Leaders Study Program , London 2014-
Leaders Pecha Kucha (Lightening Talks) What makes a Visionary Development ? - Learning from innovations in Design, Strategy & Development in London.About: The Urban Vision- Leader Network will connect the most influential urban leaders to each other and to revolutionary city building & design concepts. Our annual leaders retreat explores innovations in design and policy in some of the world's great cities. Apply Now : http://bit.ly/tuvleaders
Comenius 2011 2013, project: An extra place at table or know each other through food, presentation prepared for the first meeting in Poland, Chrzanów, Primary School nr 8 in Chrzanów, Coordinator Bernadetta Utzig
Leaders Study Program , London 2014-
Leaders Pecha Kucha (Lightening Talks) What makes a Visionary Development ? - Learning from innovations in Design, Strategy & Development in London.About: The Urban Vision- Leader Network will connect the most influential urban leaders to each other and to revolutionary city building & design concepts. Our annual leaders retreat explores innovations in design and policy in some of the world's great cities. Apply Now : http://bit.ly/tuvleaders
Comenius 2011 2013, project: An extra place at table or know each other through food, presentation prepared for the first meeting in Poland, Chrzanów, Primary School nr 8 in Chrzanów, Coordinator Bernadetta Utzig
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Accelerate your Kubernetes clusters with Varnish Caching
Contribution_of_the_polarimetric_information.pdf
1. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Contribution of the polarimetric information in
order to discriminate target from interferences
subspaces. Application to FOPEN detection
with SAR processing 1
F.Briguia , L.Thirion-Lefevreb , G.Ginolhacc and P.Forsterc
a ISAE/University of Toulouse
b SONDRA/SUPELEC
c SATIE, Ens Cachan
1
Funded by the DGA
1/24 IGARSS 2011 July 2011
2. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Context
Objective
Detection of a target embedded in a complex environment using SAR system
SAR (Synthetic Aperture Radar)
◮ airborne antenna
◮ monostatic configuration (“stop
◮ scene seen under different angles
and go“)
◮ synthetic antenna
2/24 IGARSS 2011 July 2011
3. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Application
FoPen Detection (Foliage Penetration)
◮ Man-Made Target (MMT) located u200
z
in a forest u100
y
◮ P/L band: canopy is “transparent” m
u2
10 m
0.5
u1 0
Scattering attenuation but target u0
-10 m
detection still possible 95 m 115 m
x
Modeling
◮ Scatterers of interest
◮ Target → Deterministic scattering
◮ Tree trunks (interferences) → Deterministic scattering
◮ Others scatterers
◮ Branches, foliage → Random scattering
3/24 IGARSS 2011 July 2011
4. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
FoPen Detection
Classical SAR
No prior-knowledge on the scatterers → isotropic and white point scatterer model
Real data in VV of a truck and a trihedral in the Nezer
Simulated data in VV of a box in a forest of trunks
forest
Results
◮ Low response of the target → Target not detected
◮ High response of the forest → Many false alarms
4/24 IGARSS 2011 July 2011
5. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
FoPen Detection
Classical SAR
No prior-knowledge on the scatterers → isotropic and white point scatterer model
Real data in VV of a truck and a trihedral in the Nezer
Simulated data in VV of a box in a forest of trunks
forest
Results
◮ Low response of the target → Target not detected
◮ High response of the forest → Many false alarms
4/24 IGARSS 2011 July 2011
6. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
FoPen Detection
Classical SAR
No prior-knowledge on the scatterers → isotropic and white point scatterer model
Real data in VV of a truck and a trihedral in the Nezer
Simulated data in VV of a box in a forest of trunks
forest
Results
◮ Low response of the target → Target not detected
◮ High response of the forest → Many false alarms
4/24 IGARSS 2011 July 2011
7. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
New SAR processors
Approach
◮ To reconsider the SAR image generation by including prior-knowledge on the
scatterers of interest
◮ To generate one single SAR image
→ Use of subspace methods
Awareness of the scattering and polarimetric properties:
1. Of the target → To increase its detection
2. Of the interferences → To reduce false alarms
→
Only possible if the target and the interferences scattering have different properties
5/24 IGARSS 2011 July 2011
8. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Outline
SAR Imagery Algorithms
FoPen Simulated data
Real data
Conclusion and Future Work
6/24 IGARSS 2011 July 2011
9. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
Outline
SAR Imagery Algorithms
SAR Algorithms
Classical SAR (CSAR)
SSDSAR
OBSAR
OSISDSAR
FoPen Simulated data
Real data
Conclusion and Future Work
7/24 IGARSS 2011 July 2011
10. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SAR data configuration
SAR signal
Single Polarization p
Double Polarization
SAR signal zp ∈ CNK
SAR signal z ∈ C2NK
p .
z = .
. .
.
.
z=
.
.
.
8/24 IGARSS 2011 July 2011
11. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SAR data configuration
◮ K time samples
SAR signal
Single Polarization p
Double Polarization
SAR signal zp ∈ CNK
SAR signal z ∈ C2NK
p
z1
p
z =
.
. .
. .
.
z=
.
.
.
8/24 IGARSS 2011 July 2011
12. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SAR data configuration
◮ K time samples
◮ N antenna positions ui
SAR signal
Single Polarization p
Double Polarization
SAR signal zp ∈ CNK
SAR signal z ∈ C2NK
p
z1
.
p
z =
. .
.
.
p
zN
.
z=
.
.
.
8/24 IGARSS 2011 July 2011
13. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SAR data configuration
◮ K time samples
◮ N antenna positions ui
◮ Polarization: single VV (or HH) or
SAR signal
Single Polarization p
Double Polarization
SAR signal zp ∈ CNK
SAR signal z ∈ C2NK
p
z1
zHH
p
.
1
z =
. .
.
.
p .
zN
HH
z
z= N
.
.
.
8/24 IGARSS 2011 July 2011
14. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SAR data configuration
◮ K time samples
◮ N antenna positions ui
◮ Polarization: single VV (or HH) or double (HH and VV)
SAR signal
Single Polarization p
Double Polarization
SAR signal zp ∈ CNK SAR signal z ∈ C2NK
p
z1
zHH
1
.
p
z = .
.
. .
.
p
zN HH
z
z= N
VV
z1
.
.
.
zVV
N
8/24 IGARSS 2011 July 2011
15. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
Image generation principle
For each pixel (x, y)
Computation of the SAR response of the model
Classical model
◮ White isotropic point scatterer response
Subspace models
◮ Canonical element responses for all its orientations
◮ Generation of the subspace
9/24 IGARSS 2011 July 2011
16. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
Image generation principle
For each pixel (x, y)
Computation of the SAR response of the model
Classical model
◮ White isotropic point scatterer response
Subspace models
◮ Canonical element responses for all its orientations
◮ Generation of the subspace
Computation of the complex amplitude coefficient (or the coordinate vector)
◮ Least square estimation
9/24 IGARSS 2011 July 2011
17. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
Image generation principle
For each pixel (x, y)
Computation of the SAR response of the model
Classical model
◮ White isotropic point scatterer response
Subspace models
◮ Canonical element responses for all its orientations
◮ Generation of the subspace
Computation of the complex amplitude coefficient (or the coordinate vector)
◮ Least square estimation
Intensity
◮ Square norm of the complex amplitude
9/24 IGARSS 2011 July 2011
18. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
CSAR (Classical SAR)
Modeling
No prior knowledge on scatterers of interest.
White Isotropic point model rxy
SAR signal modeling
z = axy rxy + n
axy unknown complex amplitude, n complex white Gaussian noise of variance σ 2
Double polarization: 2 possible models
◮ trihedral scattering: rxy = r+
xy
◮ dihedral scattering: rxy = r−
xy
CSAR image intensity
Equivalence with images generated with
classical SAR processors (TDCA,
± r±† z
xy
2
Backprojection, RMA)
IC (x, y ) =
σ2
10/24 IGARSS 2011 July 2011
19. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SSDSAR (Signal Subspace Detector SAR)
Target modeling
Prior-knowledge: Target is made of a Set of Plates.
Target model: Low Rank Subspace Hxy generated from PC plates.
z z
z’ z’
α z"
β
y"=y’
y’
O α β
O y
y
α
β
x (b) x’ (c)
(a) x=x’
x"
Hxy : orthonormal basis of Hxy , λxy
unknown amplitude coordinate vector.
Signal SAR modeling Double polarization:
2 possible target subspaces
z = Hxy λxy + n ◮ trihedral scattering: Hxy = H+
xy
◮ dihedral scattering: Hxy = H−
xy
11/24 IGARSS 2011 July 2011
20. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
SSDSAR (Signal Subspace Detector SAR)
`
R. Durand, G. Ginolhac, L. Thirion-Lefevre, and P. Forster, “New SAR processor based on matched subspace
detectors,” IEEE TAES, Jan 2009.
`
F. Brigui, L. Thirion-Lefevre, G. Ginolhac and P. Forster, “New polarimetric signal subspace detectors for SAR
processors,” CR Phys, Jan 2010.
z
Goal: Improvment of target detection.
PHz
SSDSAR image intensity <H>
H† z 2
xy
IS (x, y ) =
σ2
†
PHxy = Hxy Hxy : orthogonal projector into Hxy .
<J>
11/24 IGARSS 2011 July 2011
21. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
OBSAR (Oblique SAR)
Interference modeling (Trunks)
Prior-knowledge: Trunks are dielectric cylinders lying over the ground.
Interference model: Low Rank Subspace Jxy generated from dielectric cylinders lying
over the ground.
z’=z
z z" γ
δ
O y’ O y"=y’
O δ γ
y γ
δ
x x’ x"
(a) (b) (c)
Signal SAR modeling
z = Hxy λxy + Jxy µxy + n
Jxy : orthonormal basis of Jxy , µxy unknown amplitude coordinate vector.
Double polarization: 1 possible interference subspace
12/24 IGARSS 2011 July 2011
22. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
OBSAR (Oblique SAR)
`
F. Brigui, G. Ginolhac, L. Thirion-Lefevre, and P. Forster, “New SAR Algorithm based on Oblique Projection for
Interference Reduction,” IEEE TAES, submitted.
Goals:
◮ Increase of target detection.
◮ Reduce false alarms due to deterministic interferences.
z
OBSAR image intensity
EHSz
H† EHxy Jxy z
xy
2 <H>
IOB (x, y ) =
σ2
† †
EHxy Jxy = PHxy (I − Jxy (Jxy P⊥ Jxy )−1 Jxy P⊥ ):
H H
xy xy
oblique projector into Hxy along the direction
described by Jxy .
Oblique projection of z into Hxy <J>
12/24 IGARSS 2011 July 2011
23. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
OSISDSAR (Orthogonal Interference Subspace Detector Processor)
Intensity IS Intensity II⊥
H† z 2 J′† z
xy
2
xy II⊥ (x, y ) =
IS (x, y ) = σ2
σ2
′† † †
Jxy = (Jxy P⊥ Jxy )−1 Jxy P⊥
H H
xy xy
z
z
PHz
<H>
<H>
T
J P Hz
<J>
<J>
13/24 IGARSS 2011 July 2011
24. SAR Algorithms
SAR Imagery Algorithms
CSAR
Simulated data
SSDSAR
Real data
OBSAR
Conclusion and Future Work
OSISDSAR
OSISDSAR (Orthogonal Interference Subspace Detector Processor)
`
F. Brigui, G. Ginolhac, L. Thirion-Lefevre, and P. Forster, “New SAR Algorithm based on Signal and Interference
Subspace Models,” IEEE GRS, To submit.
Goals:
◮ Increase of target detection.
◮ Reduce false alarms due to deterministic interferences.
OSISDSAR image intensity
IS (x, y ) I (x, y )
ISI⊥ (x, y ) = − I⊥
ES EI
ES = xy IS (x, y) and EI = xy II⊥ (x, y): normalization parameters
13/24 IGARSS 2011 July 2011
25. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Outline
SAR Imagery Algorithms
FoPen Simulated data
Configuration
Single Polarization (VV)
Double Polarization
Real data
Conclusion and Future Work
14/24 IGARSS 2011 July 2011
26. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Configuration
Radar parameters
u200
◮ 200 positions ui
z
y ◮ chirp with frequency bandwidth
u100
B = 100Mhz with f0 = 400MHz
m
u2
10 m
(P band)
0.5
u1 0
u0
-10 m Target and Interference
x ◮ target: metallic box (2m x 1.5m x
95 m 115 m
1) over a PC ground (Feko)
◮ interferences: tree trunks
(COSMO)
Interference subspaces
Signal subspaces
◮ Canonical element: dielectric
◮ Canonical element: PC plate
cylinder (11m × 20cm) over the
(2m × 1m)
ground
◮ Ranks: 10
◮ Ranks: 10
15/24 IGARSS 2011 July 2011
27. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
VV polarization
SSDSAR (ρ = 3.5 dB)
cible
Imax
ρ = 10 log( interf
)
Imax
16/24 IGARSS 2011 July 2011
28. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
CSAR (ρ = −2.5 dB) SSDSAR (ρ = 3.5 dB)
OBSAR (ρ = 3.5 dB) OSISDSAR (ρ = 3.5 dB)
16/24 IGARSS 2011 July 2011
29. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Analysis
◮ H VV et J VV too “close”
◮ Trunks response rejection not
possible
OBSAR (ρ = 3.5 dB) OSISDSAR (ρ = 3.5 dB)
16/24 IGARSS 2011 July 2011
30. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
CSAR (ρ = −3.5 dB) SSDSAR (ρ = 1.8 dB)
Dihedral case
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31. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
CSAR (ρ = −3.5 dB) SSDSAR (ρ = 1.8 dB)
OBSAR (ρ = 3.6 dB) OSISDSAR (ρ = 4.5 dB)
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32. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Analysis
◮ H et J enough “disjoint”
◮ Trunks response rejection
◮ OBSAR: robust to the target
modeling
◮ OSISDSAR: robust to the
interference modeling.
OBSAR (ρ = 3.6 dB) OSISDSAR (ρ = 4.5 dB)
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33. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Outline
SAR Imagery Algorithms
FoPen Simulated data
Real data
Configuration
Single Polarization (VV)
Double Polarization
Conclusion and Future Work
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34. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Configuration Radar parameters
◮ chirp with frequency
bandwidth B = 70Mhz
Pyla 2004 (ONERA) - Nezer forest with f0 = 435MHz
u
un Target and Interference
y
◮ MMT: Truck
u2
Nezer forest ◮ Other target: Trihedral
z 225 m (5520,150)
u1 ◮ Interferences: pine forest
u0
100 m (5584,126)
Interference subspaces
0 5480 m 5620 m x
◮ Canonical element:
Signal subspaces dielectric cylinder
(11m × 20cm) over the
◮ Canonical element: PC plate (4m × 2m) ground
◮ Ranks: 10 ◮ Ranks: 10
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35. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
VV polarization
CSAR
SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB)
OBSAR
OSISDSAR
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36. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
VV polarization
SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB) CSAR (ρc = 1 dB / ρt = 1.5 dB)
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37. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
VV polarization
SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB) OBSAR (ρc = 0.8 dB / ρt = 1.5 dB)
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38. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
VV polarization
SSDSAR (ρc = 0.8 dB / ρt = 1.5 dB) OSISDSAR (ρc = 1, 3 dB / ρt = 1.3 dB)
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39. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
SSDSAR (ρ = 1.7 dB)
Dihedral case
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40. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
CSAR
SSDSAR (ρ = 1.7 dB)
OBSAR
OSISDSAR
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41. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
SSDSAR (ρ = 1.7 dB) CSAR (ρ = 0.7 dB)
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42. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
SSDSAR (ρ = 1.7 dB) OBSAR (ρ = 2.3 dB)
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43. SAR Imagery Algorithms
Configuration
Simulated data
Single Polarization
Real data
Double Polarization
Conclusion and Future Work
Double polarization (dihedral case)
SSDSAR (ρ = 1.7 dB) OSISDSAR (ρ = 3.7 dB)
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44. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Outline
SAR Imagery Algorithms
FoPen Simulated data
Real data
Conclusion and Future Work
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45. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Conclusion
◮ Subspace Methods: target and interferences scattering taken into account for
the SAR image processing
◮ Double Polarization: reduction on false alarms due to the interferences possible
Future Work
◮ Awardeness of the canopy attenuation effets
◮ Cross-polarization (HV, VH)
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46. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Thank you for your attention!
Questions?
24/24 IGARSS 2011 July 2011
47. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Single polarization HH
CSAR SSDSAR
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48. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
CSAR SSDSAR
OBSAR OSISDSAR
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49. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Single polarization HH (real data)
CSAR
SSDSAR
OBSAR
OSISDSAR
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50. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Single polarization HH (real data)
SSDSAR CSAR
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51. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Single polarization HH (real data)
SSDSAR OBSAR
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52. SAR Imagery Algorithms
Simulated data
Real data
Conclusion and Future Work
Single polarization HH (real data)
SSDSAR OSISDSAR
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