Hyperspectral Camera Design Project Preliminary Design Report
Project #P06522
Team Members: Will Shaffer Jeff Sidoni Dan Scorse Jordan Gartenhaus
Sponsored by: D3 Engineering 222 Andrews Street Rochester, NY 14604
Hyperspectral Camera Project (P06552) D3 Engineering
Table of Contents 1. List of Figures………………………………………………………… 4 2. List of Equations ………………………………………………………5 3. Background of Sponsor ……………………………………………….6 4. Hyperspectral Introduction and Theory ………………………………7
4.1. Background of Hyperspectral Imaging …………………………..7 4.2. Spectroscopy and Spectral Reflectance …………………………..7 4.3. Hyperspectral Data Acquisition …………………………………..8
4.3.1. Linear Array Spectrograph ………………………………..9 4.3.2. Two Dimensional Sensor Method …………………………9 4.3.3. Data Cube Composition …………………………………10
4.4. Identification and Classification of Materials …………………..11 4.5. Applications of Hyperspectral Imaging …………………………12
5. Project Outline ……………………………………………………….13 5.1. Purpose ………………………………………………………….13 5.2. Requirements ……………………………………………………13 5.3. Project Timeline …………………………………………………14
6. Preliminary Design …………………………………………………..15 6.1. Imaging System …………………………………………………15
6.1.1. CCD vs. CMOS Imagers …………………………………15 6.1.2. Texas Instruments C6416 vs. DM642 DSP ………………16 6.1.3. Preliminary Imaging System Design Components ………17
6.2. User Interface ……………………………………………………17 6.3. Optical System Design …………………………………………17
6.3.1. Background Motivation ………………………………….17 6.3.2. Abstract ………………………………………………….17 6.3.3. Preliminary Research …………………………………….18 6.3.4. ImSpector Research ………………………………………20 6.3.5. Alternate Dispersion Element Design ……………………21 6.3.6. Prism Design ……………………………………………..24 6.3.7. Final Optical Design ……………………………………..29
6.4. Scanning Mirror Control ………………………………………..30 6.4.1. System Overview ………………………………………30 6.4.2. Stepper Motor Control …………………………………..31 6.4.3. Design Requirements …………………………………….32 6.4.4. Slit Width Analysis ………………………………………34 6.4.5. Kruse Control …………………………………………….36
6.5. Data Normalization and Material Identification ………………..38 6.6. Mechanical System and Enclosure ………………………………39
6.6.1. Requirements …………………………………………….39 6.6.2. Mounting Hardware ……………………………………39
6.6.2.1. Scanning Mirror Mount …………………………….39 6.6.2.2. ImSpector Lens Mounting System …………………40 6.6.2.3. Custom Design Lens Mounts ……………………….40 6.6.2.4. Imaging Device Mount …………………………….40
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6.6.3. System Enclosure ………………………………………40 6.7. Preliminary Bill of Materials ……………………………………42
7. Design Feasibility and Expected Technical Issues …………………..43 8. References ……………………………………………………………44
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1. List of Figures:
Cover Image: Hyperspectral Data Cube Figure 4.1: Electromagnetic Spectrum ………………………….7 Figure 4.2: Spectral Signature of Various Materials ……………..8 Figure 4.3: Linear array Imaging Spectrometer ………………….9 Figure 4.4: Two-dimensional Imaging Spectrometer …………..10 Figure 4.5: Hyperspectral Image Cube …………………………11 Figure 4.6: Panchromatic Combat Area Image …………………12 Figure 4.7: Multispectral Combat Area Image………………….12 Figure 4.8: Hyperspectral Combat Area Image…………………12 Figure 5.1: Project Timeline ……………………………………14 Figure 6.1: System Block Diagram ……………………………..15 Figure 6.2: Imager Response Curve …………………………….16 Figure 6.3: 90 Degree Optical Path ……………………………..19 Figure 6.4: Straight through Optical Path……………………….20 Figure 6.5: Prism Grating Prism Assembly …………………….21 Figure 6.6: Littrow Prism Spectrograph ………………………..22 Figure 6.7: Littrow Prism Spectrograph with mirror……………23 Figure 6.8: Wadsworth constant-deviation mounting …………..23 Figure 6.9: Littrow-mounted Prism Spectrograph ……………..24 Figure 6.10: Geometry of a Prism ………………………………..24 Figure 6.11: Geometry of Focal Length and Detector Height …..27 Figure 6.12: Lambda vs. Height …………………………………27 Figure 6.13: Wavelength vs. Optimized Refracted Angle ……….27 Figure 6.14: Detector Size vs. Input Angle ………………………28 Figure 6.15: Detector Size vs. Prism Angle………………………28 Figure 6.16: Pugh Diagram of Optical Design …………………..29 Figure 6.17: Phase ‘A’ Driver Schematic ………………………..31 Figure 6.18: Two Phase Stepper Motor Configuration ………….31 Figure 6.19: Micro-step Phase Winding Signals ………………..32 Figure 6.20: Open-Loop Stepper Motor Control…………………32 Figure 6.21: Required Target Workspace ………………………..33 Figure 6.22: Scanning Mirror Parameters ………………………..34 Figure 6.23: Image Slit Width Analysis ………………………….35 Figure 6.24: Two Phase Motor Schematic and Model …………..37 Figure 6.25: Closed-loop Kruse Stepper Motor Controller………37 Figure 6.26: Slit Width Analysis for Kruse Controlled System …38 Figure 6.27: Preliminary Bill of Materials ……………………….42
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2. List of Equations
Equation 6.1: Optical Path Length ………………………….18 Equation 6.2: Index of Refraction of BK7…………………..25 Equation 6.3: Exit Angle ……………………………………25 Equation 6.4: Detector Size …………………………………25 Equation 6.5: Change in Exit Angle ………………………26 Equation 6.6: Target Image Size ……………………………34 Equation 6.7: Slits per Scan…………………………………34 Equation 6.8-11: Successive Slit Width Calculations…………..34 Equation 6.12: General Slit Width Formula ………………….35 Equation 6.13-23: Kruse Control Functionality Overview …..36-37
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3. Background of Sponsor
D3 Engineering provides DSP Hardware, Software, and Algorithms in signal
processing applications. The range of projects includes medical, wireless, image
processing, and motor control.
The Hyperspectral Camera is the 3rd RIT Senior Design project that D3
Engineering has sponsored. The Senior Design Project is used by D3 to evaluate
potential full-time employees, develop IP, and prepare prototypes for potential
customer demonstration.
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4. Hyperspectral Introduction and Theory
4.1 Background of Hyperspectral Imaging Imaging in its traditional sense has always been though of as taking pictures
using three distinct wavelengths of light; red, green and blue. In remote sensing,
the term hyperspectral refers to an imaging system capable of capturing up to
several hundred narrow, contiguous spectral bands of the electromagnetic
spectrum, as seen in Figure 4.1. This method of acquiring data is what
distinguishes a hyperspectral system from a traditional multispectral system,
where only a pre-selected set of wavelengths are captured. This ability allows for
the detection of subtle variations that are often overlooked by less informative
multispectral and traditional imaging methods. The interest in this type of
imaging has increased greatly due to the amount of information that an image
contains.
Figure 4.1 – The Electromagnetic Spectrum
Advancements in manufacturing allow for better imaging systems,
imagers with lower signal to noise ratios and more accurate methods of
calibrating the cameras.
4.2 Spectroscopy and Spectral Reflectance Spectroscopy is, by definition, the study of the absorption or reflection of
various wavelengths of light by a substance. There are three main types of
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spectroscopy; emission, scattering and absorption. This type of imaging uses the
latter to measure the amount of light reflected from the materials under
investigation.
The spectral reflectance of an object is the ratio of reflected energy to incident
energy as a function of wavelength. The reflectance spectrum, also known as a
hyperspectral signature, of a material is a plot of this spectral reflectance with
respect to wavelength. Figure 4.2 gives an example of a spectral signature for
several materials.
0 5 10 15-10
0
10
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Wavelength λ (μm)
% R
efle
ctan
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AsphaltDry GrassGraniteGrassDark Soil
Figure 4.2 – Example of Spectral Signature
4.3 Hyperspectral Data Acquisition Hyperspectral images are obtained using a device called an imaging
spectrometer. By definition, this is an optical device used to measure various
properties of light over the electromagnetic spectrum. Typically, it contains an
optical system, a dispersing element such as a prism or grating, and an array of
detectors that are designed to operate over a wide variety of wavelengths.
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4.3.1 Linear Array Spectrograph
A linear array spectrograph contains a single row of detectors, depicted by
the simplified diagram in Figure 4.3. This figure shows how the spectral plot
of a single cell of the target is obtained. The reflected light from this cell is
broken into is spectral content by the element, and the array of detectors
measures the respective values of the intensity of the incident light.
Figure 4.3 – Simplified Imaging Spectrometer
In order to obtain a complete row of the target, it is necessary to use a
mirror that scan across the target while the spectrograph evaluates the spectral
content of each individual cell in the row.
The limitation of this setup is the contingency that another element is
responsible for the second dimension of motion over the target. In remote
sensing applications this is usually accomplished by the movement of the
camera inside a flying plane or a non-geosynchronous satellite over the target
area.
4.3.2 Two Dimensional Sensor Method
An alternative method of capturing the data is to replace the linear array
with a two dimensional CMOS or CCD imager. Figure 4.4 shows a simplified
diagram of such a system. One row of the target is passed through a slit in the
front end optical system and into the dispersion element. The output of the
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element is a two dimensional representation of the spectrum of each cell of the
row, which the sensor detects. One axis of the sensor represents the spectral
content, while the other represents the spatial.
Figure 4.4 – Two Dimensional Sensor Method
The use of a two dimensional sensor allows the imaging system to cover a
single linear dimension with one frame of the imager, while the linear array
spectrometer has to take successive frame captures to create a row. This will
simply the data acquisition in a lab based environment has the possibility of
eliminating the need for moving mechanical components, such as the scanning
mirror, in remote sensing applications where a two dimensional target is
acquired.
4.3.3 Data Cube Composition
The final data set consists of successive images with high spectral
resolution that allows for materials to be identified, unlike old systems that
could only differentiate between materials. In order to organize the data in a
way that is understandable, the successive two dimensional images acquired
are combined to form a three dimensional data set, known as a hyperspectral
cube. Figure 4.5 shows how a data cube is comprised; two spatial axes and
one spectral axis.
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Figure 4.5 – Hyperspectral Image Cube
4.4 Identification and Classification of Materials
Due to the vast amounts of information contained in a hyperspectral data set,
the spectral responses of the pixels can be used to identify the materials present.
There are several libraries available that contain plots of spectral signatures for
hundreds or even thousands of materials ranging from natural to man-made.
Several issues arise when extracting the data from a raw image cube. In
remote sensing applications, the target cell that is being observed rarely contains
reflectance values due to a single material since the target cell size is large. While
the degree of distortion is dependent on exactly how large the cell size is, the
resulting spectra is a combination of all the different materials within that cell,
resulting in a composite or mixed spectrum.
Classification and identification pose other problems when compared directly
with a reference spectral signature plot; the values measured with the imager
correspond to the radiance values off the surface of the material, and must be
converted into relative reflectance before a direct comparison can be made.
Several methods to accomplish this are available; some of which require only the
data set, and others require some knowledge about the conditions in which the
images were taken.
Using the approach of trying to match the acquired data set directly with a
library requires an accurate conversion from radiance to reflectance. Since the
capture spectra will not match exactly, it must be compared and the correlation
between the reference and the observed, rated, and from this rating a decision
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about the identity of the material can be made. This is complicated by issues
discussed previously with mixing of different spectra, a topic that will be
investigated further as the project progresses.
4.5 Hyperspectral Applications
With the amount of information present in a hyperspectral data cube, there
are an abundance of applications ranging from medical to agricultural to
defense. Remote sensing hyperspectral cameras are currently being used by
the military to detect targets and are normally hidden to traditional color and
even multispectral imaging systems. Figures 4.6-4.8 below show the same
image viewed with these three types of systems. The target being observed is
a combat zone which contains a camouflaged tank.
Figure 4.6-4.8 – Defense Remote Sensing Application
Other remote sensing applications include forest heath observations for
fire prevention, tracking vegetation, measuring soil quality and terrain
mapping.
Several applications of hyperspectral imaging that are on a smaller scale
occur in the medical industry for early cancer detection and food borne
illness detection and prevention. Additionally, this technology is being
utilized by the government to identify counterfeit currency.
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5. Project Outline
5.1. Purpose The purpose of this design is to build a functional hyperspectral camera system
for use by D3 Engineering’s design staff.
5.2. Requirements
The design requirements for this project were left very open, which allows for a
very research oriented design project. The following list defines the requirements
specified by the customer.
• The hyperspectral camera shall use a single CMOS or CCD detector.
• The hyperspectral camera shall have a spectral resolution of 25 to 50nm over the
visible region; 400-850nm.
• The hyperspectral camera shall utilize a scanning mirror to acquire images.
• The hyperspectral camera shall use pre-existing hardware developed by D3
Engineering for the image capture and motion control.
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5.3. Project Timeline
Figure 5.1 shows the projected timeline for this project. ID
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6. Preliminary Design
Since this project is more research oriented than developing a final product, there are
minimal design constraints. The block diagram in Figure 6.1 depicts a high-level model
of the design.
Scan
Control
Imager0
OpticsMirro
r
Figure 6.1 – System Block Diagram
6.1. Imaging System
The imaging system will consist of an embedded platform that will interface to an
image sensor capable of capturing data over the specified bandwidth of 400 to
850nm.
6.1.1. CCD vs. CMOS Imagers CCD (charge coupled devices) and CMOS (complementary metal oxide
semiconductor) imagers both provide a way to digitally capture images, but each
have their respective benefits. CMOS imagers provide a simplified method of
interfacing due to the integration of all the timing circuitry necessary to clock data
out of the sensor, while CCD sensors require a significant amount of external
circuitry to obtain the data. Key advantages to a CMOS sensor include a lower
power usage, this integration of timing circuitry, and lower cost.
The most significant property in this application is image quality, where the
CCD surpasses that of a CMOS sensor. While this will become an issue in the
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future, the main goal of this project is to develop a working hyperspectral camera
prototype, and since D3 Engineering already the ability to interface to a CMOS
sensor, this technology will be utilized in the beginning stages of this design. The
bandwidth specification of this project is between 400 and 850nm, which as seen
by the spectral response curve of the chosen imager in Figure 6.2, will be
adequate for this specification. Future improvements of this project may include
the use of a CCD image sensor over a CMOS and the possibly of migrating to a
12 or 14 bit sensor.
Figure 6.2 – Spectral Response of Imager
6.1.2. Texas Instruments C6416 vs. DM642 DSP
There are two digital signal processors available from Texas Instruments that
were considered during this design, namely the C6416 and the DM642. D3
Engineering has hardware to interface to both processors, but connectivity to a PC
varies by platform. The system will be connected using a USB interface;
therefore due to hardware limitations the C6416 will be used. While the DM642
is TI’s flagship digital media processor, the complication of the video port driver
used to capture images may cause unnecessary problems syncing the movement
of the scanning mirror with the frame capture. The C6416 has a higher clock
frequency, running at 1GHz, than the DM642 which processes at 720MHz. At
the early stage of development the processing speed is of little concern since the
algorithm development will be done in MATLAB.
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6.1.3. Preliminary Imaging System Design Components
The design will utilize hardware that D3 Engineering will provide to capture
images using an embedded system. The final imaging system specifications are
as follows:
• 1.3 Mega Pixel 10-Bit CMOS Image sensor
• D3 Engineering’s Camera Developers Kit for image acquisition.
• Spectrum Digital 6416 DSK using a Texas Instruments C6416 1GHz Digital
Signal Processor
6.2. User Interface
In the early development phase of this project the post-processing will be
preformed in MATLAB after the image cubes have been captured. This will ease the
experimentation and development of the algorithms necessary to obtain spectral
signatures and classify materials from the attained data sets. The system will
interface with MATLAB over a high speed USB 2.0 connection.
6.3. Optical System
6.3.1. Background Motivation The desire to take on the optical design for this camera stemmed from the
fact that the work experience of a team member, who has spent a year and half
working for a company that, in the particular division, dealt mostly with telescope
design, integration, and testing. The basic knowledge of optics and optical testing
beyond how it applies to a telescope would be beneficial, especially for possible
career paths.
6.3.2. Abstract
The system had a wavelength requirement of 400-850 nm and a resolution of
25-50 nm. Size of the system is to be bench top, but portable. A scanning mirror
will be integrated into the front of the system in order to sweep the target while
the detector or data acquisition unit captures the images.
Through thorough internet and library research, optical path designs and
dispersion elements were considered. Such elements such as diffraction grating,
wedge filters, and prisms were investigated while straight through and diffracted
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optical paths were considered. Existing off the shelf designs from Edmund Optics
and Specim were also researched and found to be feasible options for the given
specifications.
Low-end calculations using a prism as a dispersing element, along with
correspondence with a representative of Specim, a company who produces a
spectral photometer, discussions with Dr. Wells of ITT, and discussion’s with
Edmund Optics technicians aided concept selection. The results of the above
effort yielded an off the shelf component from either Edmund Optics or Specim
as the best choice in order to provide the most effective prototype for the
customer.
6.3.3. Preliminary Research
Dr. Conrad Wells of ITT was approached with the problem statement for
this design and an ensuing discussion took place. Dispersion elements such as
grating and prisms were first brought to attention through this discussion as there
had been no previous knowledge of how the wavelength range for a given
resolution was to be achieved. Very low level, but necessary for understanding,
light paths were illustrated to give a direction for what the system design may
resemble. Minimal requirements for an optics system consist of three lenses, a
variable slit and a dispersion element inside an enclosure. Figures 6.3 and 6.4
illustrate beginning comprehension of an optical system. The figures also
illustrate other components of the optical system such as a variable slit, a scanning
mirror, and baffles for stray light.
Further research on the internet revealed a calculation for determining
optical path length.
(6.1) ∫=
c
dssnA )(
Where ‘A’ represents path length and n(s) is index of refraction as a
function of distance. The index is also dependent upon the glass element light is
passing through.
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TARGET MIRROR
ENCLOSURE
BAFFLE
PRISM
LENS (3) PLACES
Figure 6.3 – 90 Degree Optical Path
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MIRROR
Figure 6.4 – Straight through Optical Path
6.3.4. ImSpector Research
The complex nature of an optical system promoted research of off the shelf
optical systems. Specim makes a number of ImSpector cameras that capture
images and display data cubes as specified by the customer. Initially, the
impression was that the ImSpector was only capturing visible or infrared light
separately; however, that is not the case as search results yielded that the AISA
Eagle is a bench top unit with dimensions 165mm x 200mm x 390mm and
weighing no more than 6kg. This suggested that the design could realistically be
TARGET
ENCLOSURE
BAFFLE
PRISM
LENS (3) PLACES
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sized to bench top. Correspondence with Specim resulted in contact with Wes
Procino, a sales representative who graduated from the University of Rochester.
Procino was very helpful in answering questions and offering suggestions.
Reading about the ImSpector camera revealed that a “straight through”
optical design for all their cameras was discovered by utilizing a specific
dispersion element, Prism-Grating-Prism. The Prism-Grating-Prism or PGP was
developed by Mauri Aikio’s, whose dissertation was the creation of a
Hyperspectral PGP Imaging Spectrograph. Aikio invented the PGP element as a
low-cost option to make spectrographs and as a result of his work, a company in
1995 was founded to manufacture the PGP technology.
The PGP element is a mate of a prism, long pass filter (LPF), covering glass,
grating, substrate glass, short pass filter (SPF), and a second prism. Figure 6.5
depicts this.
Figure 6.5 – Prism-grating-prism element
Prism 1 LPF
Covering glass
Grating
SPF
Prism 2
Substrate glass
6.3.5. Alternate Dispersion Element Design
While the possible intellectual property held by the PGP system was being
researched, alternate dispersion element designs were investigated. Simple prism
designs were looked at and an array of calculations were done to assess the
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feasibility of a custom optics design in this project. Prism designs along with
alternate dispersion methods are assessed in subsequent sections.
After calculations had been preformed on the prism design, more technical
advise was obtained about the possible design. Holographic gratings were
discussed, but the use of a monochromator is preferred. If the design utilized a
holographic grating, a slit would be required and calibration procedures
preformed. The attraction of the monochromator’s holographic dispersion
element revolves around the pre-calibration of the unit off the shelf; a subject that
is very complex.
Design’s in Figures 6.6 – 6.9 all utilize a Littrow prism which was not
discovered until late in the preliminary design process. A Littrow prism refracts
the light into it and then reflects it back out into the path that it came in,
effectively cutting the focal length into a fraction of a normal refracting prism. A
silver or aluminum backing on the prism allows for this reduction in focal length.
Figure 6.6 – Littrow Prism Spectrometer
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Figure 6.7 – Littrow Prism Spectrometer with Mirror
Figure 6.8 – Wadsworth constant-deviation Mounting Device
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Figure 6.9 – Littrow-mounted prism spectrograph
6.3.6. Prism Design
At first glance, a simple prism design seemed like it would be the cheapest
and easiest dispersion element in an optical system. In order to prove this,
calculations optimizing the focal length, detector size, prism angle, and angle the
prism is held (or incident angle) had to be preformed. While the calculations
were lengthy, it was necessary to properly optimize the parameters. Calculations
were preformed for a range of wavelengths from 400 nm to 700 nm.
1iϑ δ 2tϑ
α
Normal to surface
Normal to surface
Figure 6.10 – Geometry of a Prism
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Hyperspectral Camera Project (P06552) D3 Engineering
The prism material being used was BK7. The index of refraction for BK7
as a function of wavelength is as follows:
3
32
21
11C
BC
BC
Bn−
+−
+−
+=λ
λλ
λλ
λ (6.2)
Where B1, B2, B3, C1, C2, C3 are constants of the index of refraction.
The angle of incidence was predetermined and changed as needed for
optimization. Exit angle, 2tϑ , out of BK7 in a function of the index of refraction,
incidence angle, 1iϑ , and the prism angle, α . The prism angle was also
predetermined, but changed for optimization. Usually, an angle between 45
degrees or 60 degrees is utilized.
(6.3) ]cossin)sin)(sin[(sin 12/1
122
2 αϑϑαϑ iit na −−=
Detector size is determined by simple trigonometry, as seen in Figure 6.11 and Equation 6.4
fh
t =2tanϑ (6.4)
2tϑ
f
h
Figure 6.11 – Geometry of focal length and detector height
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Hyperspectral Camera Project (P06552) D3 Engineering
The variable ‘f’ represents the focal length and ‘h’ represents the height of
the detector. Units are in millimeters. This is a function of the exit angle and thus
a function of wavelength; for every wavelength there is a change in height, and
the overall delta in height returns the detector size.
Finally, a delta is found between the angle exiting the prism, 2tϑ , and the
normal to the prism in order to verify the calculations were done correctly.
(6.5) ααϑϑαϑδ −−−+= − ]cossin)sin)([(sinsin 1
2/11
2211 iii n
The delta as a function of wavelength, seen in Equation 6.5, varied
between 32 and 35 degrees. To add a degree of verification to the design,
calculations were done with an incident angle of zero degrees into a 60 degree
prism. The exit angle was 60 degrees with a delta of 30 degrees which was
expected. The figures below depict results to these calculations as well as a copy
of the spreadsheet used.
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Hyperspectral Camera Project (P06552)
PDR definition • design • development Page 27 of 44
D3 Engineering
lamda vs. h
-30
-20
-10
0
10
20
30
0.4 0.45 0.5 0.5
5 0.6 0.65 0.7 0.7
5 0.8 0.85 0.9 0.9
5 11.0
5
lamda (nm)
h (m
m)
Series1
Figure 6.12 – Lambda vs. Height
Wavelength vs. Optimized Refracted Angle
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.4
0.45 0.5
0.55 0.6
0.65 0.7
0.75 0.8
0.85 0.9
0.95 1
1.05
Lamda (nm)
Thet
a_t2
-thet
a_no
t (de
g)
Series1
Figure 6.13 – Wavelength vs. Optimized Refracted Angle
Hyperspectral Camera Project (P06552) D3 Engineering
Detector size Vs Input angle
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70
Incident Angle (deg)
Foca
l Pla
ne S
ize
(mm
)
Series1
Figure 6.14 – Detector Size vs. Input Angle
detector size vs. prism angle
0
5
10
15
20
25
30
35
40
45
50
0 10 20 30 40 50 60 70 80
prism angle (deg)
dete
ctor
siz
e (m
m)
Series1Series2Series3Series4Series5Series6Series7Series8Series9Series10Series11Series12Series13Series14Series15
Figure 6.15 – Detector Size vs. Prism Angle
PDR definition • design • development Page 28 of 44
Hyperspectral Camera Project (P06552) D3 Engineering
6.3.7. Final Optical Design The following factors were considered for the final design decision: Results
from extensive calculations performed on utilizing a prism, alternate dispersion
elements and optical path’s research, a Pugh diagram of design trades, seen in
Figure 6.16, and the customer’s specifications. Specifically, the desire of the
customer to investigate the data acquisition and image processing rather than the
construction of an optical system.
Design Criteria
Datum Handheld Spectro- scope
Prism
Grating & Slit
Chucks Apparatus
Impspector Mono- chromator
Cost
$500 + ($272)
+ ($200)
- ($900)
+ - - ($1165)
Size “Bench Top”
+ S S S S +
Complexity Ready off the shelf
+ - assembly and calibration required
- assembly and calibration required
- design of light source input needed
+ +
Light source
White light/Sun light
S S S - S S
Achieved Wavelength
400-700nm
S S S S S S
Achieved Resolution
25-50nm + + + TBA + +
Tuning Desired wavelength can be chosen
- - - + +
+
Totals + = 1 - = -1 S = 0
3 1 0 0 2 3
Figure 6.16 – Pugh Diagram
Calculations performed for utilizing a prism where done with the focus of
optimizing the focal length, incident angle, prism angle, and angle the prism is
set at. All of these parameters were optimized at reasonable values with the
exception of the focal length. It was decided that 75-200 millimeters is an
PDR definition • design • development Page 29 of 44
Hyperspectral Camera Project (P06552) D3 Engineering
optimal focal length and the length that came out of the analysis was
approximately one meter. This was unacceptable for a bench top design of this
system. Alternate forms of dispersion elements had to be looked into.
After further research about alternate dispersion elements, a blazed
holographic grating from Edmund Optics was discovered. This dispersion
element could be set at an optimal focal length of 150mm. A discussion with
Edmund Optics, however, suggested the use of a monochromator or a handheld
spectroscope over a custom built optics system. Both items are already calibrated
and are off the shelf ready to be implemented into a system. The final design
recommendation is to use an off the shelf system from either Edmund Optics of
Specim. In addition, a custom optics system will be experimented with to extend
the customers knowledge about the subject of hyperspectral imaging.
6.4. Scanning Mirror Control
6.4.1. System Overview This project requires the use of a scanning mirror to focus light from
successive slits of a stationary target image into the optical system and eventually
the CMOS imager. Since the slit widths from the target image are ideally
dimensionless, it is advantageous to implement a motion control device with the
highest possible resolution. As a starting point, a 100 pole-pair 2-phase stepper
motor has been chosen to rotate the mirror which provides a 3.6° per full-step
resolution.
Illustrated in figure 6.17 is the schematic for the circuitry needed to drive
one phase (A) of a stepper motor. An identical circuit is used to drive the second
phase (B). Based upon this configuration, four PWM signals are required to drive
the stepper motor and rotate the scanning mirror.
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D3 Engineering
100
10k
100
10k
10k
100
10k
100
47k
47k
1k
1k .033 .033
IRFZ44E
IRFZ44EIRFZ44E
IRFZ44E
1uF
IN 2
SHDN 3LO5
HO7
COM 4
VB 8
VCC 1
VS 6
IR21040
PWM_A1+
820nF
ZMM5251B100 uF
Vmm
1uF
0
0
0
0
0
Vdd Vdd
31
2-
+
MtrDA1- MtrDA1+
820nF
IN2
SHDN3 LO 5
HO 7
COM4
VB8
VCC1
VS6
Half - Bridge DriverHalf - Bridge Driver
PWM_A1-
IR2104
V3_3/2
ViA1
Motor Current Sense0
0
Figure 6.17 – Phase ‘A’ Driver Schematic
6.4.2. Stepper Motor Control
Figure 6.18 – Two Phase Stepper Motor Configuration
There exist a number of methods to control the magnitude of the angle
rotated per step. Full-stepping is a method by which both phase windings are
energized with the polarity being switched by an alternating current and thus
requiring four cycles to rotate by a full step. Using this method, the stepper motor
chosen for this project would rotate 3.6° for each successive slit. Half-stepping
doubles the resolution by alternating between single and dual-phase operation.
Hyperspectral Camera Project (P06552) D3 Engineering
Micro-stepping is a technique in which sinusoidal signals are used to drive
the phase windings as opposed to discrete transitions. This method allows the
motor’s natural step size to be further subdivided into anywhere from 16 to 256
micro-steps. Figure 6.19 illustrates the applied signals to the phase windings in a
micro-step mode.
Figure 6.19 – Micro-step Phase Winding Signals
Each of the methods described above operate in an open-loop controller
configuration. In section 6.4.5, a technique for calculating and feeding the rotor
position back into the controller is outlined. A block diagram for the open-loop
configuration is shown in Figure 6.20.
Figure 6.20 – Open-Loop Stepper Motor Controller
6.4.3. Design Requirements
Implementing the stepper motor controller is going to be an incremental
process. The required number of slits per target image to generate a useful data
set remains a variable in this design; and is largely a function of how accurately
the stepper motor can be positioned. The minimum requirement for the scanning
mirror is a +/- 20° range of motion with respect to the center of the target image.
Figure 6.21 illustrates the target workspace.
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Hyperspectral Camera Project (P06552)
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D3 Engineering
Figure 6.21 – Required Target Workspace
As a baseline, an open-loop controller configuration will be implemented
with 16 micro-steps per full step. This design generates an effective step angle of
.225° and 177 total slits. The corresponding slit widths remain only a function of
the distance between the scanning mirror and the target image. As mentioned
above, the number of micro-steps will gradually increase as the controller design
progresses until satisfactory image resolution is achieved.
Notice also from Figure 6.21 that as the scanning mirror rotates towards
the edges of the target, the corresponding slit widths increase and must be
accounted for in the image processing algorithm. Section 6.4.4 provides a more
detailed analysis of this effect.
Hyperspectral Camera Project (P06552) D3 Engineering
6.4.4. Slit Width Analysis
Figure 6.22 – Scanning Mirror Parameters
The size of the target image as a function of the distance between the
target and the scanning mirror is given by:
Target Width 2y= tan(20 )y d= ⋅ ° (6.6)
The number of slits per full 40° swing is given by:
2 20
Full ImageSlitsn ⋅ °
=ΔΘ
(6.7)
In order to develop a mathematical relationship between successive slit
widths, start at the center of the image and rotate outwardly.
(6.8) 1 tan( )y dΔ = ⋅ ΔΘ
(6.9) 2 1tan(2 )y d yΔ = ⋅ ΔΘ −Δ
(6.10) 3 2tan(3 )y d y yΔ = ⋅ ΔΘ − Δ − Δ 1
j (6.11) ( )1
1tan( ) 1
i
ij
y d i y i−
=
Δ = ⋅ ΔΘ − Δ ∀ >∑
The equation for the ith slit width can be simplified further with the
following realizations:
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Hyperspectral Camera Project (P06552)
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D3 Engineering
( )
( )
( ) ( )
1
1
2 2
1 1
tan( )
tan( ) tan ( 1)
tan tan ( 1)
i
i jj
i i
j jj j
y d i y
d i d i y y
d i d i
−
=
− −
= =
Δ = ⋅ ΔΘ − Δ
⎛ ⎞= ⋅ ΔΘ − − ΔΘ − Δ − Δ⎜ ⎟
⎝ ⎠= ⋅ ΔΘ − ⋅ − ΔΘ
∑
∑ ∑ (6.12)
Based upon the equations derived above the following table is compiled to
illustrate the relationship between the number of micro-steps per full step, step
angle size, target size, and image slit width.
3.6° per Full Step:
d [m]
y (half target width) [m] μStep Size Δθ
# Slits/ Half
Image Avg. Δy
[mm] Min Δy [mm] Max Δy
[mm] 0.0625 0.225 88 4.136 3.927 4.430 0.03125 0.1125 177 2.056 1.963 2.220
0.015625 0.05625 355 1.025 0.982 1.111 0.0078125 0.028125 711 0.512 0.491 0.556
1 0.36397023
0.00390625 0.0140625 1422 0.256 0.245 0.278 0.0625 0.225 88 8.272 7.854 8.860 0.03125 0.1125 177 4.113 3.927 4.439
0.015625 0.05625 355 2.051 1.963 2.222 0.0078125 0.028125 711 1.024 0.982 1.112
2 0.72794047
0.00390625 0.0140625 1422 0.512 0.491 0.556 0.0625 0.225 88 12.408 11.781 13.289 0.03125 0.1125 177 6.169 5.890 6.659
0.015625 0.05625 355 3.076 2.945 3.333 0.0078125 0.028125 711 1.536 1.473 1.667
3 1.09191070
0.00390625 0.0140625 1422 0.768 0.736 0.834 0.0625 0.225 88 16.544 15.708 17.719 0.03125 0.1125 177 8.225 7.854 8.878
0.015625 0.05625 355 4.101 3.927 4.444 0.0078125 0.028125 711 2.048 1.963 2.223
4 1.45588094
0.00390625 0.0140625 1422 1.024 0.982 1.112 0.0625 0.225 88 20.680 19.635 22.149 0.03125 0.1125 177 10.282 9.817 11.098
0.015625 0.05625 355 5.126 4.909 5.555 0.0078125 0.028125 711 2.560 2.454 2.779
5 1.81985117
0.00390625 0.0140625 1422 1.280 1.227 1.390
Figure 6.23 – Image Slit Width Analysis
Hyperspectral Camera Project (P06552) D3 Engineering
6.4.5. Kruse Control Kruse control is a technique by which the rotor angle can be accurately
determined without the use of an encoder at resolutions on the order of 40,000 -
60,000 steps per revolution. In order to accomplish this, the sense winding
voltages are sampled with an A/D converter and fed back into the controller.
From these values, it is possible to calculate the rotor position in real-time. The
methodology is outlined below and will eventually be incorporated into this
project as the controller algorithm advances.
Figure 6.24 – Two Phase Stepper Motor Schematic and Model
i ediV iR L Vdt
= + + mf (6.13)
sense emfdiV L Vdt
= + (6.14)
AA A e
diV Ri L Vdt
= + + mfA (6.15)
BB B em
diV Ri L Vdt
= + + fB (6.16)
( )sinemfA R E RV k nω= ⋅ ⋅ ⋅Θ (6.17)
( )cosemfB R E RV k nω= ⋅ ⋅ ⋅Θ (6.18)
(sinA )senseA E R RdiV L k ndt
ω= + ⋅ ⋅ ⋅Θ (6.19)
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D3 Engineering
( )cosBsenseB E R R
diV L k ndt
ω= + ⋅ ⋅ ⋅Θ (6.20)
( )2 2cos posA A senseA pos RV k i V dt V n k k= + = − ⋅ ⋅Θ ∀ = −∫ 1L (6.21)
( )2 2sin posB B senseB pos RV k i V dt V n k k= + = ⋅ ⋅Θ ∀ = −∫ 1L (6.22)
1 2E E
posk k k kV
n n= = −
L (6.23)
Figure 6.25 – Closed-loop Kruse Stepper Motor Controller
Using the same analysis performed in Figure 6.23, the relationship
between the number of micro-steps per full revolution, step angle size, target size,
and image slit width for the Kruse controlled system is illustrated in Figure 6.26.
Hyperspectral Camera Project (P06552) D3 Engineering
Kruse Control
d [m]
y (half target
width) [m] μSteps / Full Rev. Δθ
# Slits/ Half
Image Avg. Δy
[mm] Min Δy [mm] Max Δy
[mm] 40000 0.00900 2222 0.163802986 0.1570796 0.177874045000 0.00800 2500 0.145588094 0.1396263 0.158115250000 0.00720 2777 0.131065983 0.1256637 0.142294355000 0.00655 3055 0.119139193 0.1142397 0.1293622
1 0.36397023
60000 0.00600 3333 0.109201990 0.1047198 0.118584940000 0.00900 2222 0.327605971 0.3141593 0.355747945000 0.00800 2500 0.291176187 0.2792527 0.316230450000 0.00720 2777 0.262131966 0.2513274 0.284588655000 0.00655 3055 0.238278386 0.2284795 0.2587244
2 0.72794047
60000 0.00600 3333 0.218403981 0.2094395 0.237169840000 0.00900 2222 0.491408957 0.4712389 0.533621945000 0.00800 2500 0.436764281 0.4188790 0.474345650000 0.00720 2777 0.393197948 0.3769911 0.426882955000 0.00655 3055 0.357417579 0.3427192 0.3880866
3 1.09191070
60000 0.00600 3333 0.327605971 0.3141593 0.355754740000 0.00900 2222 0.655211943 0.6283185 0.711495945000 0.00800 2500 0.582352375 0.5585054 0.632460850000 0.00720 2777 0.524263931 0.5026548 0.569177255000 0.00655 3055 0.476556772 0.4569589 0.5174488
4 1.45588094
60000 0.00600 3333 0.436807962 0.4188790 0.474339640000 0.00900 2222 0.819014929 0.7853982 0.889369845000 0.00800 2500 0.727940469 0.6981317 0.790576150000 0.00720 2777 0.655329914 0.6283185 0.711471555000 0.00655 3055 0.595695964 0.5711987 0.6468111
5 1.81985117
60000 0.00600 3333 0.546009952 0.5235988 0.5929245Figure 6.26 – Slit Width Analysis for Kruse Controlled System
6.5. Data Normalization and Material Identification The data that is captured by the imager is measured as the intensity of the pixel,
which cannot be compared directly to any reference plot of a materials spectrum. The
obtained image must be converted in such a way that the resulting data set is in terms
of reflectance. Several methods can be use to do such a conversion. Algorithms and
methods used for this calibration and conversion will be explored during the first
phases of the project development.
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Hyperspectral Camera Project (P06552) D3 Engineering
6.6. Mechanical System and Enclosure
6.6.1. Requirements While the some of the specifications of the project were unconstrained, the
mechanical system is to be operated in a lab environment, therefore the system
should be bench top sized, but also designed to be portable if such an application
is desired. Additionally, the area of hyperspectral imaging is a relatively new
topic to this customer; this means maximum adjustability and functionality are of
utmost importance.
Due to this recent entry into the hyperspectral imaging, a tremendous
amount of time is required before they can realize the cameras abilities. Lenses,
controllers, motors, and the like will inevitably need to be swapped while fine
tuning and hyperspectral signatures are collected. Ultimately, the camera will be
assembled using parts that are precise, but unimposing to future design changes
and laboratory experimentation.
6.6.2. Mounting Hardware Much of the required mounting hardware is available from the multitude
of optical equipment distributors. In some cases however, the commercially
available mounting hardware isn’t adjustable enough to suit the customer’s needs.
In this case, custom optical, or modified commercial equipment will be machined
using the Rochester Institute of Technology’s machine shop.
The camera is primarily composed of a series of lenses, a dispersing
element such as a prisms or grating, a scanning mirror, and a detection device,
such as an imager.
6.6.2.1. Scanning Mirror Mount The scanning mirror is a precisely controlled stepper motor with a mirror
affixed to its output shaft. To ensure that the mirror’s reflective surface is
perpendicular to the ground, a mirror mount will be fabricated. The mount
will allow height and angle adjustment for fine tuning.
The stepper motor will be mounted to the board using an adapter plate, as
the motor’s bolt pattern does not match that of the breadboard. This plate will
also allow for shims should they be required.
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Hyperspectral Camera Project (P06552) D3 Engineering
6.6.2.2. ImSpector Lens Mounting System The ImSpector is a conveniently packaged imaging spectrograph, which is
offered in two different styles: OEM and cased. Both versions come standard
with a C-mount adapter for easy interfacing to a sensor.
The mount will be designed to bolt directly to the optical bench, and the
ImSpector will thread into the face of the mount using the standard C-mount.
6.6.2.3. Custom Design Lens Mounts The lenses will be held using a commercially available mounting system.
Optical equipment supplier, Standa, offers a universal adjustable lens mount.
This will ensure that the lenses are held perpendicular to the ground, and
allows for quick lens swapping. They feature a machined flat on the bottom
of the mount so they can be mounted directly to the breadboard, or to a
custom mount.
6.6.2.4.Imaging Device Mount
The imaging device must be held in a precise position directly in front of the
ImSpector. Finding this location will require a great deal of trial and error, so
the mount must allow a great deal of adjustability. As such, the imager will
be mounted on a sliding track on the ImSpector’s lens centerline.
6.6.3. System Enclosure Since the camera is going to be used in a laboratory environment, the
enclosure will simply consist of an optical breadboard and a removable aluminum
top. The simple design will be functional, inexpensive, and most importantly,
reliable.
The optical breadboard is a perfect foundation for the camera. The flat
surface and robust construction are critical for the camera’s proper operation. The
evenly spaced holes will allow for an innumerable number of optical
combinations. Most of the commercially available mounts are designed to be
compatible with the optical breadboard, but both the mounts and the breadboard
can be machined.
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Hyperspectral Camera Project (P06552) D3 Engineering
The top will be an aluminum box that will completely enclose the camera,
and will be free of light leaks. It will be lightweight, and will feature a handle on
top for easy removal. The top and breadboard interface will have a small layer of
compressible material. This will enable the use of flexible draw-style latches.
The latches are rugged, inexpensive, and forgiving if the top is not exactly
aligned.
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Hyperspectral Camera Project (P06552) D3 Engineering
6.7. Preliminary Bill of Materials
System/Description Imaging System Actual or Est. Description Cost Source 10 Bit 1.3 Mega Pixel CMOS Sensor - Provided by D3 Eng. DSK Camera Developer’s Kit - Provided by D3 Eng. Spectrum Digital 6416 DSK - Provided by D3 Eng. Optical System Description Cost Source ImSpector $2000 SPECIM Lenses Edmund Optics Slit Edmund Optics Prism(s) Edmund Optics/Custom Other Scanning Mirror Control Cost Source Spectrum Digital F2812 DSP - Provided by D3 Eng. D3 Engineering Motor Control Board - Provided by D3 Eng. Mirror $50 Edmund Optics Motor - Provided by D3 Eng. Mechanical Components Cost Source Enclosure Materials Mounting Fixtures Manufacturing Costs
Figure 6.27 – Preliminary Bill of Materials
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Hyperspectral Camera Project (P06552) D3 Engineering
7. Design Feasibility and Expected Technical Issues
This system is being designed to run in a laboratory environment to minimize many
of the effects that are presented when a hyperspectral camera is operated in a real-world
outdoor condition. Inevitably, there are some technical issues that will be come apparent
when the hardware is being evaluated and the algorithms are developed to transform the
images from measured light intensity values to reflectance for identification purposes, in
addition to the normalization of the measured values.
Technical issues that are expected to arise are such things as separating any mixing of
wavelength bands that may occur, and in the event that the camera is viewing a target at a
distance in which mixing of material spectra occur, these problems will be addressed.
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Hyperspectral Camera Project (P06552) D3 Engineering
8. References
[1] MicroImages Inc., Introduction to Hyperspectral Imaging. 2006 [2] "Electromagnetic Spectrum" Microsoft® Encarta® Online Encyclopedia 2005
http://encarta.msn.com © 1997-2005 Microsoft Corporation. All Rights Reserved. [3] "Spectroscopy" Microsoft® Encarta® Online Encyclopedia 2005 http://encarta.msn.com © 1997-
2005 Microsoft Corporation. All Rights Reserved. [4] "Spectroscopy" Wikipedia, 2006 http://en.wikipedia.org. [5] ASTER Spectral Library, speclib.jpl.nasa.gov. © 1998, 1999, 2000 California Institute of
Technology. [6] AVIRIS Image Cube, Jet Propulsion Laboratory, California Institute of Technology.
http://aviris.jpl.nasa.gov [7] USGS Digital splib04 Spectral Library, http://speclab.cr.usgs.gov/spectral.lib04/spectral-
lib04.html [8] IMINT – Hyperspectral Imaging, http://www.fas.org/irp/imint/hyper.htm [9] CMOS vs. CCD and the Future of Imaging, Kodak Research and Development.
http://www.kodak.com/US/en/corp/researchDevelopment/technologyFeatures/cmos.shtml. [10] Aikio, Mauri. Hyperspectral prism-grating-prism imaging spectrograph. Finland: Julkaisija-
Utgivare., 2001. [11] Deardon, Steve. Spectroscopy, Astronomy, and Optics. 3 March 2004.
http://astrosurf.com/dearden/ [12] Dr. Wells, Conrad. Personal interview. 11 January 2006. [13] Edmund Optics Technician. Telephone interview. 5 February 2006. [14] Hecht, Eugene and Alfred Zajac. Optics. Reading, MA: Addison-Wesley Publishing Co., 1974. [15] Hyperspectral Remote Sensing Applications,
http://www.space.gc.ca/asc/eng/satellites/hyper_brochure.asp [16] Applications of Hyperspectral Imagery,
http://home.student.uva.nl/derck.truijens/CPBG_files/Hyperspectral%20Applications.pdf.
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