Experimental Analysis of Tunable Optical Spectral Imaging System Using a Grating in the Pupil Function

被引:0
作者
Javier Garcia-Diaz, F. [1 ]
Palillero-Sandoval, Omar [1 ]
Escobedo-Alatorre, J. Jesus [1 ]
Basurto-Pensado, Miguel A. [1 ]
Marquez-Aguilar, Pedro A. [1 ]
Zamudio-Lara, Alvaro [1 ]
Paul Zavala-De Paz, Jonny [2 ]
Antonio Marban-Salgado, Jose [1 ]
机构
[1] UAEM, Inst Res Pure & Appl Sci IICBA, Ctr Res Engn & Appl Sci CIICAp, Cuernavaca 62209, Morelos, Mexico
[2] Univ Politecn Queretaro, Ingn Redes & Telecomunicac, Queretaro 76240, Mexico
关键词
Optical imaging; Diffraction; Optical sensors; Hyperspectral imaging; Diffraction gratings; Pupils; Imaging; spectral images; diffraction grating; EARTH; FIELD;
D O I
10.1109/ACCESS.2022.3193393
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Hyperspectral imaging (HSI) systems have been demonstrated as a powerful imaging technique due to their high spectral resolution. HSI can obtain the spectrum for each pixel in the image of a scene, a feature that can be exploited to design optical systems with the purpose of analyzing and characterizing objects and identifying processes within the visible electromagnetic spectrum (bandwidth). In this paper, we present an HSI system comprising a diffraction grating placed in the exit pupil of our optical configuration. The spectrum for each pixel associated with the object appears in the first order of diffraction. We used this system to characterize and tune the required spectral band of the image of the captured object obtaining more information than with an optical imaging system. Accordingly, the proposed optical system is suitable to obtain spectral and hyperspectral imaging at low cost compared to an acousto-optic system or other HSI. The scanning system captures hundreds of spectral images associated with the object, obtaining a maximum spectral resolution of 0.26nm or 260 pm for one of our configurations.
引用
收藏
页码:77462 / 77474
页数:13
相关论文
共 47 条
  • [1] Hyperspectral Imaging and Classification for Grading Skin Erythema
    Abdlaty, Ramy
    Doerwald-Munoz, Lilian
    Madooei, Ali
    Sahli, Samir
    Yeh, Shu-Chi A.
    Zerubia, Josiane
    Wong, Raimond K. W.
    Hayward, Joseph E.
    Farrell, Thomas J.
    Fang, Qiyin
    [J]. FRONTIERS IN PHYSICS, 2018, 6
  • [2] Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review
    Adam, Elhadi
    Mutanga, Onisimo
    Rugege, Denis
    [J]. WETLANDS ECOLOGY AND MANAGEMENT, 2010, 18 (03) : 281 - 296
  • [3] Aguilar P. A. M., 2016, PROC SPIE, V55, P1
  • [4] Artifacts of different dimension reduction methods on hybrid CNN feature hierarchy for Hyperspectral Image Classification
    Ahmad, Muhammad
    Shabbir, Sidrah
    Raza, Rana Aamir
    Mazzara, Manuel
    Distefano, Salvatore
    Khan, Adil Mehmood
    [J]. OPTIK, 2021, 246
  • [5] Ground truth labeling and samples selection for Hyperspectral Image Classification
    Ahmad, Muhammad
    [J]. OPTIK, 2021, 230
  • [6] A new method of acousto-optic image processing and edge enhancement
    Babkina, TM
    Voloshinov, VB
    [J]. JOURNAL OF OPTICS A-PURE AND APPLIED OPTICS, 2001, 3 (04): : S54 - S61
  • [7] Acousto-optic image processing
    Balakshy, Vladimir I.
    Kostyuk, Dmitry E.
    [J]. APPLIED OPTICS, 2009, 48 (07) : C24 - C32
  • [8] Notch spatial filtering with an acousto-optic modulator
    Banerjee, PP
    Cao, DQ
    Poon, TC
    [J]. APPLIED OPTICS, 1998, 37 (32): : 7532 - 7537
  • [9] Basic image-processing operations by use of acousto-optics
    Banerjee, PP
    Cao, DQ
    Poon, TC
    [J]. APPLIED OPTICS, 1997, 36 (14): : 3086 - 3089
  • [10] Acousto-optic tunable filters: fundamentals and applications as applied to chemical analysis techniques
    Bei, L
    Dennis, GI
    Miller, HM
    Spaine, TW
    Carnahan, JW
    [J]. PROGRESS IN QUANTUM ELECTRONICS, 2004, 28 (02) : 67 - 87