CN112146765A - Emissivity measuring method under non-isothermal condition - Google Patents
Emissivity measuring method under non-isothermal condition Download PDFInfo
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- 238000004364 calculation method Methods 0.000 abstract description 3
- 238000002329 infrared spectrum Methods 0.000 abstract description 3
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/007—Radiation pyrometry, e.g. infrared or optical thermometry for earth observation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/52—Radiation pyrometry, e.g. infrared or optical thermometry using comparison with reference sources, e.g. disappearing-filament pyrometer
- G01J5/53—Reference sources, e.g. standard lamps; Black bodies
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/80—Calibration
- G01J5/802—Calibration by correcting for emissivity
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Abstract
The invention discloses a method for measuring emissivity under a non-isothermal condition, which specifically comprises the following three steps: infrared spectrum measurement, a sliding window-based emissivity calculation method and a synthetic multi-section emissivity curve. The invention provides a non-isothermal emissivity measuring and calculating method, which uses a sliding window with a certain width to respectively carry out cyclic processing on spectral data to calculate the emissivity in the sliding window, avoids spectral deviation caused by non-isothermal emissivity through the setting of the sliding window, and reduces emissivity estimation error; in addition, the suppression of the spectral deviation is realized through the sliding window, and the estimation precision of the emissivity is improved; the invention can realize high-precision measurement of the emissivity of the non-isothermal natural earth surface and has better application value.
Description
Technical Field
The invention relates to an emissivity measuring method, belongs to the technical field of remote sensing, and particularly relates to an emissivity measuring method under a non-isothermal condition.
Background
Emissivity measurement is always a research hotspot concerned by thermal infrared quantitative remote sensing. And measuring the reflectivity by using an integrating sphere based on kirchhoff's law in a laboratory so as to obtain the emissivity. Under field conditions, researchers have developed methods for measuring surface emissivity using a bucket cover to make artificial pseudo-blackbodies. With the popularization of the infrared hyperspectral technology, the hyperspectral emissivity extraction algorithm represented by a spectrum smoothing algorithm greatly improves the emissivity measurement precision. In recent years, the research on emissivity has focused on two directions, namely, the improvement of a measuring method; and secondly, modeling and correcting research on emissivity directivity. Due to the development of quantitative remote sensing, the precision requirement on emissivity measurement is higher and higher. Researchers have begun to propose various improved algorithms for methods of emissivity measurement. A set of innovative emissivity measurement and inversion algorithms emerges: such as correlation algorithm CBTES, linear regression constraint algorithm, stepwise refinement algorithm, local separation algorithm, etc. Unfortunately, these algorithms do not take into account the effects of non-homothermy.
From the spectral distribution of the radiance, non-homothermy causes the radiance of each spectral band to change. JeffDozier discussed this phenomenon first. Chengjie et al analyzed the effect of non-isothermal on emissivity measurements using simulated data. We use liquid nitrogen refrigeration method to artificially construct non-isothermal target, and measure the spectrum deviation under non-isothermal condition. Similar explanations have been given for the phenomenon of non-isothermal induced spectral shifts in John m. The peak of the blackbody radiation spectrum varies with temperature due to the nonlinearity of the planck function. The equivalent black body radiation energy measured on a non-isothermal surface is equivalent to the superposition of a plurality of Planck functions, the black bodies have consistent temperature distribution with the ground, and the radiation spectrum of the single Planck function has spectral deviation. In practice it is difficult to estimate the temperature distribution of the earth's surface and therefore to give a black body radiation that is the same as the temperature distribution of the earth's surface. In practical application, the influence caused by non-homothermal is not considered in all the current emissivity measuring methods.
Disclosure of Invention
In order to solve the defects of the technology, the invention provides a method for measuring emissivity under the condition of non-isothermal condition.
In order to solve the technical problems, the invention adopts the technical scheme that: a method for measuring emissivity under non-isothermal conditions comprises the following steps:
s1, measuring ground-leaving radiation of the non-isothermal ground surface by using an infrared spectrometer;
s2, measuring sky downward radiation by using an infrared spectrometer;
s3, starting from the ith spectrum (first time, i equals 0), opening a data sliding window with a certain spectrum width;
s4, substituting the data in the data window into a spectrum smoothing iterative algorithm, and calculating the temperature and emissivity in the window;
s5, increasing i in S3 by 1, that is, i is i +1, repeating S3 and S4 until the whole spectral range is traversed, obtaining a multi-segment emissivity spectral curve;
and S6, synthesizing the multiple sections of emissivity spectrum curves obtained in the S5 into one emissivity spectrum curve according to a method of averaging the same spectrum positions.
Further, the infrared spectrometer was set to set the spectral resolution of the surface and sky measurements to 1cm-1。
Further, the infrared spectrometer measures the earth surface by the following steps: and vertically aligning a lens of the infrared spectrometer to a target earth surface to be measured, collecting data, and using a black body for radiometric calibration.
Further, the step of measuring the sky by the infrared spectrometer comprises the following steps: the lens of the infrared spectrometer is inclined at a zenith angle of 53 degrees to measure the sky and collect data, and a black body is used for radiometric calibration.
Further, the width of the sliding window was set to 50cm-1。
Further, the specific operation of the spectral smoothing iterative algorithm is as follows: assuming that the emissivity of each wave band is 1, calculating the temperature of each wave band according to a Planck inverse formula; selecting the mostHigh temperature T0In the temperature range [ T ]0-t,T0+t]Dividing the interior of the array into 100 parts to obtain an array; and substituting each temperature value into an equation to calculate a group of emissivity curves, calculating a smoothness index of the emissivity curves, taking the temperature with the minimum smoothness index as a true value, and taking the group of emissivity as a real emissivity.
Further, t has a value of 5.
The invention provides a non-isothermal emissivity measuring and calculating method; the invention realizes the suppression of the spectral deviation and improves the estimation precision of the emissivity through the sliding window, and the invention can realize the high-precision measurement of the emissivity of the non-isothermal natural earth surface and has better application value.
Drawings
FIG. 1 is a schematic flow chart of a method for measuring a refractive index under a non-isothermal condition according to the present invention.
FIG. 2 is a schematic diagram of an inversion error of emissivity caused by a spectrum deviation in the ISSTES algorithm.
FIG. 3 is a schematic diagram of an inversion error of emissivity due to spectral bias in the present invention.
FIG. 4 is a diagram illustrating the temperature variation with wavelength obtained by inversion in the present invention.
FIG. 5 is a graph showing the emissivity obtained by the ISSTES algorithm at three specified temperature points.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 shows a method for measuring emissivity under non-isothermal conditions, comprising the steps of:
step one, infrared spectrum measurement: the method comprises the following steps of measuring ground surface off-ground radiation and sky down-going radiation respectively by using an infrared spectrometer to obtain an off-ground infrared radiation spectrum and an infrared spectrum of sky down-going radiation, and specifically comprises the following steps:
s1, measuring the ground-borne radiation of the non-isothermal ground surface by using an infrared spectrometer, vertically aligning a lens of the infrared spectrometer to the target ground surface to be measured, collecting data, and calibrating the radiation by using a black body;
s2, measuring the downward radiation of the sky by using an infrared spectrometer, measuring the sky by inclining a lens of the infrared spectrometer at a zenith angle of 53 degrees and collecting data, and using a black body for radiometric calibration;
the spectral resolution of the infrared spectrometer for surface and sky measurement is 1cm-1；
Step two, an emissivity calculation method based on a sliding window: an original working window is modified into small sliding windows by modifying a data processing flow frame of a spectrum smoothing iterative algorithm (ISSTES), and the ISSTES algorithm is carried out in each small sliding window (spectrum range) to obtain a local emissivity/temperature value at the spectrum position. By moving the sliding window, the emissivity spectral curve and the temperature spectral curve over the entire spectral band are obtained. The specific improvement scheme of the inversion algorithm is shown in fig. 1, and the specific steps are as follows:
s3, starting from the ith spectrum (i is 0 in the first time), a data sliding window is opened with a certain spectrum width, and the width of the sliding window is set to 50cm-1；
S4, substituting the data in the data window into a spectrum smoothing iterative algorithm, and calculating the temperature and emissivity in the window, wherein the spectrum smoothing iterative algorithm specifically comprises the following operations: assuming that the emissivity of each wave band is 1, calculating the temperature of each wave band according to a Planck inverse formula; selecting the highest temperature T therein0In the temperature range [ T ]0-t,T0+t]Dividing the interior of the sample into 100 parts to obtain an array, wherein the value of t is 5; substituting each temperature value into an equation, calculating a group of emissivity curves, calculating a smoothness index of the emissivity curves, taking the temperature with the minimum smoothness index as a true value, and taking the group of emissivity as a real emissivity;
s5, increasing i in S3 by 1, that is, i is i +1, repeating S3 and S4 until the whole spectral range is traversed, obtaining a multi-segment emissivity spectral curve;
in the present invention, the width of the working window is a key parameter. If the window is too wide, the spectral deviation of the emissivity cannot be corrected with high precision, and if the window is too narrow, the downward radiation spectrum section of the atmosphere contained in the window is too small, so that enough effective information required by an iterative algorithm cannot be provided, and the inversion error is too large. Therefore, the contradiction between the spectrum deviation correction and the precision of the iterative inversion algorithm needs to be balanced;
step three, synthesizing a multi-section emissivity curve: and step two, obtaining a series of emissivity curves, wherein one emissivity curve needs to be obtained through a synthesis algorithm. The synthesis algorithm is realized by taking the average value of all estimation results based on the same spectrum position, and comprises the following specific steps:
and S6, synthesizing the multiple sections of emissivity spectrum curves obtained in the S5 into one emissivity spectrum curve according to a method of averaging the same spectrum positions.
Compared with the existing isses algorithm, under the condition of same temperature, the isses algorithm can obtain very high precision, as shown in a red line segment of fig. 2, when the component temperatures of the two halves in the pixel are both set to 300K, the inversion precision is about 0.00001, however, as the difference between the two component temperatures increases, the error caused by the spectral deviation gradually increases.
As shown in fig. 3, for the inversion result obtained by the calculation method of the present invention, it can be seen from fig. 3 that the inversion result of the emissivity of the present invention maintains good stability and very high inversion accuracy under different temperature combinations. Therefore, the novel algorithm provided by the invention has a good effect of eliminating the emissivity error caused by the spectral deviation. The reason is that in the present invention, the spectral deviation is assigned to the temperature.
As shown in fig. 4-5, the schematic diagram of the temperature variation with wavelength obtained by inversion in the present invention and the schematic diagram of the emissivity obtained by the isses algorithm at three designated temperature points are respectively shown, and it is assumed that all the wavelengths have only one temperature in the isses algorithm, as shown in fig. 5, three temperatures T1, T2, and T3 are true temperatures, and three emissivity curves are obtained by respectively using the isses algorithm, and it can be seen that the three emissivity curves are close to the true value only at three positions and have obvious deviations at other wavelength positions. Therefore, only by giving up the assumption that the temperature does not change with wavelength, a correct emissivity inversion result can be obtained.
The invention provides a non-isothermal emissivity measuring and calculating method, which uses a sliding window with a certain width to respectively carry out cyclic processing on spectral data to calculate the emissivity in the sliding window, avoids spectral deviation caused by non-isothermal emissivity through the setting of the sliding window, and reduces emissivity estimation error; in addition, the invention realizes the suppression of the spectral deviation through the sliding window and improves the estimation precision of the emissivity. The invention can realize high-precision measurement of the emissivity of the non-isothermal natural earth surface and has better application value.
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the technical scope of the present invention.
Claims (7)
1. The emissivity measuring method under the non-isothermal condition is characterized by comprising the following steps of:
s1, measuring ground-leaving radiation of the non-isothermal ground surface by using an infrared spectrometer;
s2, measuring sky downward radiation by using an infrared spectrometer;
s3, starting from the ith spectrum (first time, i equals 0), opening a data sliding window with a certain spectrum width;
s4, substituting the data in the data window into a spectrum smoothing iterative algorithm, and calculating the temperature and emissivity in the window;
s5, increasing i in S3 by 1, that is, i is i +1, repeating S3 and S4 until the whole spectral range is traversed, obtaining a multi-segment emissivity spectral curve;
and S6, synthesizing the multiple sections of emissivity spectrum curves obtained in the S5 into one emissivity spectrum curve according to a method of averaging the same spectrum positions.
2. The method of claim 1 for emissivity measurement under non-isothermal conditions, wherein: the spectral resolution of the surface and sky measurements set by the infrared spectrometer is set to1cm-1。
3. The method of claim 2, wherein the emissivity is measured at a non-isothermal condition: the infrared spectrometer comprises the following steps of: and vertically aligning a lens of the infrared spectrometer to a target earth surface to be measured, collecting data, and using a black body for radiometric calibration.
4. The method of claim 2, wherein the emissivity is measured at a non-isothermal condition: the step of measuring the sky by the infrared spectrometer is as follows: the lens of the infrared spectrometer is inclined at a zenith angle of 53 degrees to measure the sky and collect data, and a black body is used for radiometric calibration.
5. The method of claim 1 for emissivity measurement under non-isothermal conditions, wherein: the width of the sliding window is set to 50cm-1。
6. The method of claim 1 for emissivity measurement under non-isothermal conditions, wherein: the specific operation of the spectrum smoothing iterative algorithm is as follows: assuming that the emissivity of each wave band is 1, calculating the temperature of each wave band according to a Planck inverse formula; selecting the highest temperature T therein0In the temperature range [ T ]0-t,T0+t]Dividing the interior of the array into 100 parts to obtain an array; and substituting each temperature value into an equation to calculate a group of emissivity curves, calculating a smoothness index of the emissivity curves, taking the temperature with the minimum smoothness index as a true value, and taking the group of emissivity as a real emissivity.
7. The method of claim 6, wherein the emissivity is measured at a non-isothermal condition: the value of t is 5.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8532958B2 (en) * | 2010-08-06 | 2013-09-10 | Raytheon Company | Remote identification of non-lambertian materials |
| CN110567591A (en) * | 2019-09-25 | 2019-12-13 | 核工业北京地质研究院 | Temperature/emissivity inversion method suitable for ground thermal infrared data |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8532958B2 (en) * | 2010-08-06 | 2013-09-10 | Raytheon Company | Remote identification of non-lambertian materials |
| CN110567591A (en) * | 2019-09-25 | 2019-12-13 | 核工业北京地质研究院 | Temperature/emissivity inversion method suitable for ground thermal infrared data |
Non-Patent Citations (2)
| Title |
|---|
| YONGMIND DU: "《A A Modified Interactive Spectral Smooth Temperature Emissivity Separation Algorithm for Low-Temperature Surface》", 《IEEE TRANSACTIONS OF GEOSCIENCE AND REMOTE SENSING》 * |
| 程洁等: "《非同温对平面混合像元温度发射率分离的影响分析》", 《大气与环境光学学报》 * |
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