The document discusses implementing Kohonen's self-organizing map (SOM) algorithm to handle missing and erroneous data in time series. It describes the SOM properties and training process. It also provides an example of a MODIS time series over Brittany, France with missing, erroneous, and clean data points to which the modifications would be applied. Finally, it discusses the benefits of implementing the SOM in a generic programming approach.
Land Cover Feature Extraction using Hybrid Swarm Intelligence Techniques - A ...IDES Editor
The findings of recent studies are showing strong
evidence to the fact that some aspects of biogeography can be
applied to solve specific problems in science and engineering.
The proposed work presents a hybrid biologically inspired
technique that can be adapted according to the database of
expert knowledge for a more focused satellite image
classification. The paper also presents a comparative study of
our hybrid intelligent classifier with the other recent Soft
Computing Classifiers such as ACO, Hybrid Particle Swarm
Optimization-cAntMiner (PSO-ACO2), Fuzzy sets, Rough-
Fuzzy Tie up and the Semantic Web Based Classifiers and
the traditional probabilistic classifiers such as the Minimum
Distance to Mean Classifier (MDMC) and the Maximum
Likelihood Classifier (MLC).
SAL3D presentation - AQSENSE's 3D machine vision libraryAQSENSE S.L.
The 3D Shape Analysis Library (http://www.aqsense.com/products/sal3d) is the first hardware independent software architecture for range map and poing cloud processing, fully oriented to laser triangulation and 3D machine vision applications.
SAL3D means speed, accuracy, and reliability to machine builders, equipment manufacturers, system integrators, and volume end users demanding maximum flexibility and customization in their vision systems. Tools can be integrated as DLL's that allow developers access to third party components usable side by side with SAL's tools resulting in rapid development of highly complex processing tasks.
Land Cover Feature Extraction using Hybrid Swarm Intelligence Techniques - A ...IDES Editor
The findings of recent studies are showing strong
evidence to the fact that some aspects of biogeography can be
applied to solve specific problems in science and engineering.
The proposed work presents a hybrid biologically inspired
technique that can be adapted according to the database of
expert knowledge for a more focused satellite image
classification. The paper also presents a comparative study of
our hybrid intelligent classifier with the other recent Soft
Computing Classifiers such as ACO, Hybrid Particle Swarm
Optimization-cAntMiner (PSO-ACO2), Fuzzy sets, Rough-
Fuzzy Tie up and the Semantic Web Based Classifiers and
the traditional probabilistic classifiers such as the Minimum
Distance to Mean Classifier (MDMC) and the Maximum
Likelihood Classifier (MLC).
SAL3D presentation - AQSENSE's 3D machine vision libraryAQSENSE S.L.
The 3D Shape Analysis Library (http://www.aqsense.com/products/sal3d) is the first hardware independent software architecture for range map and poing cloud processing, fully oriented to laser triangulation and 3D machine vision applications.
SAL3D means speed, accuracy, and reliability to machine builders, equipment manufacturers, system integrators, and volume end users demanding maximum flexibility and customization in their vision systems. Tools can be integrated as DLL's that allow developers access to third party components usable side by side with SAL's tools resulting in rapid development of highly complex processing tasks.
Optic Flow
Brightness Constancy Constraints
Aperture Problem
Regularization and Smoothness Constraints
Lucas-Kanade algorithm
Focus of Expansion (FOE)
Discrete Optimization for Optical Flow
Large Displacement Optical Flow: Descriptor Matching
DeepFlow: Large displ. optical flow with deep matching
EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
Optical Flow with Piecewise Parametric Model
Flow Fields: Dense Correspondence Fields for Accurate Large Displacement Optical Flow Estimation
Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids
FlowNet: Learning Optical Flow with Convol. Networks
Deep Discrete Flow
Optical Flow Estimation using a Spatial Pyramid Network
A Large Dataset to Train ConvNets for Disparity, Optical Flow, and Scene Flow Estimation
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Unsupervised Learning of Depth and Ego-Motion from Video
Appendix A: A Database and Evaluation Methodology for Optical Flow
Appendix B: Learning and optimization
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
DeepVO - Towards Visual Odometry with Deep LearningJacky Liu
Author:
Sen Wang1,2, Ronald Clark2, Hongkai Wen2 and Niki Trigoni2
1. Edinburgh Centre for Robotics, Heriot-Watt University, UK
2. University of Oxford, UK
Download this paper: http://senwang.gitlab.io/DeepVO/#paper
Watch video: http://senwang.gitlab.io/DeepVO/#video
In this paper a novel method for image enhancement
using PDTDFB (Pyramidal Dual-Tree Directional Filter
Bank) and interpolation has been adopted. Generally, in
digital images since the different kinds of noise highly affects
various image processing techniques it is always better to
perform denoising first. Here, first of all the image is
decomposed into two different layers namely low pass sub
band and high pass sub band after which denoising is being
performed on both the layers so as to smoothen the image.
The smoothened image is then interpolated using edgepreserving
interpolation and then amplified. Finally, the HR
(High Resolution) image is being obtained by performing
image composition.
Etude de la variation saisonnière des paramètres physico-chimiques des sédime...AM Publications
The present work appeals to a classification approach based on a network of artificial neurons of type Self-Organizing map SOM. This algorithm has been used to better discriminate individuals (measuring points) by highlighting nonlinear relationships unobtainable with classic methods of ordination. Thus, from an unsupervised learning of an artificial neural network, this algorithm searches iteratively for similarities among the observed data and represents them on a map output (Kohonen map). In this study, 88 surface sediment samples were collected at 22 stations, during the four seasons of the agricultural year 2010-2011, at the retaining of Sidi Chahed Dam (region Fez-Meknes). Each sample is represented by 14 physicochemical parameters. The SOM map input layer consists of normalized values of the 88 samples. The output is represented by a two-dimensional map.The results obtained overall present slight spatial and temporal deviation of physicochemical parameters that would be related to the nature of the geological formations, anthropogenic activity and particularly a strong adjacent agricultural activity
Ayant un fort attrait pour les médias sociaux et le digital de manière générale, je contribue aujourd'hui à faire vivre des projets qui y sont plus ou moins liés, sous différentes casquettes:
- co-organisatrice des conférences Labcom : rencontres trimestrielles de professionnels du digital et de la communication,
- coordinatrice européenne pour les Webdays : événements de 3 à 8 jours liés aux nouvelles technologies et à l'entrepreneuriat, organisés en Afrique et au Moyen Orient
- intervenante professionnelle au sein des grandes écoles et universités sur les thématiques de l'e-réputation des personnes/des marques et du community management,
- co-auteur des livres "Bien gérer sa réputation sur Internet" (nov. 2011) et "Bad buzz - Comment gérer une crise sur les médias sociaux" (oct. 2013)
Optic Flow
Brightness Constancy Constraints
Aperture Problem
Regularization and Smoothness Constraints
Lucas-Kanade algorithm
Focus of Expansion (FOE)
Discrete Optimization for Optical Flow
Large Displacement Optical Flow: Descriptor Matching
DeepFlow: Large displ. optical flow with deep matching
EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
Optical Flow with Piecewise Parametric Model
Flow Fields: Dense Correspondence Fields for Accurate Large Displacement Optical Flow Estimation
Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids
FlowNet: Learning Optical Flow with Convol. Networks
Deep Discrete Flow
Optical Flow Estimation using a Spatial Pyramid Network
A Large Dataset to Train ConvNets for Disparity, Optical Flow, and Scene Flow Estimation
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Unsupervised Learning of Depth and Ego-Motion from Video
Appendix A: A Database and Evaluation Methodology for Optical Flow
Appendix B: Learning and optimization
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
DeepVO - Towards Visual Odometry with Deep LearningJacky Liu
Author:
Sen Wang1,2, Ronald Clark2, Hongkai Wen2 and Niki Trigoni2
1. Edinburgh Centre for Robotics, Heriot-Watt University, UK
2. University of Oxford, UK
Download this paper: http://senwang.gitlab.io/DeepVO/#paper
Watch video: http://senwang.gitlab.io/DeepVO/#video
In this paper a novel method for image enhancement
using PDTDFB (Pyramidal Dual-Tree Directional Filter
Bank) and interpolation has been adopted. Generally, in
digital images since the different kinds of noise highly affects
various image processing techniques it is always better to
perform denoising first. Here, first of all the image is
decomposed into two different layers namely low pass sub
band and high pass sub band after which denoising is being
performed on both the layers so as to smoothen the image.
The smoothened image is then interpolated using edgepreserving
interpolation and then amplified. Finally, the HR
(High Resolution) image is being obtained by performing
image composition.
Etude de la variation saisonnière des paramètres physico-chimiques des sédime...AM Publications
The present work appeals to a classification approach based on a network of artificial neurons of type Self-Organizing map SOM. This algorithm has been used to better discriminate individuals (measuring points) by highlighting nonlinear relationships unobtainable with classic methods of ordination. Thus, from an unsupervised learning of an artificial neural network, this algorithm searches iteratively for similarities among the observed data and represents them on a map output (Kohonen map). In this study, 88 surface sediment samples were collected at 22 stations, during the four seasons of the agricultural year 2010-2011, at the retaining of Sidi Chahed Dam (region Fez-Meknes). Each sample is represented by 14 physicochemical parameters. The SOM map input layer consists of normalized values of the 88 samples. The output is represented by a two-dimensional map.The results obtained overall present slight spatial and temporal deviation of physicochemical parameters that would be related to the nature of the geological formations, anthropogenic activity and particularly a strong adjacent agricultural activity
Ayant un fort attrait pour les médias sociaux et le digital de manière générale, je contribue aujourd'hui à faire vivre des projets qui y sont plus ou moins liés, sous différentes casquettes:
- co-organisatrice des conférences Labcom : rencontres trimestrielles de professionnels du digital et de la communication,
- coordinatrice européenne pour les Webdays : événements de 3 à 8 jours liés aux nouvelles technologies et à l'entrepreneuriat, organisés en Afrique et au Moyen Orient
- intervenante professionnelle au sein des grandes écoles et universités sur les thématiques de l'e-réputation des personnes/des marques et du community management,
- co-auteur des livres "Bien gérer sa réputation sur Internet" (nov. 2011) et "Bad buzz - Comment gérer une crise sur les médias sociaux" (oct. 2013)
Neural networks Self Organizing Map by Engr. Edgar Carrillo IIEdgar Carrillo
This presentation talks about neural networks and self organizing maps. In this presentation,Engr. Edgar Caburatan Carrillo II also discusses its applications.
I felt bad for the people that died in that terible tragity . Inisant people died . So that is why i wrote this . Are you guilty because you are a surviver ,well if you were in the building i fell bad for you those people were in a hospital for days ,weeks ,mabey even MONTHES I do not know but I feel bad for those people that died in the building .
Overview of the PolSARpro V4.0 software. The open source toolbox for polarime...melaneum
Overview of the PolSARpro V4.0 software. The open source toolbox for polarimetric and interferometric polarimetric SAR data processing
Eric Pottier; IETR UMR CNRS 6164 - University of Rennes 1
Laurent Ferro-Famil; IETR UMR CNRS 6164
Sophie Allain; IETR UMR CNRS 6164
Shane Cloude; AELc
Irena Hajnsek; DLR-HR
Konstantinos Papathanassiou; DLR-HR
Alberto Moreira; DLR-HR
Mark Williams; GeoSAR
Andrea Minchella; ESA-ESRIN
Marco Lavalle; ESA-ESRIN
Yves-Louis Desnos; ESA-ESRIN
Toward a gui remote-sensing environment built over OTBmelaneum
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David Dubois; École de Technologie Superieure
Richard Lepage; École de Technologie Superieure
Tullio Tanzi; Telecom ParisTech
The use of Orfeo Toolbox in the context of map updatingmelaneum
The use of Orfeo Toolbox in the context of map updating
Christophe Simler; Royal Military Academy
Charles Beumier; Royal Military Academy
Christine Leignel; Université Libre de Bruxelles
Olivier Debeir; Université Libre de Bruxelles
Eléonore Wolff; Université Libre de Bruxelles
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
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.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
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Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
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zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
The Art of the Pitch: WordPress Relationships and Sales
Implementing kohonen's som with missing data in OTB
1. Implementing Kohonen’s SOM with
Missing Data in OTB
Gr´goire Mercier and Bassam Abdel Latif
e
Institut Telecom; Telecom Bretagne
CNRS UMR 3192 lab-STICC, team CID
Technopole Brest-Iroise,
CS 83818, F-29238 Brest Cedex 3, France
IGARSS, 2009
2. The Kohonen Map
Properties
Clouds and Shadows in times series
Description
Benefits in Generic Programming
Contents
1 The Kohonen Map
Properties
Description
2 Clouds and Shadows in times series
Modification to deal with missing value
Modification to deal with erroneous value
3 Benefits in Generic Programming
page 2 G Mercier & B Abdel Latif Kohonen map in the OTB
3. The Kohonen Map
Properties
Clouds and Shadows in times series
Description
Benefits in Generic Programming
The Kohonen Map
Properties
Neural network of 1 fully connected layer to be trained by supervized or
unsupervized approaches
Corresponds to a transformation R n
=⇒ R, R 2
or R
3
• Visualization
• Compression
Neurons in their neighborhood give similar response
Self-Organizing Map (SOM)
• Clustering
Often use when neighborhood of a class has significant meaning
• Classification
• Pattern recognition
• Data mining
• ...
page 3 G Mercier & B Abdel Latif Kohonen map in the OTB
4. The Kohonen Map
Properties
Clouds and Shadows in times series
Description
Benefits in Generic Programming
The Kohonen Map
Properties
Neural network of 1 fully connected layer to be trained by supervized or
unsupervized approaches
Corresponds to a transformation R n
=⇒ R, R 2
or R
3
• Visualization
• Compression
Neurons in their neighborhood give similar response
Self-Organizing Map (SOM)
• Clustering
Often use when neighborhood of a class has significant meaning
• Classification
• Pattern recognition
• Data mining
• ...
page 3 G Mercier & B Abdel Latif Kohonen map in the OTB
5. The Kohonen Map
Properties
Clouds and Shadows in times series
Description
Benefits in Generic Programming
The Kohonen Map
Properties
Neural network of 1 fully connected layer to be trained by supervized or
unsupervized approaches
Corresponds to a transformation R n
=⇒ R, R 2
or R
3
• Visualization
• Compression
Neurons in their neighborhood give similar response
Self-Organizing Map (SOM)
• Clustering
Often use when neighborhood of a class has significant meaning
• Classification
• Pattern recognition
• Data mining
• ...
page 3 G Mercier & B Abdel Latif Kohonen map in the OTB
6. The Kohonen Map
Properties
Clouds and Shadows in times series
Description
Benefits in Generic Programming
The Kohonen Map
Properties
Neural network of 1 fully connected layer to be trained by supervized or
unsupervized approaches
Corresponds to a transformation R n
=⇒ R, R 2
or R
3
• Visualization
• Compression
Neurons in their neighborhood give similar response
Self-Organizing Map (SOM)
• Clustering
Often use when neighborhood of a class has significant meaning
• Classification
• Pattern recognition
• Data mining
• ...
page 3 G Mercier & B Abdel Latif Kohonen map in the OTB
7. The Kohonen Map
Properties
Clouds and Shadows in times series
Description
Benefits in Generic Programming
Training Illustration
Neurons random initialization (cm )
Select randomly a training sample x and find the
winning neuron cmx :
x − cmx = min x − cm
m∈{1,...,M}
Update the weight of the winning neuron and of its
neighborhood:
cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)]
Loop until convergence
page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
8. The Kohonen Map
Properties
Clouds and Shadows in times series
Description
Benefits in Generic Programming
Training Illustration
Neurons random initialization (cm )
Select randomly a training sample x and find the
winning neuron cmx :
x − cmx = min x − cm
m∈{1,...,M}
Update the weight of the winning neuron and of its
neighborhood:
cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)]
Loop until convergence
page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
9. The Kohonen Map
Properties
Clouds and Shadows in times series
Description
Benefits in Generic Programming
Training Illustration
Neurons random initialization (cm )
Select randomly a training sample x and find the
winning neuron cmx :
x − cmx = min x − cm
m∈{1,...,M}
Update the weight of the winning neuron and of its
neighborhood:
cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)]
Loop until convergence
page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
10. The Kohonen Map
Properties
Clouds and Shadows in times series
Description
Benefits in Generic Programming
Training Illustration
Neurons random initialization (cm )
Select randomly a training sample x and find the
winning neuron cmx :
x − cmx = min x − cm
m∈{1,...,M}
Update the weight of the winning neuron and of its
neighborhood:
cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)]
Loop until convergence
page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
11. The Kohonen Map
Properties
Clouds and Shadows in times series
Description
Benefits in Generic Programming
Training Illustration
Neurons random initialization (cm )
Select randomly a training sample x and find the
winning neuron cmx :
x − cmx = min x − cm
m∈{1,...,M}
Update the weight of the winning neuron and of its
neighborhood:
cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)]
Loop until convergence
page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
12. The Kohonen Map
Properties
Clouds and Shadows in times series
Description
Benefits in Generic Programming
Training Illustration
Neurons random initialization (cm )
Select randomly a training sample x and find the
winning neuron cmx :
x − cmx = min x − cm
m∈{1,...,M}
Update the weight of the winning neuron and of its
neighborhood:
cm (t + 1) = cm (t) + hm,mx (t)[x(t) − cm (t)]
Loop until convergence
page 4 G Mercier & B Abdel Latif Kohonen map in the OTB
13. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Contents
1 The Kohonen Map
Properties
Description
2 Clouds and Shadows in times series
Modification to deal with missing value
Modification to deal with erroneous value
3 Benefits in Generic Programming
page 5 G Mercier & B Abdel Latif Kohonen map in the OTB
14. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Problem: 10 dates in a MODIS time series over Brittany, France
25-nov-2002
5-jan-2003
24-jan-2003
4-feb-2003
22-feb-2003
15-mar-2003
17-mar-2003
19-mar-2003
7-apr-2003
16-apr-2003
MODIS time series (NIR band, 250m resol. 10
dates). Images choosen with a cloud coverage
below 50%, zenithal angle below 20◦ overt center Erroneous data,
of Brittany. clouds
page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
15. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Problem: 10 dates in a MODIS time series over Brittany, France
25-nov-2002
5-jan-2003
24-jan-2003
4-feb-2003
22-feb-2003
15-mar-2003
17-mar-2003
19-mar-2003
7-apr-2003
16-apr-2003
MODIS time series (NIR band, 250m resol. 10
dates). Images choosen with a cloud coverage
below 50%, zenithal angle below 20◦ overt center Erroneous Data,
of Brittany. clouds
page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
16. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Problem: 10 dates in a MODIS time series over Brittany, France
25-nov-2002
5-jan-2003
24-jan-2003
4-feb-2003
22-feb-2003
15-mar-2003
17-mar-2003
19-mar-2003
7-apr-2003
16-apr-2003
MODIS time series (NIR band, 250m resol. 10
dates). Images choosen with a cloud coverage
below 50%, zenithal angle below 20◦ overt center Missing Data, sensor
of Brittany.
page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
17. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Problem: 10 dates in a MODIS time series over Brittany, France
25-nov-2002
5-jan-2003
24-jan-2003
4-feb-2003
22-feb-2003
15-mar-2003
17-mar-2003
19-mar-2003
7-apr-2003
16-apr-2003
MODIS time series (NIR band, 250m resol. 10
dates). Images choosen with a cloud coverage
below 50%, zenithal angle below 20◦ overt center Erroneous Data,
of Brittany. clouds
page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
18. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Problem: 10 dates in a MODIS time series over Brittany, France
25-nov-2002
5-jan-2003
24-jan-2003
4-feb-2003
22-feb-2003
15-mar-2003
17-mar-2003
19-mar-2003
7-apr-2003
16-apr-2003
MODIS time series (NIR band, 250m resol. 10
dates). Images choosen with a cloud coverage
below 50%, zenithal angle below 20◦ overt center Erroneous Data,
of Brittany. clouds
page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
19. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Problem: 10 dates in a MODIS time series over Brittany, France
25-nov-2002
5-jan-2003
24-jan-2003
4-feb-2003
22-feb-2003
15-mar-2003
17-mar-2003
19-mar-2003
7-apr-2003
16-apr-2003
MODIS time series (NIR band, 250m resol. 10
dates). Images choosen with a cloud coverage
below 50%, zenithal angle below 20◦ overt center Clean Data
of Brittany.
page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
20. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Problem: 10 dates in a MODIS time series over Brittany, France
25-nov-2002
5-jan-2003
24-jan-2003
4-feb-2003
22-feb-2003
15-mar-2003
17-mar-2003
19-mar-2003
7-apr-2003
16-apr-2003
MODIS time series (NIR band, 250m resol. 10
dates). Images choosen with a cloud coverage
below 50%, zenithal angle below 20◦ overt center Missing Data, sensor
of Brittany.
page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
21. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Problem: 10 dates in a MODIS time series over Brittany, France
25-nov-2002
5-jan-2003
24-jan-2003
4-feb-2003
22-feb-2003
15-mar-2003
17-mar-2003
19-mar-2003
7-apr-2003
16-apr-2003
MODIS time series (NIR band, 250m resol. 10
dates). Images choosen with a cloud coverage
below 50%, zenithal angle below 20◦ overt center Erroneous Data,
of Brittany. clouds
page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
22. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Problem: 10 dates in a MODIS time series over Brittany, France
25-nov-2002
5-jan-2003
24-jan-2003
4-feb-2003
22-feb-2003
15-mar-2003
17-mar-2003
19-mar-2003
7-apr-2003
16-apr-2003
MODIS time series (NIR band, 250m resol. 10
dates). Images choosen with a cloud coverage
below 50%, zenithal angle below 20◦ overt center Clean Data
of Brittany.
page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
23. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Problem: 10 dates in a MODIS time series over Brittany, France
25-nov-2002
5-jan-2003
24-jan-2003
4-feb-2003
22-feb-2003
15-mar-2003
17-mar-2003
19-mar-2003
7-apr-2003
16-apr-2003
MODIS time series (NIR band, 250m resol. 10
dates). Images choosen with a cloud coverage
below 50%, zenithal angle below 20◦ overt center Erroneous Data,
of Brittany. sensor
page 6 G Mercier & B Abdel Latif Kohonen map in the OTB
24. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
From Erroneous to Missing data
Type of errors
Presence of clouds (± thickness)
Presence of shadow
Impulse noise
outliers
Simple Cloud/Shadow Detector
Based on the Box and Whisker technique, rank statistics
let x = {x1 , . . . , xk , . . .} be a time series
˛ ` ´˛
xk is erroneous if ˛xk − α x3/4 − x1/4 ˛ > |x1/2 |, α = 1.5
Neuron definition
x = {NIR1 , . . . , NIRk , . . . , NIR10 }
where NIRi = stands for NIR band of MODIS at time i
page 7 G Mercier & B Abdel Latif Kohonen map in the OTB
25. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
From Erroneous to Missing data
Type of errors
Presence of clouds (± thickness)
Presence of shadow
Impulse noise
outliers
Simple Cloud/Shadow Detector
Based on the Box and Whisker technique, rank statistics
let x = {x1 , . . . , xk , . . .} be a time series
˛ ` ´˛
xk is erroneous if ˛xk − α x3/4 − x1/4 ˛ > |x1/2 |, α = 1.5
Neuron definition
x = {NIR1 , . . . , NIRk , . . . , NIR10 }
where NIRi = stands for NIR band of MODIS at time i
page 7 G Mercier & B Abdel Latif Kohonen map in the OTB
26. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
From Erroneous to Missing data
Type of errors
Presence of clouds (± thickness)
Presence of shadow
Impulse noise
outliers
Simple Cloud/Shadow Detector
Based on the Box and Whisker technique, rank statistics
let x = {x1 , . . . , xk , . . .} be a time series
˛ ` ´˛
xk is erroneous if ˛xk − α x3/4 − x1/4 ˛ > |x1/2 |, α = 1.5
Neuron definition
x = {NIR1 , . . . , NIRk , . . . , NIR10 }
where NIRi = stands for NIR band of MODIS at time i
page 7 G Mercier & B Abdel Latif Kohonen map in the OTB
27. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Kohonen Map Modification
Deal with missing value
In a series x = {x1 , . . . , xk , . . .}
if xk is missing ⇐⇒ k ∈ Mx
Where Mx is a set that contains indices of missing components
Winning Neuron Evaluation
X “ ”2
2
x − cm = x(j) − cm (j)
j ∈Mx
/
Winning Neuron Update cmx
( h i
cm;k (t) + hm,mx (t) xk − cm;k (t) if k ∈ Mx ,
/
cm;k (t + 1) =
cm;k (t) if k ∈ Mx .
page 8 G Mercier & B Abdel Latif Kohonen map in the OTB
28. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Results
Initial NIR Band Associated neuron value
25-nov-2002 25-nov-2002
page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
29. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Results
Initial NIR Band Associated neuron value
5-jan-2003 5-jan-2003
page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
30. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Results
Initial NIR Band Associated neuron value
24-jan-2003 24-jan-2003
page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
31. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Results
Initial NIR Band Associated neuron value
4-feb-2003 4-feb-2003
page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
32. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Results
Initial NIR Band Associated neuron value
19-mar-2003 19-mar-2003
page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
33. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Results
Initial NIR Band Associated neuron value
16-apr-2003 16-apr-2003
page 9 G Mercier & B Abdel Latif Kohonen map in the OTB
34. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Validation
Comparison with compositing products of MODIS/Terra
Kohonen MOD13Q1 MOD09Q1
CV-MVC through 16 days CV-MVC through 8 days
18-jan-2003 ` 02-feb-2003
a 18-jan-2003 ` 25-jan-2003
a
page 10 G Mercier & B Abdel Latif Kohonen map in the OTB
35. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Appropriated Similarity Measure
Vanishing outiers impact
Euclidean Measure (no detection):
n
X
ED(x, y) = (xi − yi )2
i=1
page 11 G Mercier & B Abdel Latif Kohonen map in the OTB
36. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Appropriated Similarity Measure
Vanishing outiers impact
Euclidean Measure (no detection):
n
X
ED(x, y) = (xi − yi )2
i=1
Euclidean Measure (with missing value):
X
ED(x, y) = (xi − yi )2
i ∈Mx
/
page 11 G Mercier & B Abdel Latif Kohonen map in the OTB
37. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Appropriated Similarity Measure
Vanishing outiers impact
Euclidean Measure (no detection):
n
X
ED(x, y) = (xi − yi )2
i=1
Euclidean Measure (with missing value):
X
ED(x, y) = (xi − yi )2
i ∈Mx
/
Sparse Measure:
n
X
D(x, y) = |xia − yia |b , b > 0, a>0
i=1
page 11 G Mercier & B Abdel Latif Kohonen map in the OTB
38. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
How to set parameters a and b
a, b = 1
100 b, a = 1 100
a =0.1
Correct matching %
80
Correct Matching %
80
a=1
60 60
40 40
20
20
0
0 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
0 0.5 1 1.5 2 2.5 3
a or b b
100 Same distribution
0 < a, b < 1.
Correct Matching %
80
fit ≈ 100% when b = 0.1.
60
He
D(x, y) = i |xi − yi |0.1
avy
P
-ta
ile
40 d
20
0
0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
b
page 12 G Mercier & B Abdel Latif Kohonen map in the OTB
39. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Comparison with other distances
pPn
a) Euclidean Distance ED(x, y) = i=1 (xi − yi )2
„ «
−1 <x, y>
b) Spectral Angle SA(x, y) = cos
x y
E(xy−¯¯
xy
c) Spectral Correlation SCorr(x, y) = σx σy
d) Spectral Info Divergence SID(x, y) = D(x y) + D(y x)
|xi − yi |0.1
P
e) Sparse Distance D(x, y) = i
page 13 G Mercier & B Abdel Latif Kohonen map in the OTB
40. The Kohonen Map
Modification to deal with missing value
Clouds and Shadows in times series
Modification to deal with erroneous value
Benefits in Generic Programming
Comparison with other distances
Statistiques des images de diff´rence
e
a) ED b) SA c) SCorr d) SID e) Sparse D
SOM (no outlier detection at training)
µ -.03575 0.03665 0.03606 0.03878 -0.0002
σ .05594 0.13173 0.14953 0.1218 0.01607
SOM (with outlier detection at training)
µ -0.00736 -0.00233 0.00228 -0.00246 -0.00018
σ 0.03516 0.08345 0.09807 0.07718 0.01609
page 13 G Mercier & B Abdel Latif Kohonen map in the OTB
41. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Contents
1 The Kohonen Map
Properties
Description
2 Clouds and Shadows in times series
Modification to deal with missing value
Modification to deal with erroneous value
3 Benefits in Generic Programming
page 14 G Mercier & B Abdel Latif Kohonen map in the OTB
42. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Generic Programming
The OTB code with Kohonen map
Periodic SOM for training
typedef itk::Statistics::EuclideanDistance< VectorType >
DistanceType;
typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType;
typedef otb::PeriodicSOM< SampleListType, MapType,
LearningBehaviorFunctorType, NeighborhoodBehaviorFunctorType >
SOMType;
SOMType::Pointer som = SOMType::New();
som->SetListSample( sampleList );
som->Set...
som->Update();
page 15 G Mercier & B Abdel Latif Kohonen map in the OTB
43. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Generic Programming
The OTB code with Kohonen map
Periodic SOM for training
SOM for classification
typedef itk::Statistics::EuclideanDistance< VectorType >
DistanceType;
typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType;
typedef otb::SOMClassifier<SampleType,SOMMapType,LabelPixelType>
ClassifierType;
ClassifierType::Pointer classifier = ClassifierType::New();
classifier->SetSample(sample.GetPointer());
classifier->SetMap(somreader->GetOutput());
classifier->Update();
page 15 G Mercier & B Abdel Latif Kohonen map in the OTB
44. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Generic Programming
The OTB code with Kohonen map
Periodic SOM for training
SOM for classification
SOM for segmentation
typedef itk::Statistics::EuclideanDistance< VectorType >
DistanceType;
typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType;
typedef otb::SOMbasedImageFilter<SampleType,SOMMapType,LabelPixelType>
FilterType;
FilterType::Pointer filter = FilterType::New();
filter->SetInputImage( inputImage );
filter->SetMap(somReader->GetOutput());
filter->Update();
page 15 G Mercier & B Abdel Latif Kohonen map in the OTB
45. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Generic Programming
The OTB code with Kohonen map for missing value
Periodic SOM for training with erroneous data
typedef itk::Statistics::EuclideanDistance<VectorType>
DistanceType;
typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType;
typedef otb::PeriodicSOM< SampleListType, MapType,
LearningBehaviorFunctorType, NeighborhoodBehaviorFunctorType >
SOMType;
SOMType::Pointer som = SOMType::New();
som->SetListSample( sampleList );
som->Set...
som->Update();
page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
46. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Generic Programming
The OTB code with Kohonen map for missing value
Periodic SOM for training with erroneous data
typedef otb::Statistics::EuclideanDistanceWithMissingValue<VectorType>
DistanceType;
typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType;
typedef otb::PeriodicSOM< SampleListType, MapType,
LearningBehaviorFunctorType, NeighborhoodBehaviorFunctorType >
SOMType;
SOMType::Pointer som = SOMType::New();
som->SetListSample( sampleList );
som->Set...
som->Update();
page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
47. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Generic Programming
The OTB code with Kohonen map for missing value
Periodic SOM for training with erroneous data
typedef otb::Statistics::EuclideanDistanceWithMissingValue<VectorType>
DistanceType;
typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType;
typedef otb::PeriodicSOM< SampleListType, MapType,
LearningBehaviorFunctorType, NeighborhoodBehaviorFunctorType >
SOMType;
typedef otb::BoxAndWhiskerImageFilter<ImageType> CloudFilterType;
SOMType::Pointer som = SOMType::New();
som->SetListSample( sampleList );
som->Set...
som->Update();
page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
48. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Generic Programming
The OTB code with Kohonen map for missing value
Periodic SOM for training with erroneous data
SOM for recovering missing data
typedef otb::Statistics::EuclideanDistanceWithMissingValue<VectorType>
DistanceType;
typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType;
typedef otb::SOMbasedImageFilter<SampleType,SOMMapType,LabelPixelType>
FilterType;
FilterType::Pointer filter = FilterType::New();
filter->SetInputImage( inputImage );
filter->SetMap(somreader->GetOutput());
filter->Update();
page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
49. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Generic Programming
The OTB code with Kohonen map for missing value
Periodic SOM for training with erroneous data
SOM for recovering missing data
SOM for recovering erroneous data
typedef otb::Statistics::FlexibleDistanceWithMissingValue<VectorType>
DistanceType;
DistanceType::SetAlphaBeta(a,b);
typedef otb::SOMMap< VectorType, DistanceType, Dimension > MapType;
typedef otb::SOMbasedImageFilter<SampleType,SOMMapType,LabelPixelType>
FilterType;
FilterType::Pointer filter = FilterType::New();
filter->SetInputImage( inputImage );
filter->SetMap(somreader->GetOutput());
filter->Update();
page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
50. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Generic Programming
The OTB code with Kohonen map for missing value
Periodic SOM for training with erroneous data
SOM for recovering missing data
SOM for recovering erroneous data
SOM for what particular application?
Set your own distance
Set the neurone type
Set the kohonen map training procedure
Set the Kohonen map way of using...
page 16 G Mercier & B Abdel Latif Kohonen map in the OTB
51. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Implementing Kohonen’s SOM with Missing Data in OTB
Conclusion
Generic tool for using missing and erroneous data
Thematic part validated by the COSTEL for land use purpose
May be adapted for many kind of supervized/unsupervised learning
procedure based on the Kohonen map.
page 17 G Mercier & B Abdel Latif Kohonen map in the OTB
52. The Kohonen Map
Clouds and Shadows in times series
Benefits in Generic Programming
Implementing Kohonen’s SOM with Missing Data in OTB
Conclusion
Generic tool for using missing and erroneous data
Thematic part validated by the COSTEL for land use purpose
May be adapted for many kind of supervized/unsupervised learning
procedure based on the Kohonen map.
Just use it!
page 17 G Mercier & B Abdel Latif Kohonen map in the OTB