effective methods for detecting interesting patterns...
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02/03/2017, WS ASI, Rome Marco DIANI
Effective methods for detecting interesting patterns in hyperspectral data
Marco DianiRemote Sensing and Image Processing Group
Department of Information Engineering, University of Pisa
56122 Pisa, Italy
m.diani@iet.unipi.it
02/03/2017, WS ASI, Rome Marco DIANI
RS&IP Group: main projects
PROJECT DESCRIPTION Funded by/users-customers PRIME
HIGHSENSE2013-2015 – Very high spatial and spectral resolution remote sensing: a novel integrated data analysis system
Italian Ministry of University and Research -MIUR
University of Genoa
SAP4PRISMA2010-2014 – Development of algorithms and products for applications in agriculture and land monitoring to support the PRISMA mission
Italian Space Agency - ASI CNR IMAA
DUCAS2009 – 2013 – Detection in Urban scenario using Combined Airborne imaging Sensors
European Defence Agency - EDA FOI
SULA2010-2012 – Advanced Sensor for Underwater Laser 3D Analysis and Detection
Italian Ministry of Defence DII
COSMOSkyMed
2010-2012 – Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures
Italian Space Agency - ASI University of Genoa
ECOMOS2014- The European Computer Model For Optronic System Performance Prediction (ECOMOS)"
European Defence Agency - EDA DLR
HIPOD2006 - Hyperspectral Imaging Program fOrDefense
European Defence Agency - EDA FOI
Projects funded by or in cooperation with:
Italian Space Agency (ASI)
Ministry of Defence
EDA (ONERA, TNO, RMA, FFI, FGAN etc.)
MIUR
Tuscany Region
National Industry SELEX-ES
Local companies (IDS, FlyBy, etc.)
• Permanent staff– Prof. Marco Diani (Italian Naval Academy)– Prof. Giovanni Corsini (University of Pisa)– Dr. Nicola Acito (Italian Naval Academy)– Dr. Stefania Matteoli (CNR-IEIIT)
• PhD students– Matteo Moscadelli– Dr. Zingoni Andrea
02/03/2017, WS ASI, Rome Marco DIANI
– Object/material detection in HSI
– Object/material detection taxonomy
• Single image analysis
– Anomaly detection
– Spectral matching
• Multitemporal image analysis
– Change detection
– Object relocation
– The “Viareggio 2013” trial
– Conclusions
Outline
02/03/2017, WS ASI, Rome Marco DIANI
The detection problem
Objective: generate a gray scale image (black/white after thresholding) where intensity measures
the degree of interest of the pixels (black=uninteresting/background, white=object/material of
interest).
Hypothesis: Object pixels cover a small fraction of the image.
Detection approach:
Detection vs classification:
Training: in general, only one object reference spectrum is available. Background class must
be learned from the data themselves. Background includes most of the image pixels and is
made up of different classes.
Decision strategy: Bayesian approach based on minimization of the average error probability
does not fit. Neyman-Pearson criterion is invoked. CFAR property is desired.
Dimensionality reduction: methods must preserve rare objects (PCA cannot be used).
Real-time or near real-time is often required.
Detection vs unmixing
not estimated as physical endmembers with corresponding abundances.
0
1
: target material not present On the basis of decide:
: target material present
H
H
X
Noise and lack of fit1 1
Target materials signatures Background signatures
T B
k n
n n
t b t k b n
k n
a a
X Sa Ba w s b w
, 1, 2, ,n B
n nb
Structured background model
(subpixel)
02/03/2017, WS ASI, Rome Marco DIANI
HSI processing for object detection
Anomaly Detection algorithm
Background Characterization
AD MAP
ANOMALY DETECTION
0 0.5 1 1.5 2 2.50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
[m]
Reflecta
nce
Sensor-measured Radiance image
Object Spectral Signature
(Reflectance in VNIR-SWIR)
Object Detection algorithm
Background Characterization
OD MAP
MATERIAL/OBJECT DETECTION
SSB OD
Atmospheric Compensation
5
2.7
2.8
2.9
3
3.1
3.2
3.3
3.4
100 200 300 400 500 600 700
50
100
150
200
T1T2T3T4T5
T6
02/03/2017, WS ASI, Rome Marco DIANI
SSB OD: FM-TD scheme
0 0.5 1 1.5 2 2.50
2000
4000
6000
8000
10000
12000
14000
16000
18000
[m]
Radia
nce [
W
/ c
m2 s
r
m]
0 0.5 1 1.5 2 2.50
2000
4000
6000
8000
10000
12000
14000
16000
18000
[m]
Radia
nce [
W
/ c
m2 s
r
m]
0 0.5 1 1.5 2 2.50
2000
4000
6000
8000
10000
12000
14000
16000
18000
[m]
Radia
nce [
W
/ c
m2 s
r
m]
Set of at-sensor radiance converted
spectra
FORWARD MODELING
MODTRAN C
m
m
sL
1
)(~
reflectance (spectralsignature)
OBJECT SUBSPACE ESTIMATION
N
TTTT ,...,,21
Object subspace basis
SUBSPACE-BASED OBJECT
DETECTION ALGORITHM
ATMOSPHERICCONDITIONS;
VIEWINGCONDITIONS.
sL
At-sensor measured radiance
OFF-LINE
ATMOSPHERICCONDITIONS;
VIEWINGCONDITIONS.
ATMOSPHERICCONDITIONS;
VIEWINGCONDITIONS.
detection statistic
02/03/2017, WS ASI, Rome Marco DIANI
Unsupervised multitemporal analysis: anomalous change detection
50 100 150 200 250 300 350 400
50
100
150
200
250
300
350
400
450
500
50 100 150 200 250 300 350 400
50
100
150
200
250
300
350
400
450
50050 100 150 200 250 300 350 400
50
100
150
200
250
300
350
400
450
500
Radiometric and
atmospheric
equalization
Change
detection
algorithm*
Flight #1
Flight #2
Red spots are changes documented in the ground truth
*CD strategy
Takes into account
coregistration error
02/03/2017, WS ASI, Rome Marco DIANI
Radiometric and
atmospheric
equalization
Spectral
Matching
Flight #1
Flight #2 (wider area)
Spectral
signature
selection
Supervised multitemporal analysis: object rediscovery
ACE GLOBAL
OBJ SUBSPACE
3
4
21
02/03/2017, WS ASI, Rome Marco DIANI
Combining search criteria reduces # of detections
SSB Object detectionMap of pixels whose
spectrum resembles the
reference one
ACD mapMap of pixels where an object
has entered the scene
ACE GLOBAL
OBJ SUBSPACE
ACD
Covariance eq.
Robust to coreg. errors
02/03/2017, WS ASI, Rome Marco DIANI
Combining search criteria reduces # of detections
Pixels whose spectrum
resembles the reference
one
Pixels where an object has
entered the scene .AND.
02/03/2017, WS ASI, Rome Marco DIANI
«Viareggio 2013» trial
N. Acito, S. Matteoli, A. Rossi, M. Diani, G. Corsini, Hyperspectral Airborne «Viareggio 2013 Trial» Data Collection for
Detection Algorithm Assessment, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
Vol. 9, Issue: 6, June 2016.
Free data and ground truth available online at http://rsipg.dii.unipi.it/
Campagna di misura
condotta da
CISAM – Pisa
Leonardo (Finmeccanica)
Università di Pisa
02/03/2017, WS ASI, Rome Marco DIANI
Conclusion
Object/material detection is a method for the automatic analysis of hyperspectral images
which is of potential interest in different applications (detection and tracking of pollutants,
detection of areas of vegetation stress, geology, wide area surveillance, search and
rescue - SAR) and for different final users (civil protection, coast guard, agencies for
defense and security, etc.).
Object detection aim to drive the operator’s attention to few ROIs that deserve further
analysis. This reduces the time required to explore wide areas searching for specific
objects and materials.
Hyperspectral sensors complement the information carried by other instruments
(broadband imagery, SAR, etc.) and improve the capability of detecting objects/materials
in a monitored area using the information carried by the spectrum of the radiance
emitted/reflected from an object.
Custom advanced multidimensional signal processing is needed to detect small dim
objects.
Research is still ongoing on many aspects of the HSI detection problem.
Detectability in a subpixel framework.
Technological and processing aspects related to the exploitation of such spectral
regions as SWIR, MWIR, LWIR. This is especially true for MWIR and LWIR regions
that are necessary to guarantee day/night operability.
Benefits from big data analysis?
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