METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
Hyperspectral data and MaxEnTES algorithm: perspectives for novel
application products
V. Nardino, D. Guzzi, A. Barducci, R. Carlà,
I. Pippi, V. Raimondi
IFAC-CNR
ASI PRISMA Workshop Roma, 1-3 March 2017
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
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1),(
Planck formula:
Measured radiance in the i-th channel:
N values for emissivity+
Temperature value
)(ii
ill-posed problem
Solutions using a priori assumptions:• Model Emittance Calculation (Kahle et al., 1980),• Grey Body Emissivity (Barducci et al., 1996),• Emissivity Spectrum Normalization (ESN) or Optimum Band Selection (OBS): selection of the maximum brightness temperature band (Warner et al., 1990)
The background
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METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
THE METHOD
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
Entropy H as estimate of information
Given a probability distribution function p(xi) , the (information) entropy H
estimates the number of “bits” for coding p without loosing information:
i
iixpxpH )(log)(
H measures the amount of
information (i.e. bits if log2 is used)
pk peaked H small (needed less
bits for coding it)
E. T Jaynes* introduces (1957) the MaxEnt principle: Maximum entropy “represents the most honest description of our state of knowledge”
(*) E. T Jaynes, “Information theory and statistical mechanics,” Phys. Rev., vol. 106, no. 4, pp. 620–630, May 1957.
The MaxEnTES
approach
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
NO
YES
Temperature Range Selection
(provides MaxEnt metrics)
Expectation values of t and e() using t0 as
first guess
TRS MaxEnt e() > emax
e() > emax
emin, emax
MaxEnTES
algorithm scheme
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
THE PERFORMANCES
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
0,7
0,75
0,8
0,85
0,9
0,95
1
7,0 8,0 9,0 10,0 11,0 12,0 13,0
Emis
sivi
ty
Wavelength (um)
Minerals emissivityE{e} gypsum measured emiss. gypsum E{e} alunite
measured emiss. alunite E{e} hematite measured emiss. hematite
E{e} goethite measured emiss. goethite E{e} quartz
measured emiss. quartz E{e} magnetite measured emiss. magnetite
MaxEnTES applied to simulated data:
measured vs. retrieved
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
0,7
0,75
0,8
0,85
0,9
0,95
1
0,7 0,8 0,8 0,9 0,9 1,0 1,0
Max
Ent
em
issi
vity
Measured emissivity
Measured vs. MaxEnt emissivity scatter plotgypsum
alunite
hematite
goethite
quartz
magnetite
muscovite
olivine
constructionconcretebare red brick
constructionasphaltsea water
grey silty clay
reddish brownfine sandy loam
MaxEnTES applied to simulated data:
performances
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07Standard deviation
MaxEnTES applied to simulated data:
performances
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
0
10
20
30
40
50
60
70
80
90
0
1
2
3
4
5
6
7
8
0,001 0,01 0,1 1
SNR
(d
B)
Tem
pe
ratu
re (
K)
Noise standard deviation
Threshold effect (SNR < 25 dB)
Temperature bias
Temperature standard dev.
SNR (avg. on all channels)
MaxEnTES applied to simulated data:
performances
Threshold effect
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
MIVIS dataset:location: Alps mountains (Italy)date and time: July 1999, 10:55 local timeground height: between 1 km and 2 kmradiometric calibration: on board black bodyatmospheric correction:
with:
t: ground to sensor optical thickness r0: average ground reflectanceS↑, S↑: up-welling, down-welling irradiance
atmospheric emissiongroud reflectedMaxEnTES input
to-sensor transmittance
r
tt
SeSeTBL 0 ,
Ground emitted radiance 8.38 µm
Band index Wavelength (µm) FWHM (µm)
#93 8.37776 0.153924
#94 8.7507 0.1483
#95 9.17875 0.159724
#96 9.57371 0.155506
#97 10.0028 0.149747
#98 10.4292 0.174995
#99 10.9272 0.176988
#100 11.4201 0.166759
#101 11.9046 0.16783
#102 12.4171 0.18534Ground emitted radiance 12.42 µm
MaxEnTES applied to MIVIS images:
dataset
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
MaxEnTES applied to MIVIS images:
comparison with other methods
MaxEnTES
Estimated temperature for the MIVIS dataset
GBE MEC
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
MaxEnTES / MEC comparison: ground temperature
MaxEnTES temperature image MEC temperature image
• MaxEnTES image is “softer” (noisy bands contribution is distributed along all data);• MEC uses the temperature from an arbitrary band (at longer wavelength).
MaxEnTES applied to MIVIS data:
performances
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
THE PERSPECTIVES
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
Fragmented scenario
Emissivity
characterisation on man-
made targets
Reliable temperature
mapping
High spatial detail needed
Hypersharpening/ data
integration with VNIR
MaxEnTES Applications:
URBAN ENVIRONMENTS
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
TES not essential for natural
targets, but useful for
geological features
characterisation based on
emissivity spectral analysis
High spatial detail not required
MaxEnTES Applications:
GEOLOGICAL FEATURES
Geological feature?
Main differences
MaxEnTES 10.00 µm
MEC 10.00 µm
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
Fires front detection
Pros in case of saturated
pixels
Synergies with:
Hypersharpening
Vegetation damage severity level
using hyperspectral VNIR data
MaxEnTES Applications:
FIRESFire
fronts
MaxEnTES 12.42 µm
MEC 12.42 µm
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
CONCLUSIONS
METEORS groupMEthods and TEchniques for active and passive Optical Remote Sensing
No external hypothesis required for TES
Results consistent with other methods
Less noisy images
Threshold effect vs. SNR
Less sensitive to saturated pixels
Emissivity of geological targets
Fire fronts detection
Emissivity of manmade targets in urban
environment
T
H
A
N
K
Y
O
U!
MaxEnTES final considerations