Chromatic and achromatic (AC) assessments of camouflage textiles have been critical to the defense researchers for concealment, detection, recognition, and identification (CDRI) of target signature against multidimensional combat background (CB). AC assessment and camouflage measurement techniques are simulated and experimented for assessment of camouflage textiles against CB. This model has been demonstrated for color measurement spectrophotometer, scanning electron microscopy (SEM), digital imaging, hyperspectral imaging, and image processing software (ImageJ) for the advancement and establishment of AC camouflage textiles assessment. The chromatic variations of 48 artificial target objects (TOBs) have been synthesized by image processing; the technique can be implemented for defense CB-CDRI assessment. Microstructural variation versus optical signal of woodland, desertland and stoneland CB materials have been elucidated by SEM magnification. The achromatic variation of CB materials have been demonstrated for the replacement of optical signal against modern remote sensing device to the imaging sensor. Color difference (ΔE), microstructural variations, pixel variations to imaging signal and standard deviation of CB materials have been represented for remote sensing surveillance of defense applications against TOB-CB-CDRI. Technical simulation of color, texture, gloss, and pixel intensity has been derived for AC-CDRI assessment of camouflage textiles in TOBs-CB environment.
SchaeferHMLeveyDJSchaeferVet al. The role of chromatic and achromatic signals for fruit detection by birds. Behav Ecol2006; 17: 784–789.
2.
XuLJingWSongHet al. High-resolution remote sensing image change detection combined with pixel-level and object-level. IEEE Access2019; 7: 78909–78918.
3.
RussellBJDierssenHM.Use of hyperspectral imagery to assess cryptic color matching in Sargassum associated crabs. PLoS One2015; 10: e0136260.
4.
ShresthaMDyerAGBoyd-GernySet al. Shades of red: bird-pollinated flowers target the specific colour discrimination abilities of avian vision. New Phytol2013; 198: 301–310.
5.
DyerAGBoyd-GernySMcLoughlinSet al. Parallel evolution of angiosperm colour signals: common evolutionary pressures linked to hymenopteran vision. Proc Biol Sci2012; 279: 3606–3615.
6.
BrelstaffGJParragaATrosciankoTet al. Hyperspectral camera system: acquisition and analysis. In: Proceedings of the geographic information systems, photogrammetry, and geological/geophysical remote sensing, Paris, 25–28 September 1995, pp. 150–159. Bellingham, WA: SPIE.
7.
AsnerGP.Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens Environ1998; 64: 234–253.
8.
BuschmannCLenkSLichtenthalerHK.Reflectance spectra and images of green leaves with different tissue structure and chlorophyll content. Isr J Plant Sci2012; 60: 49–64.
9.
DattnerMBohnD.Characterization of print quality in terms of colorimetric aspects. In: IzdebskaJThomasS (eds) Printing on polymers. Amsterdam: Elsevier, 2016, pp. 329–345.
10.
DaiSPanXMaLet al. Discovery of the linear region of near infrared diffuse reflectance spectra using the Kubelka-Munk theory. Front Chem2018; 6: 154.
11.
AhmedNOultonDPTaylorJA.The use of reflectance measurements in the determination of fixation of reactive dyes to cotton. Color Res Appl2006; 31: 117–121.
12.
SenthilkumarMSelvakumarNShameyR.The effect of humidity, fabric surface geometry and dye type on the colour of cotton fabrics dyed with a select range of anionic dyes. Dyes Pigments2011; 90: 225–232.
13.
BaumannWGroebelB-TKrayerMet al. Determination of relative colour strength and residual colour difference by means of reflectance measurements. J Soc Dyers Colour1987; 103: 100–105.
14.
HunterRS.Methods of determining gloss, vol. 18 (Part of journal of research of the National Bureau of Standards). Gaithersburg, MD: National Bureau of Standards, 1937.
15.
GhamisiPYokoyaNLiJet al. Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art. IEEE Geosci Remote S2017; 5: 37–78.
16.
XuKYeH.Preparation and optimization of biomimetic materials simulating solar spectrum reflection characteristics of natural leaves. J Mater Sci2020; 55: 12848–12863.
17.
WeiQFWangXQMatherRRet al. New approaches to characterisation of textile materials using environmental scanning electron microscope. Fibres Text East Eur2004; 12: 79–83.
18.
GarsideP.Investigations of analytical techniques for the characterisation of natural textile fibres towards informed conservation. PhD Thesis, University of Southampton, Southampton, 2002.
19.
BabinSBorisovSSItoHet al. Simulation of scanning electron microscope images taking into account local and global electromagnetic fields. J Vac Sci Technol2010; 28: C6C41–C6C47.
20.
JiWPointerMRLuoRMet al. Gloss as an aspect of the measurement of appearance. J Opt Soc Am A2006; 23: 22–33.
21.
ObeinGLerouxTRVienotF.Bidirectional reflectance distribution factor and gloss scales. In: Proceedings of the human vision and electronic imaging VI, San Jose, CA, 20–26 January 2001, pp. 279–290. Bellingham, WA: SPIE.
22.
YuanKYanH-MJinS-Z. The design of color spectrophotometer based on diffuse illumination and compatible SCE/SCI geometric condition. In: Proceedings of the SPIE, international conference on optical instruments and technology: optoelectronic measurement technology and systems, Beijing, China, 17–19 November 2013, p. 90460V. Bellingham, WA: SPIE.
23.
LeloupFBHanselaerPVersluysJet al. BRDF and gloss measurements. Ghent: Laboratorium Voor Lichttechnologie, Gebroeders de Smetstraat, 2007.
24.
MizrachALuRRubinoM.Gloss evaluation of curved-surface fruits and vegetables. Food Bioprocess Tech2009; 2: 300–307.
25.
KunYHuiminYShangzhongJ.A type of spectrophotometer with both SCI and SCE measurement structures. Optik2014; 125: 4672–4677.
26.
KandiSG.The effect of spectrophotometer geometry on the measured colors for textile samples with different textures. J Eng Fiber Fabr2011; 6: 70–78.
27.
MutterWE.Gloss measuring instrument. Patent 3,999,864, USA, 1976.
28.
DalalENNatale-HoffmanKM.The effect of gloss on color. Color Res Appl1999; 24: 369–376.
29.
KerekesJPBaumJE.Hyperspectral imaging system modeling. Lincoln Lab J2003; 14: 117–130.
30.
HoulbrookAWGilmoreMAMoorheadIRet al. Scene simulation for camouflage assessment. In: Proceedings of the targets and backgrounds VI: characterization, visualization, and the detection process, Orlando, FL, 24–28 April 2000, pp. 247–255. Bellingham, WA: SPIE.
31.
ArianaDPLuR.Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging. J Food Eng2010; 96: 583–590.
32.
XuJQianWChenQ.Calculation model of scattering depolarization for camouflaged target detection system. Optik2018; 158: 341–348.
33.
RamseySAMayoTBHowellsCAet al. Modeling and simulation of apparent color and apparent patterning of camouflage fabrics. Report no. NRL/MR/6390–18-9777, 18July2018. Washington, DC: Naval Research Laboratory.
34.
WelshTAshikhminMMuellerK.Transferring color to greyscale images. ACM T Graphic2002; 21: 277–280.
35.
UmbaughSE.Digital image processing and analysis: human and computer vision applications with CVIPtools. 2nd ed.Boca Raton, FL; London; New York: CRC Press, Taylor & Francis Group, 2010.
36.
PikeTW.Quantifying camouflage and conspicuousness using visual salience. Methods Ecol Evol2018; 9: 1883–1895.
37.
RenGWangYNingJet al. Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality. Spectrochim Acta A: M2020; 237: 118407.
