CN110887563B - Hyperspectral area array detector bad element detection method - Google Patents
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- 238000001514 detection method Methods 0.000 title claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 14
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 239000000523 sample Substances 0.000 claims description 14
- 238000003384 imaging method Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 238000010186 staining Methods 0.000 abstract description 2
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- 101001021281 Homo sapiens Protein HEXIM1 Proteins 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
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- JBRZTFJDHDCESZ-UHFFFAOYSA-N AsGa Chemical compound [As]#[Ga] JBRZTFJDHDCESZ-UHFFFAOYSA-N 0.000 description 1
- DGJPPCSCQOIWCP-UHFFFAOYSA-N cadmium mercury Chemical compound [Cd].[Hg] DGJPPCSCQOIWCP-UHFFFAOYSA-N 0.000 description 1
- 238000005266 casting Methods 0.000 description 1
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- 239000000428 dust Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 229910052738 indium Inorganic materials 0.000 description 1
- APFVFJFRJDLVQX-UHFFFAOYSA-N indium atom Chemical compound [In] APFVFJFRJDLVQX-UHFFFAOYSA-N 0.000 description 1
- 210000003127 knee Anatomy 0.000 description 1
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- 238000013139 quantization Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 229910052714 tellurium Inorganic materials 0.000 description 1
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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
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J3/2823—Imaging spectrometer
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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
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J2003/2866—Markers; Calibrating of scan
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Abstract
The invention discloses a method for detecting a bad element of a hyperspectral area array detector. The method can detect the bad elements of the hyperspectral multi-type area array detector, the detection types comprise no response, over response, flash elements, slit grey staining and the like, the problem of detecting the multi-type bad elements of the multi-spectral-range different-type detector at present is solved, and the method plays an important role in calibration and data preprocessing of an imager.
Description
Technical Field
The invention belongs to the field of remote sensing detection and imaging spectrometer data processing, and particularly relates to a method for detecting a bad element of a hyperspectral area array detector.
Background
Due to the casting process of the detection component or the environmental change, the detector generally has a bad element, especially the detector made of materials such as indium gallium arsenic, tellurium cadmium mercury and the like, the image quality of the linear array push-scan imaging spectrometer can be seriously influenced by the existence of the bad element, and the radiation calibration precision can also be influenced. Therefore, the detection of the bad elements is a necessary link before the image processing, and the detection of the bad elements directly influences the data processing and the image quality evaluation of the image.
The bad element types comprise no response, over response, flash elements and the like, and the grating light splitting type hyperspectrum can be processed into bad elements in the data processing process under the condition that the response of a detector is too low due to slit dust pollution. The existing bad element detection method is mainly based on a laboratory calibration method, although the method can achieve high precision, the detection cannot be realized for bad elements caused by flash elements and slit grey-staining and new bad elements formed by detector state changes, and the existing detection method is mostly limited to an infrared focal plane. Therefore, a method for detecting the bad elements of the hyperspectral area array detector is needed, and the detection of various types of bad elements is met.
Disclosure of Invention
The invention aims to solve the problems that: the method is suitable for detecting the bad elements of the hyperspectral multi-type area array detector, and solves the problem of detecting the bad elements of the multi-type multi-spectral-range different-type detector.
The invention comprises the following steps:
(1) installing an imaging spectrometer, carrying out aviation flight, carrying out image data acquisition in a fixed aviation height range, acquiring no less than L frames of ground imaging images (wherein L is more than or equal to 100000), wherein L is the frame number of image acquisition, and preprocessing an original image.
(2) And (4) data distribution statistics, namely, statistics of an accumulated histogram of each probe gray value of the aviation image.
(3) The method comprises the following steps of extracting a characteristic statistical position and a characteristic threshold value:
(3-1) extracting the number of pixels with the gray value of 0 of each probe element, extracting the gray values corresponding to the cumulative histogram number of each probe element of 0.1 xL and 0.9 xL, solving the difference value, and obtaining the difference result of statistical data, wherein L is the frame number of image acquisition;
and (3-2) sequencing pixels of each row of the statistical data difference result from small to large, extracting low-inflection point values and high-inflection point values on two sides of each row after sequencing based on derivative calculation, and storing the inflection point values of each row of all the rows.
(4) Bad element detection and marking, which comprises the following steps:
(4-1) acquiring the number of pixels of which the gray value of the probe element extracted in the step (3-1) is0, and judging that the probe element is a bad element if the number of pixels is larger than 0;
(4-2) acquiring the low inflection point value and the high inflection point value calculated in the step (3-2), judging each row of data and an inflection point threshold value, and if the data is smaller than the low inflection point value or larger than the high inflection point value, judging the probe element as a bad element;
and (4-3) overlapping the bad elements identified in the steps (4-1) and (4-2) to finish the bad element detection of the detector.
By the method, the high-precision detection of the bad elements of the detection unit of the imager can be realized, the missing detection of the conventional method and the detection of various bad elements such as flash elements and the like caused by the change of the state of the imager can be effectively avoided, and the application effect of the actual bad element detection is greatly improved.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method for detecting a bad element of a hyperspectral area array detector;
FIG. 2 is a schematic diagram of acquiring an L-frame image by aviation flight;
FIG. 3 is a diagram of a single probe cumulative histogram statistic;
FIG. 4 is a graph of difference image reordering and high and low corner threshold results.
Detailed Description
The method for detecting a bad element of a detector is described in detail below with reference to fig. 1 to 4.
(1) The imaging spectrometer is installed, the size of a detector is 640 multiplied by 512, the size of a space dimension is 640, the size of a spectrum dimension is 512, the quantization bit number of a single pixel is 10, and the dynamic range of data is 0-4095. And (3) carrying out aviation flight, carrying out data acquisition within the range of 1000 meters of fixed aviation height, acquiring 116000 frames of ground imaging images, and preprocessing the acquired image data as shown in the figure 2.
(2) And (2) performing data distribution statistics, performing statistics on an accumulated histogram of aviation flight data, performing statistics on distribution of 116000 DN values of each probe element, wherein the size of a statistical result is 640 multiplied by 512 multiplied by 116000, and the numerical value of the statistical result is HIST (m, n, s), wherein m is more than or equal to 0 and less than 640, n is more than or equal to 0 and less than 512, s is more than or equal to 0 and less than 4096, and fig. 3 shows a statistical curve of m being 639 and n being 0 at a black point in fig. 2, wherein the curve shows that the main distribution range of DN values of pixels is 1000 and 3000, and DN represents the gray value of the pixels.
(3) Extracting and processing the characteristic position;
(3-1) extracting a result that each probe HIST (m, n, s) is a specific value, obtaining a statistical result HIS0(m, n) with DN being 0, a statistical result HIS1(m, n) with HIST (m, n, s) being 11600, and a statistical result HIS2(m, n) with HIST (m, n, s) being 104400, wherein 0 is greater than or equal to m <640, and 0 is greater than or equal to n < 512; obtaining a difference value between HIS2(m, n) and HIS1(m, n) to obtain a statistical data difference result HISS (m, n);
(3-2) sorting 640 image elements of each line of HISS (m, n) from small to large, wherein the sorted image elements are HISR (m, n), and m is more than or equal to 0<640; based on derivative calculation, the inflection points at two sides of 640 data of HISR (m, n) in each row are obtained, and the numerical value of the low inflection point is recorded as PiThe value of the high inflection point is recorded as QiWherein 0 is not more than i<512, fig. 4 shows the results of sorting 512 rows of 640 HISR (i, n) values from small to large, and also shows a sorting curve of 640 HISR (i, n) values from small to large when i is 100, wherein P100 is 674, and Q100 is 2167;
(4) bad element detection and marking
(4-1) setting the blind pixel detection image E (m, n) to be 0, wherein m is more than or equal to 0 and less than 640, and n is more than or equal to 0 and less than or equal to 512;
(4-2) acquiring HIS0(m, n) extracted in the step (3-1), and determining that if HIS0(m, n) is greater than 0, then E (m, n) is 1; defining i to be 0, and acquiring the low inflection point value P calculated in the step (3-2)iAnd high knee value Qi；
(4-3) judging 640 image elements of HISS (i, n) in the ith row, if HISS (i, n) is less than PiOr greater than QiIf E (i, n) ═ 1;
(4-4) i is i +1, i <512, and repeating the step (4-3) until the bad element detection of 512 rows of data is completed;
and (4-5) superposing the results of the two detections to obtain the final E (m, n), wherein the E (m, n) is equal to 1, namely the bad element.
Claims (1)
1. A hyperspectral area array detector bad element detection method is characterized by comprising the following steps:
(1) installing an imaging spectrometer, carrying out aviation flight, carrying out image data acquisition in a fixed aviation height range, acquiring a ground imaging image with no less than L frames, wherein L is the number of frames of image acquisition and is more than or equal to 100000, and preprocessing an original image;
(2) data distribution statistics is carried out, and an accumulated histogram of each detection element gray value of the aviation image is counted;
(3) the method comprises the following steps of extracting a characteristic statistical position and a characteristic threshold value:
(3-1) extracting the number of pixels with the gray value of 0 of each probe element, extracting the gray values corresponding to the cumulative histogram number of each probe element of 0.1 xL and 0.9 xL, solving the difference value, and obtaining the difference result of statistical data, wherein L is the frame number of image acquisition;
(3-2) sorting pixels of each row of the statistical data difference result from small to large, extracting low inflection point values and high inflection point values of two sides of each row after sorting based on derivative calculation, and storing inflection point values of all rows;
(4) bad element detection and marking, which comprises the following steps:
(4-1) acquiring the number of pixels of which the gray value of the probe element extracted in the step (3-1) is0, and judging that the probe element is a bad element if the number of pixels is larger than 0;
(4-2) acquiring the low inflection point value and the high inflection point value calculated in the step (3-2), judging each row of data and an inflection point threshold value, and if the data is smaller than the low inflection point value or larger than the high inflection point value, judging the probe element as a bad element;
and (4-3) overlapping the bad elements identified in the steps (4-1) and (4-2) to finish the bad element detection of the detector.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106441808A (en) * | 2015-12-01 | 2017-02-22 | 中国科学院上海技术物理研究所 | Thermal infrared hyperspectral imager blind pixel detection device and method |
| CN107292856A (en) * | 2017-06-12 | 2017-10-24 | 北京理工大学 | A kind of method of infrared focal plane detector image enhaucament |
| CN107316318A (en) * | 2017-05-26 | 2017-11-03 | 河北汉光重工有限责任公司 | Aerial target automatic testing method based on multiple subarea domain Background fitting |
| CN109671035A (en) * | 2018-12-26 | 2019-04-23 | 哈工大机器人（山东）智能装备研究院 | A kind of infrared image enhancing method based on histogram |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106441808A (en) * | 2015-12-01 | 2017-02-22 | 中国科学院上海技术物理研究所 | Thermal infrared hyperspectral imager blind pixel detection device and method |
| CN107316318A (en) * | 2017-05-26 | 2017-11-03 | 河北汉光重工有限责任公司 | Aerial target automatic testing method based on multiple subarea domain Background fitting |
| CN107292856A (en) * | 2017-06-12 | 2017-10-24 | 北京理工大学 | A kind of method of infrared focal plane detector image enhaucament |
| CN109671035A (en) * | 2018-12-26 | 2019-04-23 | 哈工大机器人（山东）智能装备研究院 | A kind of infrared image enhancing method based on histogram |
Non-Patent Citations (2)
| Title |
|---|
| 基于模糊中值的IRFPA自适应盲元检测与补偿;冷寒冰 等;《红外与激光工程》;20150331;第44卷(第3期);第821-826页 * |
| 红外焦平面阵列的盲元自适应快速校正;李凌霄 等;《光学精密工程》;20170430;第25卷(第4期);第477-486页 * |
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