CN108020322B - Quantitative detection method of airborne thermal infrared hyperspectral remote sensing in coal field fire area - Google Patents
Quantitative detection method of airborne thermal infrared hyperspectral remote sensing in coal field fire area Download PDFInfo
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Abstract
本发明属于遥感探测技术领域，具体涉及一种煤田火区的机载热红外高光谱遥感定量探测方法；本发明的目的是，针对现有技术不足，提供一种解决煤田火区复杂危险环境下无法快速准确地定位煤火燃烧位置和燃烧范围的难题的煤田火区的机载热红外高光谱遥感定量探测方法；包括以下步骤：步骤一：数据获取；步骤二：几何校正和图像均色镶嵌；步骤三：煤火探测波段选择；步骤四：地表温度反演模型；步骤五：热异常信息提取；步骤六：基于发射率特征精确提取煤田火区范围；实现了煤田火区的定量化探测，探测煤火区范围准确，采用的方法步骤简洁，完全可用于高光谱热红外遥感技术的煤田火区探测当中，为煤田火区灭火工程提供实时性支撑数据。
The invention belongs to the technical field of remote sensing detection, and in particular relates to an airborne thermal infrared hyperspectral remote sensing quantitative detection method in a coal field fire area; the purpose of the invention is to provide a solution to the problem of the complex and dangerous environment of the coal field fire area in view of the deficiencies of the prior art. Airborne thermal infrared hyperspectral remote sensing quantitative detection method for coal field fire areas where it is difficult to quickly and accurately locate the burning position and combustion range of coal fires; including the following steps: step 1: data acquisition; step 2: geometric correction and image homogeneous color mosaic ; Step 3: Coal fire detection band selection; Step 4: Surface temperature inversion model; Step 5: Extraction of thermal anomaly information; Step 6: Accurately extract the coal field fire area based on the emissivity feature; Realize the quantitative detection of coal field fire area , the detection range of coal fire area is accurate, and the method and steps used are simple, which can be used in the detection of coal field fire area by hyperspectral thermal infrared remote sensing technology, and provide real-time support data for coal field fire area fire extinguishing project.
Description
Technical Field
The invention belongs to the technical field of remote sensing detection, and particularly relates to an airborne thermal infrared hyperspectral remote sensing quantitative detection method for a coal field fire area.
Background
In China, the reserves of coal resources are the first in the world, but the spontaneous combustion phenomenon of coal fire is very serious, which not only causes huge waste of the coal resources, but also causes great pollution to the environment. The students at home and abroad utilize the remote sensing technology to carry out coal field fire area identification and monitoring, mainly rely on multispectral and medium-low resolution remote sensing images, can only provide information such as fire source position and fire area approximate range qualitatively or semi-quantitatively, and are difficult to meet the requirements of increasing monitoring precision and accuracy of the coal field fire area. The conventional remote sensing coal fire detection method adopts a proper threshold value to perform density segmentation on the temperature inverted according to thermal infrared data so as to achieve the aim of roughly detecting coal fire, and the range of the coal field fire area detected by the method usually comprises non-coal fire temperature abnormal interference ground objects such as water bodies, hot plants and the like, so that the range of the coal field fire area is difficult to accurately extract. How to accurately and effectively extract the coal fire area range by adopting a remote sensing technology is an urgent problem to be solved in coal field fire area treatment.
The appearance of hyperspectral thermal infrared remote sensing provides a solution to the above problems. The hyperspectral thermal infrared remote sensing has three-dimensional information of space, radiation and spectrum, large data volume and rich information. The included feature information of the ground object radiation is more fine. How to utilize the abundant ground feature radiation information to finely distinguish ground feature heat anomaly and finely and quantitatively detect coal fire of a coal field is few in domestic research.
Under the background condition, the invention provides an aviation hyperspectral thermal infrared remote sensing coal fire quantitative detection method. The hyperspectral instrument adopts a TASI airborne hyperspectral sensor developed in Canada, and the spectral range is as follows: 8.0-11.5 mu m, the number of the spectral channels theoretically has infinite separability, the data of the embodiment of the invention has 32 spectral channels, and the dynamic range is 16 bits. The method fully utilizes the technical advantage of multiple hyperspectral thermal radiation channels, realizes efficient and rapid quantitative detection of the fire area of the coal field, and provides real-time basis for work deployment and fire area management and evaluation of fire extinguishing engineering of the coal field.
Disclosure of Invention
The invention aims to provide an onboard thermal infrared hyperspectral remote sensing quantitative detection method for a coal field fire area, which solves the problem that the combustion position and the combustion range of coal fire cannot be quickly and accurately positioned in a complex dangerous environment of the coal field fire area
The technical scheme of the invention is as follows:
an airborne thermal infrared hyperspectral remote sensing quantitative detection method for a coal field fire area comprises the following steps:
the method comprises the following steps: data acquisition
Carrying out laboratory spectrum calibration and radiometric calibration on the sensor by adopting a known laser generator and a black body, setting a low-temperature black body reference source at 0 ℃, setting a high-temperature black body reference source at 100 ℃, and selecting an area with an obvious ground linear target to carry out geometric parameter correction flight on the sensor to obtain correction data; acquiring hyperspectral data according to a designed flight path, and simultaneously, performing ground synchronous temperature measurement by adopting a high-sensitivity infrared radiant spot thermometer to serve as calibration data of the hyperspectral data;
step two: geometric correction and image color-equalizing mosaic;
inputting POS positioning data, original IMU data, DEM data and ground GPS base station data into an ITRES system data processing module, and performing system geometric error correction on aviation hyperspectral thermal infrared data; on the basis, more than 30 control points are selected from the high-precision topographic map for geometric correction, and the irregular geometric error of the flight band caused by topographic relief is eliminated;
determining a reference flight band image as a reference of an output mosaic image, and adjusting the flight band image with chromatic aberration to the reference image by adopting a chromatic order adjusting method as shown in a formula (1) according to the statistical radiation difference of the overlapping area of the adjacent flight band images so as to keep the tone of the adjacent images consistent; finally, a data overlapping area pixel value histogram statistical method is adopted for color homogenizing treatment, and adjacent flight band radiation difference is thoroughly eliminated;
wherein X is the flight band data to be adjusted; r is a radiation adjustment coefficient and is determined according to difference statistics of adjacent image values; n is the original data radiation quantization value, m is the radiation quantization value after data adjustment, and usually m is equal to n;
step three: coal fire detection band selection
Noise statistic analysis is carried out on the hyperspectral thermal infrared data, the wave bands with large strip noise, low signal-to-noise ratio and small difference of surface feature radiation brightness are deleted, the background and the thermal abnormal radiation brightness value with obvious difference and large difference of medium, low and high temperature abnormal radiation brightness values are selected as data participating in surface temperature inversion;
step four: surface temperature inversion model
Primarily estimating the earth surface temperature by adopting an emissivity normalization method, acquiring a regression correction parameter between the estimated earth surface temperature and ground synchronous temperature measurement data by utilizing a least square linear regression method, and performing optimized correction on the estimated temperature by utilizing the correction parameter to improve the temperature inversion precision;
(1) supposing that the obtained hyperspectral thermal infrared data has a maximum emissivity value epsilon in a certain wave bandmaxHere, the preliminary estimated temperature for each wavelength is determined from equation (2) according to Planck's law, without taking into account the ambient radiation:
where L (λ) is the radiance value obtained by the sensor, c1，c2Is Planck constant; epsilonmaxAssigning a value according to the maximum emissivity of the ground object in the image;
(2) taking the maximum value of the temperature of each wavelength as a target estimated temperature:
TM＝max(Tλ) (3)
(3) based on a least square linear regression fitting method, correcting the preliminarily estimated temperature data by using the fitting coefficient of the preliminarily estimated temperature and the ground synchronous measured temperature to eliminate the influence of environmental radiation;
step five: thermal anomaly information extraction
Performing histogram probability statistical analysis according to the inverted temperature image information, and searching a knee point position where the high-frequency temperature is excessive to the low-frequency temperature as a lower limit of a thermal abnormal temperature threshold;
step six: accurate extraction of coal field fire zone range based on emissivity characteristics
The thermal infrared radiation received by the sensor comprises ground object self radiation, atmospheric uplink radiation and reflected radiation of the ground object to the surrounding environment, and under the condition of thermal equilibrium, the ambient radiation reflected by the ground object is mainly atmospheric downlink radiation; based on the lambert of the earth surface, according to kirchhoff's law, the sum of the reflectivity and the emissivity of the earth surface is 1, and the radiation brightness received by the sensor in the thermal infrared hyperspectral band range can be expressed as:
τλis the atmospheric transmission rate, epsilon, between the sensor and the targetλEmissivity of ground object, Bλ(Tc) At a temperature of TcThe black body radiation of (2) can be obtained according to a Planck black body radiation formula;
respectively, the downlink radiation and the uplink radiation of the atmosphere; in No. atWhen synchronous sounding data is provided, analog calculation is performed by using Modtran4
τλ；
Converting the formula (4) to obtain emissivity epsilonλThe calculation formula of (2):
Bλ(Tc)，
τλare integral average values within the bandwidth of the corresponding channel of the sensor; it is noted that the fire area range of the coal field is accurately extracted based on the emissivity characteristics, and all band data of the thermal infrared hyperspectrum participate in the operation, which is different from the coal fire detection band selected in the third step;
analyzing the difference between the interference factors and the ground object emissivity of the coal fire area: the emissivity wave band difference is enhanced by adopting a ratio method, and the difference between a target coal field fire area and interference factors is highlighted; using a threshold segmentation method in bpSeparating the thermal plant from the thermal anomaly in the image to generate a plant thermal anomaly range region of interest; re-selecting threshold value at bwSeparating the water body from the thermal anomaly in the image to generate a water body thermal anomaly range region of interest; finally, combining the thermal plant and the water body thermal abnormal range to generate a thermal plant and water body non-coal fire interference thermal abnormal range region of interest; and taking the generated non-coal-fire-interference heat abnormal range interested region as a mask file to remove the heat abnormal range extracted in the step five, thereby achieving the purpose of accurately extracting the coal field fire region.
The invention has the beneficial effects that:
1. the invention successfully realizes the quantitative detection of the fire area of the coal field;
2. the method has the advantages that the coal fire area detection range is accurate, and the steps of the method are simple and efficient;
3. the method can be used for detecting the fire area of the coal field by the hyperspectral thermal infrared remote sensing technology, and provides real-time support data for fire extinguishing engineering of the fire area of the coal field.
Drawings
FIG. 1 is a flow chart of an airborne thermal infrared hyperspectral remote sensing quantitative detection method for a coal field fire area;
FIG. 2 is a radiation profile of a terrain at different temperatures;
FIG. 3 is a statistical histogram of temperature and frequency in a fire zone
Detailed Description
The invention will be further described with reference to the following figures and examples:
an airborne thermal infrared hyperspectral remote sensing quantitative detection method for a coal field fire area comprises the following steps:
the method comprises the following steps: data acquisition
Carrying out laboratory spectrum calibration and radiometric calibration on the sensor by adopting a known laser generator and a black body, setting a low-temperature black body reference source at 0 ℃, setting a high-temperature black body reference source at 100 ℃, and selecting an area with an obvious ground linear target to carry out geometric parameter correction flight on the sensor to obtain correction data; acquiring hyperspectral data according to a designed flight path, and simultaneously, performing ground synchronous temperature measurement by adopting a high-sensitivity infrared radiant spot thermometer to serve as calibration data of the hyperspectral data;
step two: geometric correction and image color-equalizing mosaic;
inputting POS positioning data, original IMU data, DEM data and ground GPS base station data into an ITRES system data processing module, and performing system geometric error correction on aviation hyperspectral thermal infrared data; on the basis, more than 30 control points are selected from the high-precision topographic map for geometric correction, and the irregular geometric error of the flight band caused by topographic relief is eliminated;
determining a reference flight band image as a reference of an output mosaic image, and adjusting the flight band image with chromatic aberration to the reference image by adopting a chromatic order adjusting method as shown in a formula (1) according to the statistical radiation difference of the overlapping area of the adjacent flight band images so as to keep the tone of the adjacent images consistent; finally, a data overlapping area pixel value histogram statistical method is adopted for color homogenizing treatment, and adjacent flight band radiation difference is thoroughly eliminated;
wherein X is the flight band data to be adjusted; r is a radiation adjustment coefficient and is determined according to difference statistics of adjacent image values; n is the original data radiation quantization value, m is the radiation quantization value after data adjustment, and usually m is equal to n;
step three: coal fire detection band selection
Noise statistic analysis is carried out on the hyperspectral thermal infrared data, the wave bands with large strip noise, low signal-to-noise ratio and small difference of surface feature radiation brightness are deleted, the background and the thermal abnormal radiation brightness value with obvious difference and large difference of medium, low and high temperature abnormal radiation brightness values are selected as data participating in surface temperature inversion;
step four: surface temperature inversion model
Primarily estimating the earth surface temperature by adopting an emissivity normalization method, acquiring a regression correction parameter between the estimated earth surface temperature and ground synchronous temperature measurement data by utilizing a least square linear regression method, and performing optimized correction on the estimated temperature by utilizing the correction parameter to improve the temperature inversion precision;
(1) supposing that the obtained hyperspectral thermal infrared data has a maximum emissivity value epsilon in a certain wave bandmaxHere, the preliminary estimated temperature for each wavelength is determined from equation (2) according to Planck's law, without taking into account the ambient radiation:
where L (λ) is the radiance value obtained by the sensor, c1，c2Is Planck constant; epsilonmaxAssigning a value according to the maximum emissivity of the ground object in the image;
(2) taking the maximum value of the temperature of each wavelength as a target estimated temperature:
TM＝max(Tλ) (3)
(3) based on a least square linear regression fitting method, correcting the preliminarily estimated temperature data by using the fitting coefficient of the preliminarily estimated temperature and the ground synchronous measured temperature to eliminate the influence of environmental radiation;
step five: thermal anomaly information extraction
Performing histogram probability statistical analysis according to the inverted temperature image information, and searching a knee point position where the high-frequency temperature is excessive to the low-frequency temperature as a lower limit of a thermal abnormal temperature threshold;
step six: accurate extraction of coal field fire zone range based on emissivity characteristics
The thermal infrared radiation received by the sensor comprises ground object self radiation, atmospheric uplink radiation and reflected radiation of the ground object to the surrounding environment, and under the condition of thermal equilibrium, the ambient radiation reflected by the ground object is mainly atmospheric downlink radiation; based on the lambert of the earth surface, according to kirchhoff's law, the sum of the reflectivity and the emissivity of the earth surface is 1, and the radiation brightness received by the sensor in the thermal infrared hyperspectral band range can be expressed as:
τλis the atmospheric transmission rate, epsilon, between the sensor and the targetλEmissivity of ground object, Bλ(Tc) At a temperature of TcThe black body radiation of (2) can be obtained according to a Planck black body radiation formula;
respectively, the downlink radiation and the uplink radiation of the atmosphere; under the condition of not having synchronous sounding data, the Modtran4 is utilized to simulate calculation
τλ；
Converting the formula (4) to obtain emissivity epsilonλThe calculation formula of (2):
Bλ(Tc)，
τλare integral average values within the bandwidth of the corresponding channel of the sensor; it is noted that the fire area range of the coal field is accurately extracted based on the emissivity characteristics, and all band data of the thermal infrared hyperspectrum participate in the operation, which is different from the coal fire detection band selected in the third step;
analyzing the difference between the interference factors and the ground object emissivity of the coal fire area: the emissivity wave band difference is enhanced by adopting a ratio method, and the difference between a target coal field fire area and interference factors is highlighted; using a threshold segmentation method in bpSeparating the thermal plant from the thermal anomaly in the image to generate a plant thermal anomaly range region of interest; re-selecting threshold value at bwSeparating the water body from the thermal anomaly in the image to generate a water body thermal anomaly range region of interest; finally, combining the thermal plant and the water body thermal abnormal range to generate a thermal plant and water body non-coal fire interference thermal abnormal range region of interest; and taking the generated non-coal-fire-interference heat abnormal range interested region as a mask file to remove the heat abnormal range extracted in the step five, thereby achieving the purpose of accurately extracting the coal field fire region.
Examples
The flow of the airborne thermal infrared hyperspectral remote sensing quantitative detection method for the coal field fire area is shown in figure 1, and the method comprises the following steps:
the method comprises the following steps: data acquisition
The known laser generator and the black body are adopted to carry out laboratory spectrum calibration and radiometric calibration on the sensor, the reference source of the low-temperature black body is set to be 0 ℃, the reference source of the high-temperature black body is set to be 100 ℃, and the identifiability of low-temperature and high-temperature ground objects is ensured. And selecting an area with an obvious ground linear target to carry out sensor geometric parameter correction flight, and acquiring correction data. And acquiring hyperspectral data according to the designed flight path, and simultaneously, performing ground synchronous temperature measurement by adopting a high-sensitivity infrared radiant spot thermometer to serve as calibration data of the hyperspectral data.
Step two: geometric correction and image color-mean-mosaic.
And inputting the POS positioning data, the original IMU data, the DEM data and the ground GPS base station data into an ITRES system data processing module, and performing system geometric error correction on the aviation hyperspectral thermal infrared data. On the basis, a sufficient number of control points are selected from the high-precision topographic map to carry out geometric correction, and the irregular geometric error of the flight band caused by topographic relief is eliminated.
Determining a reference aerial image as the reference of the output mosaic image, and adjusting the aerial image with chromatic aberration to the reference image by adopting a chromatic order adjustment method (formula 1) according to the statistical radiation difference of the overlapping area of the adjacent aerial images so as to keep the tone of the adjacent images consistent. And finally, carrying out color homogenizing treatment by adopting a data overlapping area pixel value histogram statistical method, and thoroughly eliminating adjacent flight band radiation difference.
X is the flight band data to be adjusted; r is a radiation adjustment coefficient and is determined according to difference statistics of adjacent image values; n is the original data radiation quantization value, m is the data adjusted radiation quantization value, and m is n.
Step three: coal fire detection band selection
Noise statistic analysis is carried out on thermal infrared hyperspectral data, strip noise with equal intervals of 29-32 wave bands of data in an embodiment is large, the signal to noise ratio is low, and the difference of radiation brightness of ground objects at different temperatures is small. The high-temperature abnormality at the temperature of more than 200 ℃ is overlapped and crossed with the high-temperature abnormal radiation brightness at the temperature of 155-195 ℃ in a wave band of 17-28 ℃; in the 1-16 wave band, the background and the heat abnormal radiation brightness value have obvious difference, and the difference of the medium, low and high temperature abnormal radiation brightness values is large, so that the difference is easy to distinguish (figure 2). The wave bands of 1 to 16 are the optimal wave bands for detecting abnormal coal fire information.
Step four: surface temperature inversion model
(1) Supposing that the obtained hyperspectral thermal infrared data has maximum emissivity in a certain wave bandValue epsilonmaxHere, the preliminary estimated temperature for each wavelength is determined from equation (2) according to Planck's law, without taking into account the ambient radiation:
where L (λ) is the radiance value obtained by the sensor, c1，c2Is Planck constant. EpsilonmaxAnd assigning a value according to the maximum emissivity of the ground object in the image.
(2) Taking the maximum value of the temperature of each wavelength as a target estimated temperature:
TM＝max(Tλ) (3)
(3) and correcting the preliminarily estimated temperature data by using the fitting coefficient of the preliminarily estimated temperature and the ground synchronous measured temperature based on the least square linear regression fitting method, so as to eliminate the influence of environmental radiation.
Step five: thermal anomaly information extraction
Histogram probability statistical analysis is carried out on the inversion temperature data of the thermal infrared image, and the position of an inflection point (figure 3) at which the high-frequency temperature is excessive to the low-frequency temperature is searched and used as the lower limit of the thermal abnormal temperature threshold value, so that the thermal abnormal ground object is separated from the background.
Step six: accurate extraction of coal field fire zone range based on emissivity characteristics
Different ground objects have different properties such as composition, surface structure and the like, and have different emissivity. The interference factors in the thermal anomaly of the difference of the ground object emissivity are utilized.
The thermal infrared radiation received by the sensor comprises ground object self radiation, atmospheric uplink radiation and reflected radiation of the ground object to the surrounding environment, and under the thermal equilibrium condition, the ambient radiation reflected by the ground object is mainly the atmospheric downlink radiation. Based on the lambert of the earth surface, according to kirchhoff's law, the sum of the reflectivity and the emissivity of the earth surface is 1, and the radiation brightness received by the sensor in the thermal infrared hyperspectral band range can be expressed as:
τλis the atmospheric transmission rate, epsilon, between the sensor and the targetλEmissivity of ground object, Bλ(Tc) At a temperature of TcThe black body radiation of (2) can be obtained according to a Planck black body radiation formula;
respectively, the down radiation and the up radiation of the atmosphere. Under the condition of not having synchronous sounding data, the Modtran4 is utilized to simulate calculation
τλ。
Converting the formula (4) to obtain emissivity epsilonλThe calculation formula of (2):
Bλ(Tc)，
τλare the integrated average values over the bandwidth of the corresponding channel of the sensor. It should be noted that the fire area range of the coal field is accurately extracted based on the emissivity characteristics, and all band data of the thermal infrared hyperspectrum participate in the operation, which is different from the coal fire detection band selected in the third step.
Analyzing the difference between interference factors (water body and factory building) and the emissivity of ground objects in the coal fire area: the emissivity of the embodiment data adopted by the invention is obviously lower in the emissivity of water body in the 23-25 wave band and obviously lower in the emissivity of hot plant in the 9-12 wave band. The differences are small in value, and the invention adopts a ratio method to enhance the differences of the emissivity wave bands (6 and 7 formulas) and highlights the differences between the target coal field fire area and the interference factors. Using a threshold segmentation method in bpAnd separating the hot plant from the thermal anomaly in the image to generate a plant thermal anomaly range region of interest. Re-selection ofTaking the threshold value as bwAnd separating the water body from the thermal anomaly in the image to generate a water body thermal anomaly range region of interest. And finally, combining the thermal plant and the water body thermal abnormal range to generate an interested region of the thermal plant and the water body non-coal fire interference thermal abnormal range. And taking the generated non-coal-fire-interference heat abnormal range interested region as a mask file to remove the heat abnormal range extracted in the step five, thereby achieving the purpose of accurately extracting the coal field fire region.
bp＝e10*b9(6)
bw＝e10*b24(7)
b9Emissivity data for the 9 th band, b24The emissivity data of the 24 th wave band.
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| CN108983309B (en) * | 2018-08-02 | 2020-05-26 | 中国矿业大学 | Method for detecting coal fire by combining thermal infrared and radar remote sensing |
| CN109509209B (en) * | 2018-11-15 | 2023-08-15 | 上海卫星工程研究所 | Analysis method for detecting air moving target in sea-air environment by utilizing hyperspectral technology |
| CN109598431A (en) * | 2018-11-28 | 2019-04-09 | 西安工程大学 | Solar energy resources power generation potential evaluation method based on Surface Temperature Retrieval |
| CN109901240A (en) * | 2019-04-03 | 2019-06-18 | 中国矿业大学 | A method for detecting spontaneous combustion area of outcrop residual coal |
| CN111563957B (en) * | 2020-05-06 | 2023-03-31 | 中国矿业大学 | Three-dimensional temperature field digital imaging method for coal field fire and gangue dump fire |
| CN113834572B (en) * | 2021-08-26 | 2023-05-12 | 电子科技大学 | Unmanned aerial vehicle non-refrigeration type thermal imager temperature measurement result drift removal method |
| CN115900958A (en) * | 2022-12-06 | 2023-04-04 | 中国自然资源航空物探遥感中心 | Geothermal region judgment system and method based on thermal infrared hyperspectral remote sensing |
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| CN102879105B (en) * | 2012-09-28 | 2015-06-10 | 中国神华能源股份有限公司 | Method and device for monitoring coal fire in mining area and data processing equipment |
| CN103559497A (en) * | 2013-10-30 | 2014-02-05 | 中国神华能源股份有限公司 | Coal field fire area information extracting method and device |
| CN103760619A (en) * | 2014-01-07 | 2014-04-30 | 中国神华能源股份有限公司 | Method and device for monitoring coal field fire zone |
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