Spectral and Polarimetric Imagery Collection Experiment 

(SPICE) Longwave Infrared Spectral Dataset

by Dalton Rosario, Joao Romano, and Christoph Borel-Donohue

ARL-TR-7051 September 2014

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Army Research Laboratory

Adelphi, MD 20783-1138

ARL-TR-7051 September 2014

Spectral and Polarimetric Imagery Collection Experiment 

(SPICE) Longwave Infrared Spectral Dataset

Dalton Rosario

Sensors and Electron Devices Directorate, ARL

Joao Romano

US Army Armament Research Development Engineering Center (ARDEC)

Picatinny Arsenal, NJ

Christoph Borel-Donohue

US Air Force Institute of Technology (AFIT)

Wright Patterson Air Force Base, OH

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14. ABSTRACT

This report summarizes our observations using the longwave infrared (LWIR) hyperspectral data subset of the Spectral and 

Polarimetric Imagery Collection Experiment (SPICE) dataset, which emphasizes the inherent challenges associated with 

applying autonomous spectral pattern recognition methods to LWIR hyperspectral imagery, as well as data quality and 

qualitative validation of expected atmospheric spectral features. SPICE is a collaborative effort between the US Army Research 

Laboratory, US Army Armament Research, Development, and Engineering Center, and the US Air Force Institute of 

Technology. It focused on the long-term LWIR hyperspectral and polarimetric data collection of a target site, under various 

weather and non-ideal conditions, and exploitation of this dataset for pattern recognition tasks. We concluded from this effort 

that the quality of SPICE hyperspectral LWIR data is categorically comparable to other datasets recorded by a different sensor 

of similar specifications and is adequate for algorithmic research. The vast dataset and associated ground-truth information are a 

great asset for research.

15. SUBJECT TERMS

Longwave infrared, LWIR hyperspectral, dataset, temperature and emissivity separation, radiometric calibration, SPICE

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iii

Contents

List of Figures iv

Executive Summary vii

1. Introduction 1

2. Data Collection and Sensor Characterization 2

2.1 Facility.............................................................................................................................2

2.2 Targets.............................................................................................................................2

2.3 Sensor Characterization...................................................................................................2

2.4 Temperature Emissivity Retrieval Problem and Algorithm............................................6

2.4.1 Previous Temperature-emissivity Separation Algorithm....................................7

2.4.2 ARTEMISS Summary of Improvements............................................................9

2.5 Qualitative Data Assessment.........................................................................................11

3. Conclusions 29

4. Future Plans 31

5. References 32

List of Symbols, Abbreviations, and Acronyms 34

Distribution List 36
iv

List of Figures

Fig. 1 Telops LWIR Hyper-Cam’s basic specs .............................................................................3

Fig. 2 Illustration of the indoor setting used at AFIT to test and validate performance of 

both the ARL- and AFIT-owned LWIR Hyper-Cams...............................................................4

Fig. 3 Data processing flow of the radiometric calibration method applied to Telops HyperCam raw data .............................................................................................................................5

Fig. 4 Effect of channeling in spectral data recorded by the Telops Hyper-Cam; the spectral 

ripple (noise), although embedded in the data, does not significantly change the 

predominant underlying spectral characteristics observed in the illustrated spectrum (red 

box highlighting the spectral feature in the top image) .............................................................6

Fig. 5 Basic ARTEMISS algorithm flow diagram ........................................................................8

Fig. 6 Improved ARTEMISS flow diagram ................................................................................10

Fig. 7 New ARTEMISS algorithm based on iterative retrieval of the atmosphere based on 

the AMOEBA algorithm followed by the fast three-band ratio technique (CIBR) to 

retrieve temperature .................................................................................................................11

Fig. 8 Typical AFIT calibration/TES code’s diagnostic output using SPICE LWIR 

hyperspectral data ....................................................................................................................12

Fig. 9 Example of daytime partly cloudy condition; downwelling radiance is a mixture of 

Planck’s blackbody radiance from the clouds and clear sky. The color code corresponds 

to color-box-highlighted objects in the scene, plot colors, and colored object-name fonts. 

The spectral interval of the sensor’s operation under specs is between 8.0 and 

11.5 micron. .............................................................................................................................14

Fig. 10 (Top) Example of night and cloudy conditions; downwelling radiance matches the 

environment, which is at the same temperature. (Bottom) Example of daytime cloudy 

conditions; clouds behave like a blackbody when the environment is hotter than cloud 

temperature. (The color code corresponds to the same one in Fig. 12.) The spectral 

interval under specs is between 8.0 and 11.5 micron. .............................................................15

Fig. 11 (Top) Example of a thermal cross-over; downwelling radiance matches the 

environment and all surfaces have the same temperature. (Bottom) Example of daytime 

clear and hot conditions; trees show a Planck’s blackbody radiance, surrogate tanks are 

hotter than environment and show absorption features, and sky plate reflects cold sky. 

(The color code corresponds to the same one in Fig. 9.) The spectral interval under specs 

is between 8.0 and 11.5 micron. ..............................................................................................16
v

Fig. 12 (Left) Single band image (of the same scene shown in Fig. 8) depicting data blocks 

used to estimate the average mean spectra of spatially apart objects in the scene; some of 

these objects have the same material type. Surrogate targets: red region (0° aspect angle), 

green area (same paint/tank posed at 90°), and dark blue area (same paint/tank posed at 

135°). Vegetation trees: yellow and dark magenta (upper right) area. Sparse grass and 

soil: light blue area. Dirt/pebble road: magenta area (far left middle). (Right) Sample 

average spectral profiles representing selected spatial areas in the scene shown in the lefthand side image; spectra within each color area were the only ones used to produce the 

average spectral profile per color area. The plot color code corresponds to the same one 

in the left image. ......................................................................................................................17

Fig. 13 Illustration of spectral profiles of different material types at the ARDEC SPICE site, 

where (left) data from the AFIT-owned Hyper-Cam were collected in July 2011 and 

(right) data from the ARL-owned Hyper-Cam were collected a year later in July 2012 ........18

Fig. 14 MODTRAN models: MWIR/LWIR diffuse downwelling radiance as a function of 

atmospheric conditions ............................................................................................................20

Fig. 15 MODTRAN vs. real measurements of the downwelling sky radiance analysis using 

the AFIT-owned Hyper-Cam, where such a validation was needed for the development of 

the QTES code .........................................................................................................................21

Fig. 16 (Top) MWIR/LWIR diffuse downwelling radiance as a function of cloud conditions. 

(Bottom) LWIR downwelling radiance measurements for different cirrus cloud 

thicknesses compared to clear sky and liquid clouds...............................................................22

Fig. 17 Spectra from the same object (target T0) and approximate pixel location on the 

target, both collected by two independent Hyper-Cams (AFIT vs. ARL) a year apart (July 

2011 and July 2012). The strong oscillation patterns between 8.0 and 9.4 μm are due to 

water vapor concentration in the atmosphere between the target surface and the sensor. 

Oscillation amplitude is also impacted by the humidity level in the atmosphere. The 

target (surrogate howitzer) was hotter during AFIT data acquisition and spectral 

absorptions appear stronger because of its finer spectral resolution than ARL’s data. 

Distinct spectral patterns in both spectra are clearly visible between 9.4 and 11.0 μm 

using both sensors a year apart are encouraging. (The spectrum from the AFIT sensor 

was sub-sampled from 206 to 103 bands—the number of bands recorded by the ARL 

sensor, as described in text, so both spectra could be plotted in the same graph.)..................24

Fig. 18 Spectral mean averages of data collected from a 0.46 m (1.5 ft) range (top image 

and a 570 m (1,870 ft) range (bottom image), using the SPICE LWIR Hyper-Cam to 

collect data of the same material (green paint on the surrogate howitzer target). Due to 

many factors, spectral differences are expected to exist, given the large difference in 

range, to include reflection of emitted downwelling sky radiance off the target at far 

ranges (which is blocked by the sensor’s physical presence at close range), and a 

significantly higher concentration of atmosphere between target and sensors........................25

Fig. 19 Examples of individual spectrum of the same target recorded in three different 

years: July 2011 (AFIT sensor), July 2012 (ARL sensor), and July 2013 (ARL sensor) 

from two ranges (0.46 and 570 m). Among other factors, the cumulative concentration of 

water vapor in the atmosphere, which increases as a function of range, plays a major role 

in changing the spectral profile of the target. ..........................................................................27

Fig. 20 Selected number of band-averaged images of the same target site during two 

complete diurnal cycles on 22–23 July 2012...........................................................................28
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vii

Executive Summary

This technical report summarizes our observations using the longwave infrared (LWIR) 

hyperspectral data subset of the Spectral and Polarimetric Imagery Collection Experiment 

(SPICE) dataset, which emphasizes the inherent challenges associated with using the LWIR 

region of the electromagnetic spectrum and hyperspectral sensing modality for applying 

autonomous spectral pattern recognition methods to imagery acquired by corresponding remote 

sensing cameras, as well as data quality, qualitative validation of expected atmospheric spectral 

features, and qualitative comparison against another dataset of the same site using a different 

LWIR hyperspectral sensor. 

SPICE is a collaborative effort between the US Army Research Laboratory (ARL), US Army 

Armament Research, Development, and Engineering Center, and more recently, the US Air 

Force Institute of Technology. It focused on the collection and exploitation of LWIR 

hyperspectral and polarimetric imagery. 

We concluded from this effort that the quality of SPICE hyperspectral LWIR data is 

categorically comparable to other datasets recorded by a different sensor of similar specifications 

and is adequate for algorithm research, given the scope of SPICE. 

The scope was set to use both sensing modalities to conduct a long-term LWIR data collection of 

the same site, consisting of multiple distinct material types (manmade and natural), under various 

weather and non-ideal conditions. Using the vast dataset and associated ground-truth 

information, our near future plan is to assess performance of the state-of-the-art algorithms, and 

more importantly, determine performance degradation sources from this assessment, to include 

unrealistic data assumptions.

Our expectation is that results from this assessment will spur new and more realistic algorithmic 

ideas that could reach beyond the region of the spectrum and sensing modalities used in the 

SPICE effort. This is likely to significantly augment the performance of autonomous pattern 

recognition methods for remote sensing applications. 

We are confident that over time the SPICE dataset will prove to be an asset to ARL, its partners, 

and the wider open scientific community. Of course, this optimism should not undermine the 

amount of work that lies ahead using an unprecedented database of over 25,000 LWIR 

hyperspectral data cubes. 
viii

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1

1. Introduction

Detection and identification of military and civilian targets from airborne platforms using 

hyperspectral sensors is of great interest. Relative to broadband or multispectral sensing, 

hyperspectral sensing can increase the detectability of pixel and subpixel size targets by 

exploiting finer detail in the spectral signatures of targets and natural backgrounds. 

A multitude of adaptive detection and recognition algorithms have appeared in the literature or 

have found their way into software packages and end-user systems: algorithms for resolved and 

subpixel targets with known or unknown spectral characterization, in a background with known 

or unknown statistics, that are theoretically justified or ad hoc, and with low or high 

computational complexity

But despite the high activity level of the past two decades, which can be measured by the high 

number of publications produced by researchers in the geosciences and remote sensing 

community, during which new autonomous algorithms with promises of better performance were 

proposed, a sizeable percentage of researchers in this community seem now convinced that the 

pattern recognition problem will not be solved by purely relying on human innovation and 

idealized mathematical concepts. 

Data are needed and are indispensible, especially, if the interest is to assess the value of exotic 

sensing modalities, such as hyperspectral or polarimetric, for material pattern recognition in 

remote sensing applications. 

The US Army Research Laboratory (ARL) and the US Army Armament Research,

Development, and Engineering Center (ARDEC) have heeded this data plea from the target 

community and are acted on it. The result became known as the Spectral and Polarimetric 

Imagery Collection Experiment (SPICE). SPICE is a collaborative effort between ARL, 

ARDEC, and more recently, the US Air Force Institute of Technology (AFIT) that focuses on the 

longwave infrared (LWIR) hyperspectral and polarimetric sensing modalities. SPICE is aimed at 

collecting a comprehensive dataset of both sensing modalities spanning multiple years in order to 

capture sensor performance in a wide variety of meteorological (MET) conditions, focusing on 

the diurnal and seasonal changes inherent to a northeastern region of the United States. A portion 

of the dataset is planned to be made public release in the future in order to enable unrestricted 

dissemination of it to the wider open scientific community for algorithmic research and 

development (R&D).

In this report, we present some results from our first observations of the LWIR hyperspectral 

data subset of SPICE. (Results have been published using the LWIR polarimetric data subset of 

SPICE.1,2) Our focus in this report is to discuss the inherent challenges associated with using the 

SPICE LWIR hyperspectral dataset for the purpose of pattern recognition, as well as consider
2

data quality, qualitative validation of expected atmospheric spectral features, and qualitative 

comparison against another dataset of the same site using a different LWIR hyperspectral sensor.

The SPICE database contains over 25,000 LWIR hyperspectral data cubes, of which only a very 

small but well-selected sample of it has been used for the work presented in this report.

2. Data Collection and Sensor Characterization

2.1 Facility

The SPICE facility allows for a sensor altitude of 126 m with respect to the target placement site, 

which is located at a slant range of 570 m out from the tower base used to house the sensors. 

Being located in the northeastern region of the United States, various adverse weather conditions 

are experienced at the target site throughout the year. Expected weather conditions include light 

to heavy rain, light to heavy fog, as well as various types of cloud cover, various snow types, and 

different humidity levels. To accurately determine and characterize these adverse weather 

conditions and help aid in the measurement of the propagation path attenuation, the tower is 

equipped with a fully automated onsite MET data collection system composed of various basic 

and advanced MET sensors at both the target site and tower. All MET instrumentations are 

connected to a networked data logger located at the base tower, which allows for autonomous 

ground-truth data collection. The MET data are archived and can be retrieved for future analysis 

at a later time. Also a subset of the MET data is included in the header of calibrated imagery. 

LWIR and midwave infrared (MWIR) signal propagation is certainly affected by adverse 

weather conditions that occur during data collection; hence, maintaining an accurate account of 

these conditions is vital for characterization and analysis of the dataset.

2.2 Targets

The use of actual operational military vehicles as targets to support the continuous and automatic 

nature of a long-term data collection at the tower proved to be too expensive, thus, surrogate 

Russian 2S3 howitzers were used instead. The 2S3 surrogate targets provide reasonably 

validatable broadband infrared signatures, which were accomplished by producing a physical 

replication of the actual vehicle using a similar metallic construction and paint, and adding

supplemental heated surfaces. The supplemental heated surfaces mimic the characteristics of an 

operational howitzer (idling or gun firing). Three 2S3 surrogates were located at the target site 

and were oriented in aspect angles of 0°, 90°, and 135° (counterclockwise) with respect to the 

sensors (shown later in the upper-right portion of Fig. 8).

2.3 Sensor Characterization 

The hyperspectral sensor used in SPICE is a commercially available Fourier-transform 

spectrometer LWIR imager that requires cooling to 65 K to achieve a typical noise equivalent 
3

spectral radiance (NESR) of 20 nW/cm2

sr cm–1 for a nominal 300 K blackbody radiance. Its 

320x256 LWIR photovoltaic mercury cadmium telluride (PV MCT) focal plane array (FPA)

detectors are particularly sensitive to thermal radiance between 8 and 11.5 micron, by design 

specification, and the spectral resolution is user selectable from 0.25 to 150 cm–1. The sensor was 

manufactured by the Canadian company Telops (Quebec), using system components from other 

international vendors, in particular, Sofradir of France, which manufactured the FPA. The sensor 

is known as LWIR Hyper-Cam. Additional specifications are provided in Fig. 1.

Fig. 1 Telops LWIR Hyper-Cam’s basic specs

In the last 15 years, Telops’ LWIR Hyper-Cam (previously known as LWIR FIRST) has become 

popular in the scientific community because of its commercial availability, affordability, 

reasonable performance, and unrestricted status under the American and Canadian International 

Traffic in Arms Regulations (ITAR). Accordingly, the LWIR Hyper-Cam is based on a starring

rather than pushbroom hyperspectral system.

3

The software for the LWIR Hyper-Cam used in SPICE was uniquely configured to allow 

continuous and programmable data collection and a remote control capability via an Intranet or 

Internet network. These capabilities, however, do not eliminate the challenges associated with 

conducting an automatic and continuous data collection effort over all kinds of atmospheric and 

environmental conditions (cloudy, raining, snowing, cold, hot, etc.). As such, one must decide on 

an ad-hoc basis what settings to use to meet the desired critical parameters. Examples of these 

parameters are spectral resolution, integration time, focus, and temperature interval between both 

blackbodies that would embrace as narrowly as possible all possible material temperatures in the 

scene. These settings are critical in order to achieve acceptable data quality results, both daytime 

and nighttime. As of March 2014, the SPICE database contains over 25,000 uncalibrated LWIR 

hyperspectral data cubes, where 100% of this dataset is radiometrically calibrated at ARL using 

calibration code by Borel et al.

4

We performed a basic assessment of the LWIR Hyper-Cam used in SPICE for a period of 

30 days at AFIT before the data collection started at ARDEC and compared basic performance 

against another specs-similar imager. (In order to distinguish between the two imagers, we refer 
4

to the instrument used in SPICE as the “ARL-owned sensor” or “ARL LWIR Hyper-Cam.”) In 

particular, we performed characterization of the ARL LWIR Hyper-Cam at the AFIT Remote 

Sensing Laboratory, a well-equipped sensor calibration/characterization facility, and compared 

its results against results produced by another Telops LWIR Hyper-Cam owned by AFIT. (The 

AFIT-owned Hyper-Cam is similar to the ARL-owned Hyper-Cam.)

Both sensors were employed in the same indoor lab and limited outdoor experimentations. For 

the test, we used a few distinct material types of known emissivity under a surface temperature 

controlled setting (Fig. 2). 

Fig. 2 Illustration of the indoor setting used at AFIT to test and validate performance of both the ARL- and AFITowned LWIR Hyper-Cams

Figure 2 depicts both sensors imaging an indoor scene consisting of natural stones of known 

emissivity values and a large blackbody (among other material types), where the temperature of 

these material types were measured and adjusted accordingly to different desired values as part 

of the experimentation. Figure 2 also shows an illustration of output images produced at different 

stages of the data processing method applied to raw data (interferogram) from the scene. Figure 2 

and the flowchart in Fig. 3 show two blackbodies (BB1 and BB2) directly attached to the HyperCams (both ARL’s and AFIT’s), which were allowed to move (software-controlled movement) 

immediately in front of the sensor optics so blackbody raw data could be recorded at zero range 

and used later to perform radiometric calibration of the scene data. During the scene data 

collection, BB2 was set to a temperature value lower than the lowest material temperature in the 

scene and BB1 was set to a temperature value higher than the highest material temperature in the 
5

scene. Details on the method depicted in Fig. 3, where QTES denotes Quick Temperature and 

Emissivity Separation, have been published elswhere4 and are summarized in Section 2.4. (The 

method depicted in Fig. 3 is the same one applied to the raw data recorded by both ARL- and 

AFIT-owned LWIR Hyper-Cams.)

Fig. 3 Data processing flow of the radiometric calibration method applied to Telops Hyper-Cam raw data

After analysis of results from the experiments performed at AFIT, we concluded that the 

experiment results produced by the ARL-owned Hyper-Cam were comparable to the results 

produced by the AFIT-owned Hyper-Cam, and noted that the data quality produced by these 

sensors is as good as one can expect using the technology employed by Telops to manufacture 

these sensors, i.e., FPA of PV MCT detectors cooled to 65 K. We also confirmed that the data 

produced by the LWIR Hyper-Cam are noisier (Fig. 4) than some other datasets collected with 

far more expensive sensors, citing as an example the US Air Force sponsored SEBASS LWIR 

hyperspectral instrument, which requires cooling the FPA of silicon arsenide (SiAs) impurityband-conduction detectors to less than 10 K.

5

Channeling, from when the device passed through the radiometric calibration process, caused a

spectral ripple (noise) observed in the data recorded by the Telops Hyper-Cam. However, the 

resulting spectral ripple does not seem to significantly change the predominant underlying 

characteristics of observed spectra, as shown in Fig. 4 (top image: the red box highlights the 

spectral characteristics undisturbed by noise), specifically, the manmade and natural objects as 

opposed to the gases.
6

Fig. 4 Effect of channeling in spectral data recorded by the Telops Hyper-Cam; the spectral ripple (noise), 

although embedded in the data, does not significantly change the predominant underlying spectral 

characteristics observed in the illustrated spectrum (red box highlighting the spectral feature in the top 

image)

Also, as expected, internal energy reflection due to plane parallel surfaces inside a sensor yields 

both constructive and destructive interferences; and, accordingly, when constructive interference 

in particular occurs (i.e., when the phase difference between two waves is a multiple of 2π), the 

phenomenon will usually cause channeling (Fig. 4). As mentioned earlier, the effect of 

channeling is the spectral ripple, or noise, in the spectral domain.

2.4 Temperature Emissivity Retrieval Problem and Algorithm

The central problem of temperature-emissivity separation7 is that given N spectral measurements 

of radiance, one needs to find N+1 unknowns (N emissivities and one temperature). In the past, 

several methods were proposed:

1. Assumed channel 6 emittance model8

2. Emissivity Spectrum Normalization (ESN)7

3. Thermal log and alpha residual9

4. Mean-maximum difference (MMD)10

These algorithms were developed to retrieve surface emissivity and temperature from multispectral data.

To analyze hyperspectral thermal data, it is necessary to correct the data for atmospheric effects 

in order to retrieve surface emissivity and temperature. This process is commonly called 
7

temperature-emissivity separation (TES), and only a few algorithms exist for hyperspectral data. 

The following algorithms are known to us:

1. In-scene atmospheric correction (ISAC) algorithm11,12

2. Autonomous atmospheric compensation13

Using hyperspectral data has the advantage of having more bands than necessary and thus it 

might be possible to find solutions for the unknowns, although uniqueness is not guaranteed.

2.4.1 Previous Temperature-emissivity Separation Algorithm 

It was found that by varying the atmospheric parameters, such as water vapor, atmospheric 

temperature and surface temperature (the latter in small steps), the emissivity could be 

estimated.14 When the surface temperature is correctly retrieved, the emissivity is spectrally 

smooth, that is, it shows no presence of atmospheric features. The observation that surface 

emissivities15 tend to be much smoother than atmospheric transmission features was very 

important in the development of a hyperspectral TES algorithm.

After several years, the basic TES algorithm16 that was improved as part of this work, namely, 

the Automatic Retrieval of Temperature and Emissivity using Spectral Smoothness 

(ARTEMISS), was developed as shown in Fig. 5. It is based on three steps:

1. The ARTEMISS In-Scene Atmospheric Compensation (ARTISAC) method estimates the 

atmospheric transmission.

2. The ARTISAC estimated transmission is compared to a database (transmission, up-welling, 

downwelling radiance [TUD]-look-up table [LUT]) of atmospheres computed using 

Moderate Resolution Atmospheric Transmission (MODTRAN),

6 and the closest matches 

are evaluated and ranked as candidate atmospheres. The best candidate is chosen by a pixel 

voting scheme.

3. The modeled at-sensor radiance is computed using a spectrally smooth emissivity. By 

minimizing the difference between measured and modeled radiance, ARTEMISS produces 

three outputs: a temperature image T(x,y), an emissivity cube ε(λ,x,y), and a root mean 

square error (RMSE) radiance fitting error σ(x,y). 
8

Fig. 5 Basic ARTEMISS algorithm flow diagram

The measured radiance Lmeasured(λ) can be written as

( ) ( ) ( ), L LL measured λ λλ = + ground path (1)

where the ground-leaving radiance Lground(λ) is given by

[ ]

( ) [ ( ) ( , )] ( )

 + 1 ( ) ( ),

ground ground atmo

down

L B T

L

λ ελ λ τ λ

ελ λ

=

− (2)

and the path radiance Lpath(λ) is given by

( ) [1 ( )] ( , ), Lpath λ τλλ = − atmo B Tatmo (3)

where ε(λ) is the spectral emissivity, τatmo(λ) is the transmission from the ground to the sensor, 

B(λ,T) is the Planck blackbody function, and Tx is a temperature. Equation 1 can be solved for the 

emissivity as

( )

m d

est d

p LLL

BT L

ε

λ τ

− − = , − , (4)

where the estimated ground temperature Test is given by

1 0

0

0

(1 ) . m p d

est

LL L

T B

ε

λ

ε τ

− − −− = ,

      (5)

ARTISAC Find best 

Atmosphere

ARTEMISS

L(λ, x, y)

T(x, y) ε(λ,x, 

TUDLUT

σ(x, y)

MODTRAN5

τ 
9

where λ0 is arbitrary selected to be 10 μm, atmospheric absorption is small, and transmittance is 

large. The “best” temperature, Topt, is found by minimizing the RMSE of the difference between 

the measured radiance and a fitted radiance Lfit: 

min[ { }], T LL opt = − σ m fit (6)

where Lfit is given by

( , ) ( , ) (1 ) LT B T L L fit opt opt opt opt opt d p ε ελ τ ε = +− + (7)

The opt ε is the three-point smoothed emissivity from Eq. 4.

2.4.2 ARTEMISS Summary of Improvements 

Recent developments have improved code performance in both accuracy and speed. The 

ARTISAC method has been refined to produce transmission estimates using only certain pixels. 

The matching of the estimated transmission to a TUD-LUT database is still available and has 

been parallelized using message passing interface (MPI) software, which reduces the TUD-LUT 

generation time significantly when a cluster of computers is available. The atmospheric 

parameters that are varied for the TUD-LUT are 1) an atmospheric temperature offset, 2) a water 

vapor scaling factor, and 3) an ozone scaling factor. The need to generate a large TUD-LUT has 

been made unnecessary by the development of a two-step process. The first step is a coarse 

retrieval of the effective atmospheric temperature, columnar water vapor amount, and ozone. 

This can be done using a small-size TUD-LUT (a 3 x 3 x 3 or 5 x 5 x 5 is usually adequate) and a 

library matching method, or by using the inverse Planck blackbody function to estimate the 

atmospheric temperature coupled with various atmospheric absorption features to estimate water 

vapor and ozone.

Figure 6 shows how these improvements fit into the basic ARTEMISS algorithm.
10

Fig. 6 Improved ARTEMISS flow diagram

Using these estimates and knowledge of the sensor and surface geometry allows a more precise 

inversion of the estimated atmospheric transmission using the AMOEBA (also called NelderMead or Simplex algorithm) optimization method.

17 The AMOEBA routine creates inputs for the 

MODTRAN files varying up to 20 parameters. Inclusion of more than just three parameters now 

allows the variation of the atmospheric temperature profile and the addition of sub-visible clouds 

and cirrus clouds between and above the sensor. The two main advantages are that significantly

fewer MODTRAN runs are required and it is possible to simultaneously optimize sensor 

parameters such as a channel position offset and instrument line spread function, the latter of 

which is often difficult to characterize. Figure 7 shows how the new version of ARTEMISS 

works. Note that the AMOEBA step takes approximately as much central processing unit (CPU) 

time as the generation of a TUD-LUT, but the temperature retrieval based on the continuum

interpolated band ratio (CIBR) (Fig. 7) is very fast and the generation of the emissivity cube is 

based on Eq. 4. 

σ ( x, y)

ARTISAC

ARTEMISS

Spectral smile 

and FWHM

TUD-LUT 

generation

T(x, y)

TUD-LUT

L(λ, x, y)

Improved 

selection of 

atmospheric 

candidates

Convolve

with 

sensor 

response

ε (λ, x, y)
11

Fig. 7 New ARTEMISS algorithm based on iterative retrieval of the atmosphere based 

on the AMOEBA algorithm followed by the fast three-band ratio technique (CIBR) 

to retrieve temperature

The improved ARTEMISS algorithm, as shown in Fig. 7, was used in the estimation of and 

separation between temperature and emissivity using radiometrically calibrated LWIR 

hyperspectral imagery in the SPICE dataset. 

2.5 Qualitative Data Assessment

Under SPICE, the Telops LWIR Hyper-Cam has been employed using the setting for continuous 

and autonomous data collection for the very first time in this product’s history, according to 

Telops. This setting adds significantly more challenges to a data collection effort, since a human 

operator is out of the loop and cannot make appropriate sensor parameter adjustments to account 

for changing conditions in the scene (atmospheric changes, diurnal cycle). So, concerns

regarding data quality under this setting are justifiable.

We used SPICE data (July–Sept 2012 collection) to code a standard radiometric calibration 

method for the SPICE dataset. The raw data preprocessing and calibration methods used in the 

code have been described previously4 (preprocessing) or are outlined in Section 2.4 (calibration). 

The code has been used at ARL in a mass production setting for the radiometric calibration of 

SPICE hyperspectral LWIR data. (The data received from ARDEC are raw [Michalson 

L(λ, x, y)

ARTISAC

AMOEBA 

Algorithm

MODTRAN5

T(x, y) ε(λ, x, y) 

ARTEMISSCIBR

Eq(4)
12

interferogram] data of the scene [5-min interval measurements] and two [hot and cold]

blackbody objects [hourly measurements from a zero range]). The spectral resolution was set at 

once to 4 wavenumbers, which produced 164 bands between 8 and 11.5 micron; additional specs 

are given later. 

Figure 8 depicts a typical diagnostic output from the application of the calibration/TES code.

Fig. 8 Typical AFIT calibration/TES code’s diagnostic output using SPICE LWIR hyperspectral data

Calibration used measurements from two blackbodies (referred to as BB1 and BB2 in Fig. 8). 

Figure 8 shows the retrieved temperature image (lower right) of the SPICE test scene (picture 

upper right); the data were collected July 2012 at 2300 h. The retrieved temperature image shows 

well-resolved temperature values from spatial observations from the cooler ground toward the 

top of canopy vegetation (bushes, trees).

Summarized in Section 2.4 is basic information about the corresponding data cube (e.g., cube 

size, minimum and maximum wavelengths, and spectral resolution), some data quality numbers 

(e.g., number of bad pixels), and some MET data. The temperature image of the test scene at 

ARDEC (SPICE data collected around 2300 h on July 2012) is shown in the lower-right side of 

Fig. 8; the upper-right side image in Fig. 8 is the red, blue, green (RBG) color picture of the 

same test site, which includes labeled markers highlighting the manmade objects in the scene. 

Note that the temperature of a floral bush in the scene could be clearly observed as a smooth,

gradually increasing set of values from a location closer to the ground soil (where it was colder) 
13

to the top of the bush (where it was warmer). Examples of such a temperature resolution within a 

relatively small spatial area containing the same object would not be possible from a dataset that 

did not contain an adequate amount of spectral information from the objects in the scene. This 

reinforces, once again, that the SPICE LWIR hyperspectral dataset is rich in spectral content and 

temporal variation. 

In other examples, not included in this report, we saw favorable results from a temporal analysis 

performed using another software tool developed at AFIT and tailored to the SPICE data format 

and available MET data. Some of the plots we saw depicted retrieved temperature values in 

Celsius, as a function of time, of multiple material types in the test scene estimated using the 

software tool on the calibrated SPICE LWIR hyperspectral data. Results from canopy tree leaves 

could be highlighted, since this material type is often used in the scientific community as an 

approximate representation of a perfect radiation emitter—a blackbody. We point out that the 

retrieved temperature values of canopy tree leaves, retrieved from radiometrically calibrated 

SPICE LWIR Hyper-Cam data cubes, showed an almost perfect alignment fit through a 24-h 

time period with the actual air temperature values that were measured using an independent 

thermometer at the scene. Accordingly, this is a vote of confidence on the spectral content 

adequacy of the SPICE LWIR hyperspectral data, because, while temperature is independent of 

wavelength, the TES method does depend on the spectral content adequacy of the calibrated data 

and corresponding signal-to-noise ratio (SNR). 

To appreciate how much spectral variability one should expect due to various sources of noise 

imposed on the spectral measurements from a scene, see Figs. 9 through 11. The color code in 

Figs. 9 through 11 features colored boxes (showing where spectra in the imagery were sampled 

from to compute average object spectra, e.g., Fig. 9), colors associated with spectral plots 

(Figs. 9 through 11), and colors associated with object-name fonts (Figs. 9 through 11). The 

spectral interval for the sensor’s operational specs is between 8.0 and 11.5 micron. Figure 9 

shows an example of a daytime partly cloudy condition, where the downwelling radiance is a 

mixture of Planck’s blackbody radiance from the clouds and clear sky. Figure 10 (top) shows an 

example of night and cloudy conditions, where the downwelling radiance matches the 

environment and both are at the same temperature. Figure 10 (bottom) depicts an example of 

daytime cloudy conditions, where clouds behave like a Planck’s blackbody when the 

environment is hotter than cloud temperature. Figure 11 (top) shows an example of a thermal 

cross-over, where the downwelling radiance matches the environment and all surfaces have the 

same temperature. Figure 11 (bottom) shows an example of a daytime clear and hot condition, 

where trees show a Planck’s blackbody radiance, the surrogate howitzers are hotter than the 

environment and show absorption features, and the sky plate reflects cold sky. 
14

Fig. 9 Example of daytime partly cloudy condition; downwelling radiance is a mixture of Planck’s blackbody 

radiance from the clouds and clear sky. The color code corresponds to color-box-highlighted objects in the 

scene, plot colors, and colored object-name fonts. The spectral interval of the sensor’s operation under 

specs is between 8.0 and 11.5 micron. 
15

Fig. 10 (Top) Example of night and cloudy conditions; downwelling radiance matches the environment, which is 

at the same temperature. (Bottom) Example of daytime cloudy conditions; clouds behave like a blackbody 

when the environment is hotter than cloud temperature. (The color code corresponds to the same one in 

Fig. 12.) The spectral interval under specs is between 8.0 and 11.5 micron. 
16

Fig. 11 (Top) Example of a thermal cross-over; downwelling radiance matches the environment and all surfaces 

have the same temperature. (Bottom) Example of daytime clear and hot conditions; trees show a Planck’s 

blackbody radiance, surrogate tanks are hotter than environment and show absorption features, and sky

plate reflects cold sky. (The color code corresponds to the same one in Fig. 9.) The spectral interval under 

specs is between 8.0 and 11.5 micron. 

Our general assessment of calibrated SPICE data is that, while the dataset is noisier than some 

other datasets collected with far more expensive sensors, the dataset is rich in spectral content 

and temporal variation, forming a unique set that will be of significant value to researchers. 
17

We also performed occasional sanity checks on samples of calibrated SPICE LWIR 

hyperspectral data cubes. As a first cut, understanding that various factors can contribute to the 

apparent radiance observed in material spectral profiles (e.g., surface-reflected downwelling sky 

radiance, atmosphere path radiance, downwelling cloud radiance, downwelling radiance from the 

environment, degraded sensor SNR due to unstable temperature levels [between the sensor and 

local air temperature, caused, for instance, by relatively high winds in the tower facility]), we 

focused on two basic aspects, checking for the following: 

1. Spectral distinction among spectra of different material types, independently of the 

atmospheric absorption features observed in individual spectrum, and 

2. Consistency (appearance-wise) of spectral profiles from objects spatially apart in the scene, 

but having the same material (e.g., same paint [surrogate howitzers]). 

A simple and fast way to address these aspects, while reducing the ripple in spectra, is to average 

spectra representing a single object in the scene and visually compare the resulting sample 

average mean spectra to different objects in the scene. Figure 12 depicts results using this 

approach.

Fig. 12 (Left) Single band image (of the same scene shown in Fig. 8) depicting data blocks used to estimate the 

average mean spectra of spatially apart objects in the scene; some of these objects have the same material 

type. Surrogate targets: red region (0° aspect angle), green area (same paint/tank posed at 90°), and dark 

blue area (same paint/tank posed at 135°). Vegetation trees: yellow and dark magenta (upper right) area. 

Sparse grass and soil: light blue area. Dirt/pebble road: magenta area (far left middle). (Right) Sample 

average spectral profiles representing selected spatial areas in the scene shown in the left-hand side image; 

spectra within each color area were the only ones used to produce the average spectral profile per color 

area. The plot color code corresponds to the same one in the left image.

In Fig. 12 (right), the spectral sample average plots of three surrogate howitzers (dark blue, 

green, and red) show a reasonable level of similarity (appearance-wise) across the wavelengths, 

despite of the fact that the three objects are spatially apart from each other and posed at distinct 

aspect angles (0°, 90°, and 135°; these targets are also denoted herein as T0, T90, and T135, 

respectively). Natural canopy material also shows similar favorable results, as seen in the 

spectral profiles from grass (light blue) and vegetation trees (yellow and dark magenta). These 
18

results address the concern in the first aspect mentioned previously and give another vote of 

confidence on the suitability of SPICE data for algorithmic R&D. (These favorable results were 

also observed using a number of other arbitrarily chosen SPICE data cubes.) It is worth noting 

that the general spectral characteristics of the tanks shown in Fig. 12 will likely not be duplicated 

in a scenario where the same target is spectrally measured from a zero or near zero range (e.g., 

1.5 ft), for reasons discussed shortly.

Another vote of confidence in the SPICE data are the spectral similarities found in sample data 

of the same scene while recorded by the two independent Hyper-Cams (AFIT vs. ARL). 

Fortunately, the AFIT-owned Hyper-Cam was also used in a very limited basis to collect data of 

the same target site at ARDEC—a year earlier (July 2011) than the starting date of the SPICE 

data collection (July 2012). In particular, the AFIT sensor recorded a small number of data cubes 

during daytime only in July 2011. Figure 13 illustrates spectral profiles among different material 

types in imagery independently acquired a year apart by both Hyper-Cams. It is readily 

noticeable from Fig. 13 that the spectral profiles (a spectrum per material type) are comparable 

between both data cubes. 

Fig. 13 Illustration of spectral profiles of different material types at the ARDEC SPICE site, where (left) data 

from the AFIT-owned Hyper-Cam were collected in July 2011 and (right) data from the ARL-owned 

Hyper-Cam were collected a year later in July 2012

The AFIT sensor acquired data on a particular day in July 2011 using a spectral resolution of 

2 wavenumbers, or 324 center bands between 7.41 and 12.32 μm, and had a human operator 

monitoring the collection of each individual data cube acquisition at a time, changing sensor 

parameters when appropriate to maximize data quality (e.g., integration time, optical focus). To 
19

improve the SNR, 31 data cubes were averaged in the sensor’s electronic card to produce a final 

data cube version representing a single acquisition; this number is the maximum allowed by the 

Hyper-Cam, significantly increasing the time required for a single data cube acquisition.

The ARL sensor acquired data on most days in July 2012 (a year later from the AFIT data 

acquisition), using a spectral resolution of 4 wavenumbers, or 165 center bands between 

7.41 and 12.45 μm. The data acquisition was fully autonomous, having all sensor parameters set 

to fixed values independently of the time of the day, weather condition, heat index, etc. The ARL 

sensor internally averaged 16 data cubes to yield an output data cube, compared to AFIT’s 31 

averaging setting, to improve the SNR.

Fig. 13 depicts a single band image produced by each sensor (top images) and a collection of 

spectra (single spectrum per material type, no averages from multiple sample spectra) from the 

ARDEC test site. They represent two general classes of material types: manmade objects, which 

included three surrogate howitzers posed at three aspect angles (counterclockwise) 135° (T135), 

90° (T90), and 0° (T0), and natural objects (vegetation bush, grass, and dirt road). Notice in 

Fig. 13 (bottom plots) that between 8.0 and 11.0 μm, the manufacturer’s specified range of 

operation for both Hyper-Cams, the AFIT sensor produced twice the number of bands as 

compared to the ARL sensor (206 versus 103). A key point from these spectral plots is the strong 

visual similarity between spectral samples of the same material type independently produced by 

each sensor a year apart. For instance, the apparent radiance at the sensor from the sky plate 

(aluminum plate) in the scene is dominated by the surface-reflected downwelling sky radiance 

toward the sensor. Notice also that the most dramatic spectral features in the sky plate spectral 

profile can be equally observed in corresponding sky plate spectrum recorded by each sensor. 

These features are not observed in spectra of other material types, although the high oscillation 

between 8.0 and 9.3 μm due to water vapor and other gases is also observable in corresponding 

spectra of three other manmade objects in the scene (T0, T90, and T135), as these strong spectral 

featured are added during transmission of radiance from these objects through the atmosphere 

toward the sensor (path radiance), in addition to surface-reflected downwelling radiance from 

other nearby objects in the scene.

The spectral characteristics of the surface-reflected downwelling sky radiance shown in Fig. 13 

for both sensors are consistent with the downwelling sky radiance models discussed in the 

literature. Figure 14 shows that spectral features from ozone gas in the atmosphere between 9 

and 10 μm are also observed.

3
20

Fig. 14 MODTRAN models: MWIR/LWIR diffuse downwelling radiance as a function of atmospheric 

conditions 

Notice in Fig. 14 that there is a measureable amplitude difference (not a spectral shape 

difference) between downwelling sky radiance propagating in a tropical-like atmosphere, where 

air humidity is relatively high, as compared to a desert or some other condition. This is notable 

since the portion of the LWIR region of the spectrum that is relevant to the Telops’ Hyper-Cam 

is between 8 and 11 microns, Fig. 12 shows the applicable interval from plots in Fig. 14 coarsely 

aligned with ARL sensor’s measurement of downwelling sky radiance shown earlier in Fig. 13; 

the correspondence among the various spectral characteristics between the MODTRAN models 

and ARL measurement is self-evident, where the atmospheric condition in July 2012 at the 

ARDEC test site is closer to the tropical condition portrayed in the model’s spectral plot. 

(MODTRAN is a computer program designed to model atmospheric propagation of 

electromagnetic radiation for the 0.2 to 100 micron spectral range.

6

) 

The message to be emphasized from Fig. 14 is the presence of a clearly observable strong 

correspondence among the downwelling sky radiance plots from the MODTRAN models, the 

sample AFIT Hyper-Cam measurement, and the sample ARL Hyper-Cam measurement, which 

also gives a strong indication of the sky atmospheric condition during collection of the specific 

SPICE LWIR hyperspectral downwelling sky sample spectrum presented in Fig. 13, i.e., clear 

horizon with partial stratus clouds.

Over the years, analysis between MODTRAN models and Telops’ Hyper-Cam measurements of 

downwelling sky radiance has been a topic of discussion in technical conferences and workshop;

a Google search with the appropriate keywords would find some of these materials. It is beyond 

the scope of this report to discuss this topic further; however, to show an indication of this type 

of work, we provide Fig. 15 as an example of a similar analysis performed using the AFIT 

Hyper-Cam. Additional challenges are shown in Fig. 16.
21

Fig. 15 MODTRAN vs. real measurements of the downwelling sky radiance analysis using the AFIT-owned 

Hyper-Cam, where such a validation was needed for the development of the QTES code
22

Fig. 16 (Top) MWIR/LWIR diffuse downwelling radiance as a function of cloud conditions. (Bottom) 

LWIR downwelling radiance measurements for different cirrus cloud thicknesses compared to 

clear sky and liquid clouds.

As would be expected, changes in the atmosphere (e.g., presence and thickness of specific types 

of clouds) and data collection parameters (e.g., sensor viewing perspective) can cause some 

significant variation in remotely measured spectra; see the spectral profiles in Fig. 13 for 

examples of some of these changes.
23

As mentioned, Fig. 16 shows the MWIR/LWIR diffuse downwelling radiance as a function of 

cloud conditions: clear, cumulus (below 6,000 ft), stratus (below 6,000 ft), and cirrus (above 

18,000 ft). The thickness of these clouds will determine whether their downwelling radiance will 

behave like the one shown in Fig. 16; cloud thickness or liquid level, for instance, explains the 

difference between the downwelling radiance shown for stratus in other measurements from Fig. 

16 (top side).3 Figure 16 (bottom side) further depicts the effect of cloud thickness, using as 

reference cirrus cloud, where radiance is shown as a function of wavenumber (cm–1

), not 

wavelength. 

The plots in Fig. 16 emphasize additional expected complexity for developing physics-based 

LWIR radiance models and making correct data quality judgments from real hyperspectral 

LWIR measurements (e.g., SPICE dataset). These plots also emphasize the inherent difficulties 

one would have in comparing spectral samples of the same material from real hyperspectral data 

cubes without having full knowledge of the data collection ground truth (e.g., sensor’s viewing 

perspectives, range, cloud, and wind conditions); some of this information is often not available. 

Beyond the downwelling sky radiance, it would also be helpful to take a closer look at the 

spectral profiles of another manmade object in the scene, using data from both sensors. Figure 17 

shows the alignment of a corresponding sub-sampled version of another example manmade 

material spectrum recorded by the AFIT sensor. In this case, we arbitrarily keep every other 

radiance value among the 206 bands in order to match the number of bands produced by the 

same manmade object in the SPICE data (103 bands). Of course, by following this procedure, 

one would not expect to align the radiance values from the AFIT sensor with the exact 103 

center wavelengths produced by the ARL sensor, and vice versa; however, since the only interest 

here is to observe course trends in the spectra, sub-sampling was employed for visual 

comparison of the spectra using the center wavelengths of the ARL sensor for the band 

horizontal axis. Figure 17 shows the resulting spectral profiles of target T0 as the example 

manmade object.
24

Fig. 17 Spectra from the same object (target T0) and approximate pixel location on the target, both collected by two 

independent Hyper-Cams (AFIT vs. ARL) a year apart (July 2011 and July 2012). The strong oscillation 

patterns between 8.0 and 9.4 μm are due to water vapor concentration in the atmosphere between the target 

surface and the sensor. Oscillation amplitude is also impacted by the humidity level in the atmosphere. The 

target (surrogate howitzer) was hotter during AFIT data acquisition and spectral absorptions appear stronger 

because of its finer spectral resolution than ARL’s data. Distinct spectral patterns in both spectra are clearly 

visible between 9.4 and 11.0 μm using both sensors a year apart are encouraging. (The spectrum from the 

AFIT sensor was sub-sampled from 206 to 103 bands—the number of bands recorded by the ARL sensor, as 

described in text, so both spectra could be plotted in the same graph.)

Given that both sensors collected data of the same scene but a year apart, Fig. 17 shows a strong 

match of the target T0’s spectral patterns between 9.4 and 11.0 μm in both data cubes (AFIT and 

SPICE). It also shows a direct correlation to the spectral sample averages shown earlier in 

Fig. 12, representing the three surrogates (T0, T90, and T135). These visual matches give 

another vote of confidence in the SPICE data, since the exact atmospheric condition is virtually 

impossible to duplicate between days of the same month a year apart. There are also other 

uncertainties to be considered, e.g., unknown exact spatial location on the target where the 

spectra were sampled. The strong oscillation patterns between 8.0 and 9.4 μm are likely due to 

water vapor concentration in the atmosphere between the target surface and the sensor. The 

amplitude of the oscillation is also impacted by the humidity level in the atmosphere. Let us now 

address “zero” range data, using the ARL Hyper-Cam (Fig. 18).
25

Fig. 18 Spectral mean averages of data collected from a 0.46 m (1.5 ft) range (top image and a 570 m (1,870 ft) 

range (bottom image), using the SPICE LWIR Hyper-Cam to collect data of the same material (green paint 

on the surrogate howitzer target). Due to many factors, spectral differences are expected to exist, given the 

large difference in range, to include reflection of emitted downwelling sky radiance off the target at far 

ranges (which is blocked by the sensor’s physical presence at close range), and a significantly higher 

concentration of atmosphere between target and sensors.

As discussed earlier, due to the path radiance, spectral patterns emitted from a particular material 

surface located at a far range from the sensor, e.g., 0.57 km, will likely differ from spectral 
26

patterns of the same material type being recorded, instead, by the same sensor at a close range 

from the target, say less than a meter. To show this distinction, a similar copy of the surrogate 

howitzer (used to obtain results shown in Fig. 12) was used as the target at a close range, such 

that, the target’s dominant green paint became the specific material of interest. The data were 

collected in July 2013 around 8:00 am from a 1.5-ft range, using a spectral resolution of 

4 wavenumbers (103 bands between 8.0 and 11.0 micron). 

The spectral profile shown in Fig. 18 (top: target range 0.46 m [1.7 ft]) is the spectral mean 

average using a 10x10 window over the imaged target (target’s actual left-hand side). Figure 18 

(bottom: target range 570 m [18,70 ft]) shows the same spectral mean averages corresponding to 

T0, T90, and T135 shown earlier in Fig. 12, where data were collected a year earlier in July 2012 

on a different day and time from the close range collect. Notice that the solar load incidence 

angle on the normal surface of the target painted plate can be appreciated from the center image 

in Fig. 18 (top), as well as the presence of a tilted shadow (lower left-hand side of center image 

in Fig.18 [top]) due to the spatial geometry between the sensor and the sun, which was rising on 

the horizon from the back viewing perspective (right-hand side) of the sensor. 

Spectral profile differences are clearly noticeable between Fig. 18 (top) and Fig. 18 (bottom). 

The spectral differences are attributed to multiple factors, including blocked sky radiance and/or 

blocked environment radiance by the sensor from reaching the target at close range; pixel spatial 

resolution differences (e.g., from collects at long ranges a pixel may consist of a mixture of 

different material types and radiance from multiple surface normal angles relative to collects at 

close ranges, in which a pixel-vector consists of a radiance vector from a single material surface 

angle); and the presence of a more concentrated atmospheric effect between the sensor and 

target—most dominant at longer ranges. 

Using an individual spectrum per material type to quantify or qualify spectral differences is not a 

favorable approach in the scientific community. Because, in addition to the various noise sources 

already discussed (system noise, atmospheric and environment variation) that will change the 

spectral profile of a material type, the natural statistical variation of the multivariate distribution 

function controlling the observed samples of material emitted radiance also does play a role in 

changing spectral profiles. Figure 19 shows examples of individual spectrum from target T0 

recorded by both LWIR Hyper-Cams (ARL and AFIT) at the range of 570 m (see Fig. 16) and a 

spectrum from approximately the center spatial location of the data cube measured from the 

target paint at the range of 0.46 m (see Fig. 18 [top]). 
27

Fig. 19 Examples of individual spectrum of the same target recorded in three different years: July 2011 

(AFIT sensor), July 2012 (ARL sensor), and July 2013 (ARL sensor) from two ranges (0.46 and 

570 m). Among other factors, the cumulative concentration of water vapor in the atmosphere, 

which increases as a function of range, plays a major role in changing the spectral profile of the 

target.

A key message from Figs. 18 and 19 is that the reliability of spectral patterns emitted by the 

same material is not only directly dependent on the atmospheric/cloud conditions, environment 

(nearby objects in the scene), and system noise, but also equally on range. 

Finally, Fig. 20 illustrates the average band images of the same target site during two complete 

diurnal cycles on 22 July 2012 and 23 July 2012, where a selected number of those images are 

depicted.
28

Fig. 20 Selected number of band-averaged images of the same target site during two complete diurnal cycles on 

22–23 July 2012

The thermal variability shown in Fig. 20 for each object in the scene suggests that assuming a 

fixed probability density function (PDF) to model LWIR hyperspectral data is inadequate. 

Therefore, the results produced by any theoretical method based on this assumption intended to 

perform anomaly detection, target detection, band reduction, material un-mixing, etc., should be 

ignored, as they are being applied in an ad-hoc fashion. Incidentally, this assumption covers the 

bulk of proposed methods in the literature. 
29

3. Conclusions

Investments into LWIR experiments, such as the SPICE dataset that ARL and ARDEC have

produced, are very important for the remote sensing community—both military and civilian. The 

effort will, for the first time, make data available to a wider community and across agencies for 

full diurnal cycles taken under non-ideal conditions. The dataset is especially valuable because 

the necessary ground truth, such as weather conditions and calibration targets imaged at the same 

time as the device under test, is made available as well. Since the SPICE data are taken for 

surrogate targets in a natural scene, the danger of giving away vulnerabilities is held at a 

minimum. Data from SPICE could also be used widely in the classroom to educate a new 

generation of researchers.

From our assessment of the LWIR hyperspectral sensor used in SPICE, we concluded the 

following: 

• Indoor data are comparable between the ARL- and AFIT-owned LWIR Hyper-Cams. This 

is based on the results from the sensor characterization effort, under a controlled indoor 

environment at the AFIT Remote Sensing Laboratory. The tests were held during an entire 

month (see Fig. 2). 

• Outdoor (SPICE site) data are comparable between the ARL- and AFIT-owned HyperCams. The AFIT-owned Hyper-Cam recorded a very limited dataset of the same site used 

for SPICE. The small LWIR hyperspectral dataset was recorded during daytime only in 

July 2011; SPICE started in July 2012. Figures 13 and 16 depict typical spectral profiles 

among different material types in imagery independently acquired a year apart by both 

Hyper-Cams. Visual inspection of these plots reassures that the spectral profiles (a 

spectrum per material type) are comparable between both data cubes. Similar results were 

obtained among other data cubes recorded by both sensors. The AFIT-sensor recorded data 

have twice the spectral resolution of SPICE data between 8 and 11 micron (206 and 103 

bands, respectively). It is worth noting that, under SPICE, the Hyper-Cam used a setting of 

continuous and autonomous data collection for the very first time in this product’s history, 

according to its manufacturer Telops. This setting adds significantly more challenges to a 

data collection effort, since a human operator was out of the loop and unable to make

appropriate sensor parameter adjustments to allow for changing conditions in the scene 

(atmospheric, diurnal cycle). The data recorded by the AFIT Hyper-Cam (see Fig. 13 [left]) 

had a human operator in the loop.

• MODTRAN models match the downwelling sky radiance in the SPICE dataset. Providing 

further reassurance in the SPICE dataset, spectral samples of the aluminum plate deployed 

at the SPICE site clearly show the surface-reflected downwelling sky radiance 
30

corresponding to downwelling in the sky models featured in MODTRAN, a computer 

program designed to model atmospheric propagation of electromagnetic radiation for the 

0.2 to 100 micron spectral range. Some of these spectral feature correspondences include 

1) the strong spectral oscillation between 8.0 and 9.2 micron due—by a general consensus 

in the scientific community—to water vapor in the atmosphere and some system noise; and 

2) other known infrared absorption lines (e.g., at 11.7 micron also due to water vapor3

) and 

between 9 and 10 micron (due to ozone molecules4

). Multiple organizations have 

performed validation studies between MODTRAN5 atmospheric models and actual 

measurements using the Telops LWIR Hyper-Cam, to include AFIT (see Fig. 15). 

Specific to the ARL-owned Hyper-Cam, the sky plate spectral profile shown in Fig. 13 

(lower right) corresponds well to the MODTRAN5 downwelling radiance models for the 

tropical environment as a function of atmospheric condition, as shown in Fig. 14. 

The results reinforce the challenges associated with remote sensing data modeling using 

outdoor hyperspectral LWIR measurements of a specific material, or using spectral 

samples from such a dataset to populate a library for pattern recognition tasks, as evidenced 

in Fig. 15 (MODTRAN models of downwelling sky radiance as a function of cloud 

conditions), and Figs. 9 through 11 (selected examples under conditions of daytime and 

nighttime, cross-over, hot and clear, partially and full-cover cloudy, etc.). Additional 

spectral changes in a target’s material signature due to range differences are shown in 

Fig. 17 (close [0.45 m] and far [570 m] ranges using the ARL Hyper-Cam). The spectral 

changes shown in those figures are far from being exhaustive; they only scrape the surface 

of atmospheric variation, as adverse weather, for instance, was not included. The key 

message from those figures is that atmospheric variation can significantly change the 

expected pure spectral radiance of a material, being remotely observed by a LWIR 

hyperspectral sensor, since the apparent material radiance at the sensor is the sum of the 

upwelling material spectral radiance (given the material temperature at the time of data 

acquisition), the atmospheric path radiance between the material and sensor, the surfacereflected downwelling sky radiance, and the surface-reflected downwelling radiance of 

nearby objects in the surrounding environment, not to mention changes associated with 

surface orientation of target material. 

The key message in here is that intrinsic system noise in Telops Hyper-Cam recorded data seems 

to be a minor to moderate concern compared to the effects imposed by atmospheric and 

environment variation, the latter being responsible for the real challenges behind modeling tasks 

or lack of algorithm robustness in target recognition applications.
31

4. Future Plans

The thermal variability depicted in Fig. 20 for each object in the scene suggests that modeling 

LWIR hyperspectral data with a fixed PDF (hence, fixed parameters)—an assumption widely 

adopted in the scientific community—is not only inadequate, but incorrect for the development 

of algorithms intended for autonomous anomaly detection, target detection, band reduction, 

material un-mixing, among others. In future, we will explore other statistical models, those that 

are inherently flexible allowing distribution parameters (hence, PDFs) to evolve as a function of 

time, both diurnally and across days within a narrow time interval. 
32

5. References

1. Romano J, Rosario D, McCarthy J. Day/night polarimetric anomaly detection using SPICE 

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Chetwynd J, Hoke M, Lockwood R, Gardner J, Cooley T, Borel C, Lewis P, Shettle E. 

MODTRAN5: 2006 update. Proc. SPIE. 2006;6233.

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normalization. Proc. of the Second TIMS Workshop. JPL Publ. 1990;90–55:31–35.

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emissivity information from thermal infrared data for geologic studies. Remote Sens. 

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2002.
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18. TELOPS homepage http://www.telops.com (accessed 28 September 2011).
34

List of Symbols, Abbreviations, and Acronyms

AFIT US Air Force Institute of Technology 

ARDEC US Army Armament Research, Development, and Engineering Center 

ARL US Army Research Laboratory 

ARTEMISS Automatic Retrieval of Temperature and Emissivity using Spectral Smoothness 

ARTISAC ARTEMISS In-Scene Atmospheric Compensation 

CIBR continuum interpolated band ratio 

CPU central processing unit 

ESN Emissivity Spectrum Normalization 

FPA focal plane array 

GPS global positioing system 

Hyper-Cam hyperspectral camera 

ISAC In-Scene Atmospheric Correction 

ISR intelligence, surveillance and reconnaissance 

ITAR International Traffic in Arms Regulations 

LUT look-up table 

LWIR longwave infrared 

MCT mercury cadmium telluride 

MET meteorological

MODTRAN Moderate Resolution Atmospheric Transmission 

MMD mean-maximum difference 

MPI message passing interface 

MWIR midwave infrared

NESR noise equivalent spectral radiance 

PDF probability density function
35

PV photovoltaic 

QTES Quick Temperature and Emissivity Separation

R&D research and development

RGB red, green, blue 

RMSE root mean square error 

SiAs silicon arsenide 

SNR signal-to-noise ratio

SPICE Spectral and Polarimetric Imagery Collection Experiment 

TES temperature-emissivity separation 

TUD transmission, up-welling, downwelling radiance 
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