multi spectral imaging sensors

MULTI-SPECTRAL IMAGING SYSTEMCourse: BREE504 (Instrumentation and Control) PresentationBy: Lanrewaju Adetunji (260606673)Date: Thursday, October 29th, 2015

Wednesday 3 May 2023Spectral Image Sensor | Adetunji 2

OVERVIEW Definition of Multi-Spectral Imaging (incl HSI); The Spectrograph; Detectors or Image Sensor (incl Types and Noise); Resolution, Precision, and Accuracy of MSI systems; Application of MSI systems within the Bioresource Engineering domain Conclusion


MULTI-SPECTRAL IMAGING: DEFINITION Spectoscopy + Conventional Imaging

= Spatial + Spectral Information Hyperspectral Imaging > MSI EM radiation classified–radio wave, MW,

IR, visible light, UV, X-rays, gamma rays–according to wave length

Target illuminated by tungsten-halogen or LED source or natural lighting (Sun)

Spectrograph scatters and measures reflectance, absorbance, or fluorescence

Two-dimensional spatial image (x×y) × wavelength (spectral; λ) dimension = hypercube Fig 1: The Concept of Imaging Spectroscopy

Source: Shaw & Burke, 2003


MULTI-SPECTRAL IMAGING: DATASET ACQUISITION TECHNIQUES

Fig 2: Techniques for acquiring hypercube dataset


MULTISPECTRAL IMAGING: THE SPECTROGRAPH

The Slit-Controls amount and angle of entry light

-10, 25, 50, 100, and 200μm

Collimator-Essentially a concave mirror

The diffraction grating-determines λ range and optical resolution of the system-2 types: ruled and holographic gratings

Camera lens-Refocuses dispersed light onto the detector pixels

Detector-Made up of several pixels-Two types: CCD and CMOS

The Spectrograp

h

Scheme 1: Components and flow of radiation in a spectrograph


IMAGE SENSORS: PRINCIPLE OF OPERATION Converts captures EM radiation (light)

into electrical signal (charges); Charges iteratively converted into

voltage (AMPLIFIER) Additional circuitry converts voltage into

digital information (A/D Conv.) Depending on SC material used (Si or

InGaAs),

where: λ, upper wavelength limit; h, Planck’s constant; c, speed of light; Egap, bandgap energy of the SC

Fig 3: Operation of a CCD Image Sensor (Source: Wikipedia)

Lanrewaju Adetunji

Include: physical phenomenon, and guiding physical laws on slide 3


IMAGE SENSORS: TYPES Broadly they can be classified into

two, namely: Analog and Digital Digital image sensors are

semiconductor-based: Charge-coupled detector (CCD) Active pixel sensors e.g.

including Complementary Metal-Oxide Semiconductor (CMOS)

Common SC materials: Si (1.1eV) HgCdTe InGaAs

CCD CMOSDOMAINReadout electronic incorporation

No Yes

Radiation tolerance Lower Higher

Frame shift smear Susceptible Not Susceptible

Power consumption Higher Lower

System size Larger Smaller

Cost Higher LowerRolling-shutter effect Not susceptible Susceptible

Table 1: Comparison of two common SC-based image sensors used in Multi-spectral Imaging


MULTISPECTRAL IMAGING SYSTEMS: RESOLUTION, PRECISION, AND ACCURACY Resolution reported as spectral (or

optical) resolution: Determined by: the slit; the diffraction grating; the

detector.

Spatial resolution: Typical array sizes in pixels (or pixel resolution) in many imaging sensors vary from 640×480 to 2,048×1,536 pixels. For reference, human vision is >100 million pixels

where: δλ, spectral resolution of spectrometer; RF, resolution factor; Δλ, spectral range; Ws, slit width; Wp, pixel width; n, number of pixel.

E.g., for a spectrometer with a 25μm slit, a 14μm 2048 pixel detector and a λ range from 350 – 1050nm, calculated resolution will be 1,53nm.

Precision and resolution depend on sampling resolution


IMAGE SENSORS: TYPES

Fig 4: CCD sensor (Source: Wikipedia) Fig 5: CMOS sensor (Source: Wikipedia)


IMAGE SENSORS: NOISE Higher 1/f noise in CMOS Noise increases with increasing

illumination, but SNR also increases with illumination

Noise Types/Sources in CMOS: Intrinsic sources (Shot, thermal,

and flicker noises); Reset noise; Pixel noise; Readout noise; Quantization noise; Noise from power supply

fluctuation; Noise from environmental

influences

Lanrewaju Adetunji

Include: factors influencing performance


SIGNAL CONDITIONING AND PRE-PROCESSING:

Fig 6: CCD sensor showing array detectors (pixels) and amplifier

Lanrewaju Adetunji

Include noise sources here


CURRENT APPLICATION (INCLUDING BIORESOURCE DOMAIN):MULTI-SPECTRAL IMAGING: Remote sensing; Precision agriculture; Food quality/inspection: defect

and contaminant detection in poultry carcasses

IMAGE SENSORS: CMOS–lower-end applications

(e.g. mobile devices and small cameras)

CCD–cost-effective (in the long-run) for use in Spectral Imaging (e.g. Hyperspectral Imagers, Spectrometers, X-ray Imaging Systems)

Fig 8: Absorbance spectra of whole corn kernel and aflatoxin-contaminated kernel(Source: Yao et al, 2015)

Fig 7: Reflectance spectra of different types of vegetation

(Smith, 2012)

Lanrewaju Adetunji

Remote sensing, non-contact food quality and safety assessment;


REFERENCES: http://

www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-photos/satellite-imagery-products/educational-resources/9393#answer1.

https://en.wikipedia.org/wiki/Spectrograph. https://en.wikipedia.org/wiki/Charge-coupled_device. http://bwtek.com/spectrometer-introduction/. http://www.sensorsmag.com/machine-vision/growing-world-image-sensors-market-9533. https://en.wikipedia.org/wiki/Hyperspectral_imaging. Delwiche, S. (2015). Basics of Spectroscopic Analysis. In B. Park & R. Lu (Eds.), Hyperspectral Imaging Technology in

Food and Agriculture (pp. 57-79): Springer New York. Shaw, G.A., Burke, H.K. (2003). Spectral Imaging for Remote Sensing. MIT Lincoln Laboratory Journal. Available online

at: https://www.ll.mit.edu/publications/journal/pdf/vol14_no1/14_1remotesensing.pdf. Smith, R.B., 2012. Introduction to hyperspectral Imaging. Available online at: http://

www.microimages.com/documentation/Tutorials/hyprspec.pdf. Tian, H., 2000. Noise Analysis in CMOS Image Sensors. (Unpublished Thesis) Yao, H., Hruska, Z., Brown, R., Bhatnagar, D., Cleveland, T. (2015). Safety Inspection of Plant Products. In B. Park & R.

Lu (Eds.), Hyperspectral Imaging Technology in Food and Agriculture (pp. 127-172): Springer New York.

http://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-photos/satellite-imagery-products/educational-resources/9393#answer1




https://en.wikipedia.org/wiki/Spectrograph



https://en.wikipedia.org/wiki/Charge-coupled_device

https://en.wikipedia.org/wiki/Charge-coupled_device

http://bwtek.com/spectrometer-introduction/

http://bwtek.com/spectrometer-introduction/

http://www.sensorsmag.com/machine-vision/growing-world-image-sensors-market-9533



https://en.wikipedia.org/wiki/Hyperspectral_imaging

https://en.wikipedia.org/wiki/Hyperspectral_imaging

https://www.ll.mit.edu/publications/journal/pdf/vol14_no1/14_1remotesensing.pdf

https://www.ll.mit.edu/publications/journal/pdf/vol14_no1/14_1remotesensing.pdf

http://www.microimages.com/documentation/Tutorials/hyprspec.pdf

http://www.microimages.com/documentation/Tutorials/hyprspec.pdf


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