司 | Water quality analysis method and device based on reflection spectrum and electronic equipment |
| CN116341352A (en) * | 2022-07-27 | 2023-06-27 | 南京气象科技创新研究院 | Static satellite land infrared bright temperature simulation method based on earth surface temperature observation information constraint |
| CN116721353A (en) * | 2023-08-07 | 2023-09-08 | 南京大学 | Method for detecting torch by utilizing Sentinel-2 daytime images |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0682405A (en) * | 1992-08-25 | 1994-03-22 | Hitachi Ltd | Abnormality diagnosis of static induction electric appliance |
| CN103344344A (en) * | 2013-07-04 | 2013-10-09 | 武汉珈和科技有限公司 | Global heat abnormal point information remote sensing rapid monitoring and issuing method based on multi-source remote sensing data |
| CN106503480A (en) * | 2016-12-14 | 2017-03-15 | 中国科学院遥感与数字地球研究所 | A kind of fixed statellite fire remote-sensing monitoring method |
| CN108304780A (en) * | 2017-12-29 | 2018-07-20 | 中国电子科技集团公司第二十七研究所 | A kind of crop straw burning fire point remote-sensing monitoring method based on No. three satellites of wind and cloud |
| CN108595150A (en) * | 2018-04-27 | 2018-09-28 | 北京航空航天大学 | The method that artificial intelligence program person writes digital satellite power thermal coupling source program |
| CN109446739A (en) * | 2018-12-20 | 2019-03-08 | 中国农业科学院农业资源与农业区划研究所 | A kind of surface temperature Multi-channel hot infrared remote sensing inversion method |
| CN109580003A (en) * | 2018-12-18 | 2019-04-05 | 成都信息工程大学 | A kind of stationary weather satellite Thermal Infrared Data estimation Air Close To The Earth Surface temperature methods |
| CN111060991A (en) * | 2019-12-04 | 2020-04-24 | 国家卫星气象中心(国家空间天气监测预警中心) | Method for generating clear sky radiation product of wind and cloud geostationary satellite |
| CN112102578A (en) * | 2020-09-16 | 2020-12-18 | 成都信息工程大学 | Forest fire monitoring and early warning system, method, storage medium and computer equipment |
-
2020
- 2020-12-24 CN CN202011550067.4A patent/CN112834040A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0682405A (en) * | 1992-08-25 | 1994-03-22 | Hitachi Ltd | Abnormality diagnosis of static induction electric appliance |
| CN103344344A (en) * | 2013-07-04 | 2013-10-09 | 武汉珈和科技有限公司 | Global heat abnormal point information remote sensing rapid monitoring and issuing method based on multi-source remote sensing data |
| CN106503480A (en) * | 2016-12-14 | 2017-03-15 | 中国科学院遥感与数字地球研究所 | A kind of fixed statellite fire remote-sensing monitoring method |
| CN108304780A (en) * | 2017-12-29 | 2018-07-20 | 中国电子科技集团公司第二十七研究所 | A kind of crop straw burning fire point remote-sensing monitoring method based on No. three satellites of wind and cloud |
| CN108595150A (en) * | 2018-04-27 | 2018-09-28 | 北京航空航天大学 | The method that artificial intelligence program person writes digital satellite power thermal coupling source program |
| CN109580003A (en) * | 2018-12-18 | 2019-04-05 | 成都信息工程大学 | A kind of stationary weather satellite Thermal Infrared Data estimation Air Close To The Earth Surface temperature methods |
| CN109446739A (en) * | 2018-12-20 | 2019-03-08 | 中国农业科学院农业资源与农业区划研究所 | A kind of surface temperature Multi-channel hot infrared remote sensing inversion method |
| CN111060991A (en) * | 2019-12-04 | 2020-04-24 | 国家卫星气象中心(国家空间天气监测预警中心) | Method for generating clear sky radiation product of wind and cloud geostationary satellite |
| CN112102578A (en) * | 2020-09-16 | 2020-12-18 | 成都信息工程大学 | Forest fire monitoring and early warning system, method, storage medium and computer equipment |
Non-Patent Citations (2)
| Title |
|---|
| DIMITAR OUZOUNOV ET AL.: "Satellite thermal IR phenomena associated with some of the major earthquakes in 1999–2003", 《PHYSICS AND CHEMISTRY OF THE EARTH》 * |
| 黄宇飞等: "一种基于机器学习的火点检测算法", 《测绘科学》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116341352A (en) * | 2022-07-27 | 2023-06-27 | 南京气象科技创新研究院 | Static satellite land infrared bright temperature simulation method based on earth surface temperature observation information constraint |
| CN116341352B (en) * | 2022-07-27 | 2023-10-24 | 南京气象科技创新研究院 | Static satellite land infrared bright temperature simulation method based on earth surface temperature observation information constraint |
| CN115615938A (en) * | 2022-12-14 | 2023-01-17 | 天津中科谱光信息技术有限公司 | Water quality analysis method and device based on reflection spectrum and electronic equipment |
| CN116721353A (en) * | 2023-08-07 | 2023-09-08 | 南京大学 | Method for detecting torch by utilizing Sentinel-2 daytime images |
| CN116721353B (en) * | 2023-08-07 | 2023-11-03 | 南京大学 | Method for detecting torch by utilizing Sentinel-2 daytime images |
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