CN110989035A - Optical remote sensing detection performance evaluation method - Google Patents

Optical remote sensing detection performance evaluation method Download PDF

Info

Publication number
CN110989035A
CN110989035A CN201911314217.9A CN201911314217A CN110989035A CN 110989035 A CN110989035 A CN 110989035A CN 201911314217 A CN201911314217 A CN 201911314217A CN 110989035 A CN110989035 A CN 110989035A
Authority
CN
China
Prior art keywords
target
contrast
probability
remote sensing
background
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201911314217.9A
Other languages
Chinese (zh)
Other versions
CN110989035B (en
Inventor
鲁啸天
李峰
肖变
杨雪
辛蕾
张南
鹿明
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Academy of Space Technology CAST
Original Assignee
China Academy of Space Technology CAST
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Academy of Space Technology CAST filed Critical China Academy of Space Technology CAST
Priority to CN201911314217.9A priority Critical patent/CN110989035B/en
Publication of CN110989035A publication Critical patent/CN110989035A/en
Application granted granted Critical
Publication of CN110989035B publication Critical patent/CN110989035B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V13/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices covered by groups G01V1/00 – G01V11/00

Landscapes

  • Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Geophysics (AREA)
  • Image Analysis (AREA)

Abstract

The invention relates to an evaluation method for optical remote sensing detection performance, belonging to the technical field of remote sensing detection; the optical remote sensing detection performance evaluation index is obtained by comprehensively considering the contrast limited probability and the resolution limited probability of a target and a background; wherein the contrast-limited probability is based on a contrast factor that takes into account a minimum resolvable contrast and a target background; the resolution limited probability is based on considering the Johnson criterion and a target size factor; the contrast-limited probability model specifically includes the following influencing factors: noise equivalent contrast, target and background contrast, atmospheric transmission, detection systems and platforms, display systems and human eyes. Compared with the prior art, objective factors are comprehensively considered, so that the evaluation result is consistent with the real scene distinguished by human eyes and is more visual, the probability of finding, identifying and confirming the interested target is directly obtained, and the probability is more easily accepted by a user; as a prior evaluation system, the method can help device system design and device model selection.

Description

Optical remote sensing detection performance evaluation method
Technical Field
The invention relates to an evaluation method for optical remote sensing detection performance, which comprises a contrast limited probability (P)C) Model and resolution-limited probability (P)J) The model particularly relates to an optical remote sensing performance evaluation method based on Johnson criterion, Noise Equivalent Contrast (NEC) and Minimum Resolvable Contrast (MRC), and belongs to the technical field of remote sensing.
Background
The application field of optical remote sensing is spread out in military, industry, agriculture, aerospace, public security monitoring, disaster reduction and daily life of people. For optical remote sensing detection, the overall system design is the primary task of the whole photoelectric imaging system according to application requirements (field of view, weather and environmental conditions, target and background characteristics, action distance and the like), and a scientific detection performance evaluation method has an important guiding function on reasonable system design. A detection performance evaluation system and a working distance model which are relatively complete for a ground dim light and infrared detection system are respectively a quantitative evaluation and calculation model based on MRC and minimum distinguishable temperature difference (MRTD). However, an effective means is still lacked for evaluating the optical remote sensing detection performance, and the optical detection performance cannot be scientifically and intuitively reflected by parameter indexes such as ground resolution (GSD), Modulation Transfer Function (MTF) and signal-to-noise ratio (SNR) in the traditional method, so that the system design and the user use are not facilitated. The detection performance of the detection system is measured by using image interpretation, the national image interpretation standard (NIIRS) is researched from the beginning of the seventies in the United states, and the image quality can be quantitatively characterized. The interpretation tasks are divided into 10 grades of 0-9 grades, and the capacities of finding, identifying and confirming different targets can be evaluated by taking the grades as reference. However, the NIIRS level calculation requires images acquired by an imaging system, belongs to post evaluation, and cannot provide references in the system demonstration and design stage, and the NIIRS level evaluation on the target detection capability is not specific and quantitative enough, and cannot provide accurate discovery, identification and confirmation probability of specific targets. The johnson criterion provides the basis for a fine quantification of the detection capability of the system, and when the human eye searches for a target in a certain background or on the display of the imaging system, the continuous response of the eye can be divided into finding (detection), identification and confirmation (identification), and different stages of the target searching process correspond to different detection levels, and the detection level is a visual capability evaluation combining the system performance and the human eye vision and needs to be completed through visual psychological experiments. Johnson experimentally related target detection to the equivalent strip pattern detection problem, it is possible to evaluate the target recognition capability of the imaging system with target equivalent strip pattern resolvability, as shown in Table 1, without considering the target nature and image defects. Johnson verified that equivalent stripe pattern resolution could be used to predict detection and recognition of objects, and determined the criteria for the number of stripe cycles required for each type of detection level, which is universally accepted and widely used internationally. Therefore, the invention provides a novel optical detection efficiency evaluation method based on the Johnson criterion and the Minimum Resolvable Contrast (MRC), and the method comprehensively considers the target and background contrast, atmospheric transmission, a detector and human eyes, can quantitatively calculate the discovery, identification and confirmation probabilities of the target under different backgrounds, and lays a foundation for the design and efficiency evaluation of the remote sensing optical detection system.
TABLE 1 search rating and Johnson criterion
Figure BDA0002325379010000021
Disclosure of Invention
The invention aims to solve the problem that the optical remote sensing detection performance lacks an effective evaluation means, and provides an optical remote sensing detection performance evaluation method; the method is an optical remote sensing detection performance evaluation method based on a Johnson criterion, a noise equivalent contrast NEC and a minimum resolvable contrast MRC.
The purpose of the invention is realized by the following technical scheme:
the invention provides an optical remote sensing detection performance evaluation method, which comprises the following steps:
the optical remote sensing detection performance evaluation index is obtained by comprehensively considering the contrast limited probability and the resolution limited probability of the target and the background; wherein the contrast-limited probability is based on a contrast factor that takes into account a Minimum Resolvable Contrast (MRC) and a target background; the resolution limited probability is based on considering the johnson criterion and a target size factor.
Preferably, the contrast-limited probability model specifically includes the following influencing factors: noise Equivalent Contrast (NEC), contrast of target and background, atmospheric transmission, detection systems and platforms, display systems and human eyes.
The invention provides an optical remote sensing detection performance evaluation method, which comprises the following steps:
the method comprises the following steps: according to the geographic position and atmospheric condition of the target, atmospheric radiation analysis software is used for simulating to obtain the contrast C of the target and the background0And spectral radiance Lt(λ)、Lb(lambda) and combining optical system parameters to obtain the target and background illumination E received by the image planet(λ)、Eb(λ)；
Step two: from the detection system noise level, the noise equivalent contrast NEC is calculated according to the following equation:
Figure BDA0002325379010000031
wherein Δ E (λ) is a noise equivalent illuminance;
step three: determining the angular resolution f of the target according to the characteristic size of the target and the number of required stripe periods under different detection levels according to Johnson criterion and the distance between the detector and the targetTThe number of the strip periods is multiplied by the detection distance/characteristic size, and the characteristic size is the geometric mean of the length and the width of the target;
step four: utilizing NEC and f according toTCalculating MRC, and correcting the length-width ratio epsilon of the strip and the equivalent bandwidth delta f of the noisenThe correction of (2) is to obtain:
Figure BDA0002325379010000032
wherein the SNRDTThreshold signal-to-noise ratio, MTF, for the human eye of an observer to resolve a bands(f) For system modulation transfer function, MTFeye(f) For modulating the transfer function for the human eye, teIntegration time for the human eye (typically 0.2s), tnFor the factor of improvement of the actual integration time to the signal-to-noise ratio, the strip aspect ratio ε is (2nL/W)/7, L and W are the strip length and width, respectively, n represents the Johnson criterion line logarithm, fpβ is the vertical instantaneous field of view (unit: mrad), β is GSD/R, GSD is the ground resolution, and R is the distance between the target and the detector;
step five: combining MRC and C0Substituting a probability transfer function (from: Sjaardema, trade A., Collins. Smith, and Gabriel C. birch. "History and evolution of the Johnson criterion." SANDIA Report, SAND2015-6368 (2015.)) to obtain a contrast-limited probability PC；
Figure BDA0002325379010000041
Wherein Ec＝2.7+0.7(C0/MRC)；
Step six: the line logarithm N (L) W of the detection pixel actually contained in the target image is determined1/2Substituting (2 × GSD) and Johnson criterion line logarithm n into probability transfer function to obtain resolution limited probability PJ；
Figure BDA0002325379010000042
Wherein EJ＝2.7+0.7(N/n)；
And finally, the probability of finding, identifying and confirming the target is obtained as follows:
P＝PC·PJ(5)
advantageous effects
The invention provides an optical remote sensing detection performance evaluation method, which has the advantages and the effects that:
a) and the evaluation result conforms to the real scene distinguished by human eyes by comprehensively considering the target and background contrast, atmospheric transmission, a detector and the human eyes.
b) The detection efficiency evaluation result is more intuitive, the probability of finding, identifying and confirming the interested target is directly obtained, and the probability is more easily accepted by a user.
c) The method belongs to a prior evaluation system, and can simulate and calculate the detection efficiency as long as the related technical indexes of a detection system and a platform are input, so that if the detection capability of the system is determined, the device can be helped to select the type to meet the requirement.
d) The method is more suitable for the actual application requirements, helps the detection task planning, and reasonably distributes the detection system resources; and a foundation is laid for the design and performance evaluation of the optical remote sensing detection system.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow chart of a method for evaluating optical remote sensing detection performance according to an embodiment of the invention.
Fig. 2 shows a visible light image of an actual aerial photograph.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
In order to make the technical solutions and advantages in the examples of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and not an exhaustive list of all embodiments. It should be noted that, in the present application, the examples may be combined with each other without conflict.
The inventive principle of the inventive method is explained below.
An optical remote sensing detection performance evaluation method is shown in fig. 1, and comprises the following steps:
the method comprises the following steps: according to the geographic position and atmospheric condition of the target, atmospheric radiation analysis software is used for simulating to obtain the contrast C of the target and the background0And spectral radiance Lt(λ)、Lb(lambda), and further obtaining the target and background illumination E received by the image plane according to the optical system parameterst(λ)、Eb(λ)；
Step two: suppose the mean square root noise of the detector is σ0(unit: e)-P/s, number of electrons per pixel per second), quantum efficiency of Φ, noise equivalent illuminance Δ E (λ) (W/m)2) Comprises the following steps:
Figure BDA0002325379010000061
wherein h is Planck constant 6.63 x 10-34J.s.c. light speed 3 x 108m/s，λ0Is the wavelength (the actual calculation takes the center wavelength), AdIs the pixel area.
Therefore, the first and second electrodes are formed on the substrate,
Figure BDA0002325379010000062
the essence of NEC is to characterize the noise by the input signal strength.
Step three: determining the angular resolution f of the target according to the characteristic size of the target and the number of required stripe periods under different detection levels according to Johnson criterion and the distance between the detector and the targetTBand period × detection distance/feature size;
step four: using NEC and fTCalculating the minimum resolvable contrast MRC:
SNR (signal-to-noise ratio) of target image received by system0Comprises the following steps:
Figure BDA0002325379010000063
c is the contrast of the target and the background;
at the display outputSNR of end-band imageiComprises the following steps:
Figure BDA0002325379010000064
where R (f) is the square wave response (contrast transfer function), MTF, of the systemm(f) Is the modulation transfer function after the noise insertion point, s (f) is the noise power spectrum after the noise insertion point, Δ fnIs the noise equivalent bandwidth; using the contrast transfer function R (f) and the system modulation transfer function MTFs(f) And taking the first term to approximate
Figure BDA0002325379010000065
When the observer observes the target, the human eye will correct the display signal-to-noise ratio in four ways, resulting in a visual signal-to-noise ratio.
1) The eye extracts the banding pattern, under the condition of distinguishable signal, filters out the higher order harmonic, keeps the first order harmonic, then signal peak value decay is:
Figure BDA0002325379010000071
2) due to the time integration, the signal will integrate time (t) according to the human eyee0.2s) one independent sample while the noise is root-added, so the signal-to-noise ratio will improve (t)efp)1/2，fpThe display frame rate.
3) In the vertical direction, the eye will perform signal spatial integration and take the root mean square of the noise along the line, using the vertical instantaneous field of view β as the correlation length of the noise, resulting in an improvement in the visual signal-to-noise ratio:
Figure BDA0002325379010000072
wherein, L and W are respectively the length and width of the strip; epsilon0The aspect ratio (L/W) of the strip is shown.
4) For frequency fTPeriodic rectangleFor a banded object, the narrow-band spatial filtering effect of the human eye is equivalent to a matched filter, the transfer characteristic of which can be expressed as sin2(π/2·f/fT)·MTF2 s(f) Where f represents the spatial frequency. Thus, the integral response of the human eye can be converted by the actual system bandwidth into a noise bandwidth Δ f that takes into account the effect of the human eye matched filtereye：
Figure BDA0002325379010000073
Wherein the MTFeye(f) The transfer function is modulated for the human eye, sinc (-) is a sine basis function;
for fast calculation, assume the noise is white, when fT→0，Δfeye→fT。
Combining the above 4 modifications with the display signal-to-noise ratio to obtain the visual signal-to-noise ratio:
Figure BDA0002325379010000074
wherein, teIntegration time for the human eye, typically 0.2 s; f. ofpA display frame rate;
the threshold signal-to-noise ratio of the bands which can be resolved by the human eye of an observer is SNRDTSince the solved C is MRC, MRC is obtained:
Figure BDA0002325379010000081
step five: based on practical application, the MRC model is corrected:
1) correction of stripe aspect ratio epsilon
The aspect ratio of the strips commonly used in the laboratory is 7:1, but the aspect ratio epsilon of the target in the detection performance evaluation is not only related to the target per se, but also related to the number n of the strips of the detection level corresponding to the Johnson criterion, so that the aspect ratio epsilon is corrected
ε＝(2nL/W)/7 (16)
2) Noise equivalent bandwidth Δ fnCorrection of (2)
Since in practice only dark current noise is generally considered for visible light detector noise and the index is a fixed value, i.e. NEC is only related to the target and background radiance, in practice it is also affected by the integration time and therefore needs to be corrected.
The corrected MRC is:
Figure BDA0002325379010000082
wherein t isnIs a multiple of the actual integration time to signal-to-noise improvement.
Step six: combining MRC and C0Substituting the probability transfer function to obtain the contrast limited probability PC
Figure BDA0002325379010000083
Wherein Ec＝2.7+0.7(C0/MRC)。
Step seven: substituting the detection pixel line logarithm N and N actually contained in the target image into a probability transfer function to obtain the resolution limited probability PJ
Figure BDA0002325379010000084
Wherein EJ＝2.7+0.7(N/n)。
The probability of finding, identifying and confirming is finally obtained as follows:
P＝PC·PJ(20)
in the following, according to the present invention, a preferred embodiment of the present invention is verified and explained in detail with reference to the remote sensing picture shown in fig. 2:
the method comprises the following steps: the experimental place is a suburb in Beijing, the weather is clear, the visibility is 23km when the picture shown in figure 2 is shot, the solar altitude is 40 degrees, and the contrast C of the target (small vehicle) and the background (cement pavement) is obtained by applying Modtran atmospheric radiation analysis software simulation00.41 and radiance Lt＝0.00372W/cm2/sr、Lb＝0.00156W/cm2The aperture D of the optical system is 20mm, and the focal length f is further determined by the parameters of the optical system in the remote sensing system0' 5mm, optical system transmittance 0.7, and target and background illuminance E received at image planet＝2.2×10-3W/m2、Eb＝9.06×10-4W/m2；
Step two: dark current noise σ of the visible light detector in this example0Is 1220e-P/s (only dark current noise is considered here), the quantum efficiency phi is 90%, the center wavelength lambda0550nm, pixel size 7 μm, and noise equivalent illuminance Δ E (λ) of 10 from equation (6)-5W/m2If the NEC is 0.0032;
step three: target (car) length L ═ 4.8m, width W ═ 1.7m, characteristic dimensions (4.8 × 1.7)1/22.86m and the number of cycles of the strip required by the Johnson criterion at different detection levels (here, the 50% probability required resolution n-0.7536 for two-dimensional detection in Table 1]) And the distance R between the detector and the target is 586m, and the angular resolution f of the target is obtainedT＝[0.1537 0.6147 1.2294]cyc/mrad (the unit cyc/rad obtained according to the formula is converted into cyc/mrad which is divided by 1000, 3 numbers respectively correspond to different search levels, namely discovery, identification and confirmation, the same below);
step four: the parameters of the remote sensing system taking the picture shown in fig. 2 are: SNRDT＝2.25，MTFs(f)＝0.1，MTFeye(f) When the exponential model is 0.76, β equals 0.8532mrad, L/W equals 2.8, fp＝50Hz，GSD＝0.5m，R＝586m，tn＝1，ε＝[0.6 2.4 4.8]Calculating MRC ═ 0.00970.0190.027 according to formula (2)]；
Step five: n is calculated to be 2.86 from formulas (3) to (4), and referring to table 1, N is [ 0.7536 ]]Obtaining the detection probability P of the target [ 0.990.460.10 ═]Namely, the found probability is 0.99, the identified probability is 0.46, and the confirmed probability is 0.10, and the human eye observation result of the small car in the box of the attached figure 2 is well consistent with the above calculation result, so that the accuracy and the feasibility of the invention are proved. Here the contrast limited probability PCIs substantially 1 because of the embodimentThe image is collected under the condition of clear weather and 40 degrees of solar altitude, the illumination condition is good, so the contrast ratio does not restrict the performance of the detection system, and P isCSubstantially 1, where the system is limited primarily by resolution.
In conclusion, the method of the invention comprehensively considers the target and background contrast, the atmospheric transmission, the detector and the human eyes, so that the result is closer to the real scene. And the detection efficiency evaluation result expressed by the probability is more intuitive, the probability of finding, identifying and confirming the interested target is directly obtained, and the probability is more easily accepted by the user. Meanwhile, the method belongs to a prior evaluation system, and the detection efficiency can be simulated and calculated as long as relevant technical indexes of a detection system and a platform are given, so that if the detection capability of the system is determined, the method can help the device to select the type to meet the requirement. In addition, the method is more suitable for the actual application requirements, can help the detection task to be planned, and reasonably distributes the detection system resources; and a foundation is laid for the design and performance evaluation of the optical remote sensing detection system.
Those of ordinary skill in the art will understand that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.

Claims (3)

1. An optical remote sensing detection performance evaluation method is characterized in that an optical remote sensing detection performance evaluation index is obtained by comprehensively considering the contrast limited probability and the resolution limited probability of a target and a background; wherein the contrast-limited probability is based on a contrast factor that takes into account a Minimum Resolvable Contrast (MRC) and a target background; the resolution limited probability is based on considering the johnson criterion and a target size factor.
2. The method according to claim 1, wherein the contrast-limited probability model comprises in particular the following influencing factors: noise equivalent contrast NEC, contrast of target and background, atmospheric transmission, detection systems and platforms, display systems and human eyes.
3. The method of claim 1, wherein: the method comprises the following steps:
s1, according to the geographic position and the atmospheric condition of the target, obtaining the contrast C of the target and the background by the simulation of atmospheric radiation analysis software0And spectral radiance Lt(λ)、Lb(lambda) and combining optical system parameters to obtain the target and background illumination E received by the image planet(λ)、Eb(λ)；
S2, calculating the noise equivalent contrast NEC according to the following formula according to the noise level of the detection system:
Figure FDA0002325377000000011
wherein Δ E (λ) is a noise equivalent illuminance;
s3, calculating the angular resolution f of the target according to the characteristic size of the target and the Johnson criterionTThe number of the strip periods is multiplied by the detection distance/characteristic size, and the characteristic size is the geometric mean of the length and the width of the target;
s4, utilizing NEC and f according to the following formulaTCalculating the MRC:
Figure FDA0002325377000000012
wherein the SNRDTThreshold signal-to-noise ratio, MTF, for the human eye of an observer to resolve a bands(f) For system modulation transfer function, MTFeye(f) For modulating the transfer function for the human eye, teIntegration time, t, for the human eyenFor the factor of the improvement of the actual integration time to the signal-to-noise ratio, epsilon is the aspect ratio of the strip, and the calculation formula is as follows: epsilon ═(2nL/W)/7, L and W being respectively the length and width of the strip, fpFor the display frame frequency, β is the vertical instantaneous field of view, β is GSD/R, GSD is the ground resolution, R is the target and detector distance;
s5, mixing MRC and C0Substituting the probability transfer function to obtain the contrast limited probability PC；
Figure FDA0002325377000000021
Wherein Ec＝2.7+0.7(C0/MRC)；
S6, the line logarithm N (L W) of the detecting element actually included in the target image1/2Substituting (2 × GSD) and Johnson criterion line logarithm n into probability transfer function to obtain resolution limited probability PJ；
Figure FDA0002325377000000022
Wherein EJ＝2.7+0.7(N/n)；
And finally, the probability of finding, identifying and confirming the target is obtained as follows:
P＝PC·PJ。
CN201911314217.9A 2019-12-19 2019-12-19 Optical remote sensing detection performance evaluation method Active CN110989035B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911314217.9A CN110989035B (en) 2019-12-19 2019-12-19 Optical remote sensing detection performance evaluation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911314217.9A CN110989035B (en) 2019-12-19 2019-12-19 Optical remote sensing detection performance evaluation method

Publications (2)

Publication Number Publication Date
CN110989035A true CN110989035A (en) 2020-04-10
CN110989035B CN110989035B (en) 2022-01-11

Family

ID=70095805

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911314217.9A Active CN110989035B (en) 2019-12-19 2019-12-19 Optical remote sensing detection performance evaluation method

Country Status (1)

Country Link
CN (1) CN110989035B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111579212A (en) * 2020-05-27 2020-08-25 燕山大学 MRTD-based microscopic thermal imaging system performance evaluation method and system
CN113075150A (en) * 2021-03-19 2021-07-06 中国空间技术研究院 Space-based infrared early warning spectrum section selection method and device based on detection efficiency

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080284848A1 (en) * 2005-08-26 2008-11-20 Peter Martin Security surveillance planning tool kit
CN102567608A (en) * 2010-12-29 2012-07-11 中国科学院沈阳自动化研究所 Performance evaluation experiment analysis method for photo-electric imaging system
CN103440389A (en) * 2013-08-31 2013-12-11 西安电子科技大学 Infrared camouflage effect simulation system and infrared camouflage effect evaluation method based on full link
CN104732480A (en) * 2015-03-03 2015-06-24 中国空间技术研究院 Remote-sensed image super-resolution method based on non-local regularization model
CN205305021U (en) * 2015-04-19 2016-06-08 刘晓岚 Test system based on equivalence illumination and minimum distinguishable contrast
CN106204524A (en) * 2016-06-23 2016-12-07 凌云光技术集团有限责任公司 A kind of method and device of evaluation image quality
CN108537758A (en) * 2018-04-23 2018-09-14 北京理工大学 A kind of method for enhancing picture contrast based on display and human-eye visual characteristic
CN109060312A (en) * 2018-08-10 2018-12-21 河南科技大学 Visible light device operating distance test method based on controllable target

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080284848A1 (en) * 2005-08-26 2008-11-20 Peter Martin Security surveillance planning tool kit
CN102567608A (en) * 2010-12-29 2012-07-11 中国科学院沈阳自动化研究所 Performance evaluation experiment analysis method for photo-electric imaging system
CN103440389A (en) * 2013-08-31 2013-12-11 西安电子科技大学 Infrared camouflage effect simulation system and infrared camouflage effect evaluation method based on full link
CN104732480A (en) * 2015-03-03 2015-06-24 中国空间技术研究院 Remote-sensed image super-resolution method based on non-local regularization model
CN205305021U (en) * 2015-04-19 2016-06-08 刘晓岚 Test system based on equivalence illumination and minimum distinguishable contrast
CN106204524A (en) * 2016-06-23 2016-12-07 凌云光技术集团有限责任公司 A kind of method and device of evaluation image quality
CN108537758A (en) * 2018-04-23 2018-09-14 北京理工大学 A kind of method for enhancing picture contrast based on display and human-eye visual characteristic
CN109060312A (en) * 2018-08-10 2018-12-21 河南科技大学 Visible light device operating distance test method based on controllable target

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张涛: "《机场管制塔台通视能力评估方法研究》", 《中国优秀硕士论文全文数据库 工程科技II辑 2018/01》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111579212A (en) * 2020-05-27 2020-08-25 燕山大学 MRTD-based microscopic thermal imaging system performance evaluation method and system
CN113075150A (en) * 2021-03-19 2021-07-06 中国空间技术研究院 Space-based infrared early warning spectrum section selection method and device based on detection efficiency
CN113075150B (en) * 2021-03-19 2022-11-01 中国空间技术研究院 Space-based infrared early warning spectrum section selection method and device based on detection efficiency

Also Published As

Publication number Publication date
CN110989035B (en) 2022-01-11

Similar Documents

Publication Publication Date Title
Berberoglu et al. Assessing different remote sensing techniques to detect land use/cover changes in the eastern Mediterranean
KR101855652B1 (en) System of total cloud cover retrieval from satellite observation using machine learning and method thereof
US9906718B2 (en) Biomimetic integrated optical sensor (BIOS) system
Lang et al. Canopy gap fraction estimation from digital hemispherical images using sky radiance models and a linear conversion method
Nilsson et al. Quantifying biologically essential aspects of environmental light
CN110989035B (en) Optical remote sensing detection performance evaluation method
CN116519557B (en) Aerosol optical thickness inversion method
CN113762161B (en) Intelligent obstacle monitoring method and system
Levin et al. Illuminating the capabilities of Landsat 8 for mapping night lights
Brelstaff et al. Hyperspectral camera system: acquisition and analysis
Gălăţanu Luminance measurements for light pollution assessment
Hooda IoT and remote sensing
Janout et al. Stellar objects identification using wide-field camera
Barducci et al. Effects of light pollution revealed during a nocturnal aerial survey by two hyperspectral imagers
Zhang et al. Acquisitions and applications of forest canopy hyperspectral imageries at hotspot and multiview angle using unmanned aerial vehicle platform
Sreekanth et al. Measurements of atmospheric turbulence parameters at Vainu Bappu Observatory using short-exposure CCD images
CN108897059A (en) A kind of Infrared Targets imaging detectivity analysis method
Morris Total sky imager (TSI) handbook
CN112967346A (en) Radiometric calibration method and device for remote sensing sensor and electronic equipment
Winkelmann Spectral characterization of natural backgrounds
KR20210111578A (en) Distortion Method of Total Cloude Cover in Night Time using Ground Based Whole Sky Image Data
CN108154478A (en) A kind of remote sensing image processing method
Alexandridis et al. LAI measurement with hemispherical photographs at variable conditions for assessment of remotely sensed estimations
CN105894500B (en) A kind of visual range detection method based on image procossing
Rochdi et al. Systematic mapping of Leaf Area Index across Canada using 250-meter MODIS data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant