This document discusses using the open-source software OTB Applications and Monteverdi to teach remote sensing techniques to students. It provides an overview of the curriculum, which includes radiometric analysis, NDVI calculation, supervised classification, and case studies. Sample code demonstrates how to perform change detection, classify images from different dates, and construct and analyze a time series of images. The software configuration and tools in Monteverdi and OTB Applications for visualization, processing, and classification are also outlined.
USING ORFEO TOOLBOX A GROWING COMPETENCE IN A COLLABORATIVE ENVIRONMENTotb
various uses : training set for MEDDE and CEREMA users, integration in a processing chain (OTB, ogr & gdal application), thematic (land cover for city planning, coastline monitoring, hasards flood), Dominique HEBRARD
Inria Tech Talk : Améliorez vos applications de robotique & réalité augmentéeStéphanie Roger
Que vous œuvriez dans le secteur de l’industrie, la robotique, la santé ou la réalité augmentée, profitez de ViSP pour développer de nouvelles opportunités business / transfert industriel.
ViSP (Visual Servoing Platform) est une solution technologique utilisée en robotique et réalité augmentée pour commander un robot à l’aide d’une caméra.
USING ORFEO TOOLBOX A GROWING COMPETENCE IN A COLLABORATIVE ENVIRONMENTotb
various uses : training set for MEDDE and CEREMA users, integration in a processing chain (OTB, ogr & gdal application), thematic (land cover for city planning, coastline monitoring, hasards flood), Dominique HEBRARD
Inria Tech Talk : Améliorez vos applications de robotique & réalité augmentéeStéphanie Roger
Que vous œuvriez dans le secteur de l’industrie, la robotique, la santé ou la réalité augmentée, profitez de ViSP pour développer de nouvelles opportunités business / transfert industriel.
ViSP (Visual Servoing Platform) est une solution technologique utilisée en robotique et réalité augmentée pour commander un robot à l’aide d’une caméra.
OTB: logiciel libre de traitement d'images satellitesotb
La multiplication des capteurs et des satellites d'une part et l'amélioration des produits issus de la télédétection d'autre part se traduisent par des applications de plus en plus nombreuses dans les divers domaines de l'observation de la Terre. Depuis plus de 7 ans le CNES développe l'OTB, une bibliothèque libre d'algorithmes de traitement d'images dédiée aux données de télédétection. La librairie et le logiciel Monteverdi fédèrent maintenant autour d'elle une large communauté d'utilisateurs et de contributeurs.
Resource Allocation in Heterogeneous NetworksTrungKienVu3
Heterogeneous Networks (HetNets) are introduced by the 3GPP as an emerging tech-
nology to provide high network coverage and capacity. The HetNets are the combi-
nation of multilayer networks such as macrocell, small cell (picocell and femtocell)
networks. In such networks, users may suffer significant cross-layer interference. To
manage the interference the 3GPP has introduced Enhanced Inter-Cell Interference
Coordination (eICIC) techniques, Almost Blank SubFrame (ABSF) is one of the time-
domain technique in the eICIC solutions. We propose a dynamically optimal ABSF
framework to enhance the small cell user downlink performance while maintains the
macro user downlink performance. We also study the mechanism to help the small
cell base stations cooperate efficiently in order to reduce the mutual interference. Via
simulation, our proposed scheme achieves a significant performance and outperforms
the existing ABSF frameworks.
Invited seminar on "Online Monitoring of Business Constraints and Metaconstraints using LTL and LDL over Finite Traces" given at the University of Luxembourg on January 16, 2015.
07b. Nanotechnologies for diagnostics and nanomedicine
Lab on a chip: Miniaturization, Soft lithographies, microfluidics (Navier-Stokes equations, laminar flow in microchannels, main microfluidic components), Selected applications to chemical microreactors, separation systems and Lab On a Chip.
COIFLET-BASED FUZZY-CLASSIFIER FOR DEFECT DETECTION IN INDUSTRIAL LNG/LPG TANKScsandit
This paper describes a classification method for raw sensor data using a Fuzzy Inference
System to detect the defects in large LNG tanks. The data is obtained from a Magnetic Flux
Leakage (MFL) sensing system which is usually used in the industry to located defects in
metallic surfaces, such as tank floors. A robotic inspection system has been developed in
conjunction with the presented work which performs the same inspection tasks at much lower
temperatures than human operators would thus reducing the shutdown time significantly which
is typically of the order of 15-20 million Dollars per day. The main challenge was to come up
with an algorithm that can map the human heuristics used by the MFL inspectors in field to
locate the defects into an automated system and yet keep the algorithm simple enough to be
deployed in near real-time applications. Unlike the human operation of the MFL equipment, the
proposed technique is not very sensitive to the sensor distance from the test surface and the
calibration requirements are also very minimal which are usually a big impediment in speedy
inspections of the floor by human operator. The use of wavelet decomposition with Coiflet
waves has been utilized here for deconvolving the essential features of the signal before
calculating the classification features. This wavelet was selected to its canny resemblance with
the actual MFL signals that makes these wavelets very natural basis function for
decomposition..
OTB: logiciel libre de traitement d'images satellitesotb
La multiplication des capteurs et des satellites d'une part et l'amélioration des produits issus de la télédétection d'autre part se traduisent par des applications de plus en plus nombreuses dans les divers domaines de l'observation de la Terre. Depuis plus de 7 ans le CNES développe l'OTB, une bibliothèque libre d'algorithmes de traitement d'images dédiée aux données de télédétection. La librairie et le logiciel Monteverdi fédèrent maintenant autour d'elle une large communauté d'utilisateurs et de contributeurs.
Resource Allocation in Heterogeneous NetworksTrungKienVu3
Heterogeneous Networks (HetNets) are introduced by the 3GPP as an emerging tech-
nology to provide high network coverage and capacity. The HetNets are the combi-
nation of multilayer networks such as macrocell, small cell (picocell and femtocell)
networks. In such networks, users may suffer significant cross-layer interference. To
manage the interference the 3GPP has introduced Enhanced Inter-Cell Interference
Coordination (eICIC) techniques, Almost Blank SubFrame (ABSF) is one of the time-
domain technique in the eICIC solutions. We propose a dynamically optimal ABSF
framework to enhance the small cell user downlink performance while maintains the
macro user downlink performance. We also study the mechanism to help the small
cell base stations cooperate efficiently in order to reduce the mutual interference. Via
simulation, our proposed scheme achieves a significant performance and outperforms
the existing ABSF frameworks.
Invited seminar on "Online Monitoring of Business Constraints and Metaconstraints using LTL and LDL over Finite Traces" given at the University of Luxembourg on January 16, 2015.
07b. Nanotechnologies for diagnostics and nanomedicine
Lab on a chip: Miniaturization, Soft lithographies, microfluidics (Navier-Stokes equations, laminar flow in microchannels, main microfluidic components), Selected applications to chemical microreactors, separation systems and Lab On a Chip.
COIFLET-BASED FUZZY-CLASSIFIER FOR DEFECT DETECTION IN INDUSTRIAL LNG/LPG TANKScsandit
This paper describes a classification method for raw sensor data using a Fuzzy Inference
System to detect the defects in large LNG tanks. The data is obtained from a Magnetic Flux
Leakage (MFL) sensing system which is usually used in the industry to located defects in
metallic surfaces, such as tank floors. A robotic inspection system has been developed in
conjunction with the presented work which performs the same inspection tasks at much lower
temperatures than human operators would thus reducing the shutdown time significantly which
is typically of the order of 15-20 million Dollars per day. The main challenge was to come up
with an algorithm that can map the human heuristics used by the MFL inspectors in field to
locate the defects into an automated system and yet keep the algorithm simple enough to be
deployed in near real-time applications. Unlike the human operation of the MFL equipment, the
proposed technique is not very sensitive to the sensor distance from the test surface and the
calibration requirements are also very minimal which are usually a big impediment in speedy
inspections of the floor by human operator. The use of wavelet decomposition with Coiflet
waves has been utilized here for deconvolving the essential features of the signal before
calculating the classification features. This wavelet was selected to its canny resemblance with
the actual MFL signals that makes these wavelets very natural basis function for
decomposition..
Quantitative Study of Innovation and Knowledge Building in Questions&Answers ...KNOWeSCAPE2014
Marija Mitrovic and Bosiljka Tadic – Quantitative Study of Innovation and Knowledge Building in Questions&Answers System with Math Tags (Talk at 2nd Annual KNOWeSCAPE Scientific Meeting, http://knowescape.org/knowescape2014-2/)
Presentation of the paper "Monitoring Business Metaconstraints Based on LTL & LDL for Finite Traces" at the 12th International Conference on Business Process Management (BPM 2014).
FORTE: Few Samples for Recognizing Hand Gestures with a Smartphone-attached R...Arthur Sluÿters
Presentation of our paper "FORTE: Few Samples for Recognizing Hand Gestures with a Smartphone-attached Radar" at the EICS 2023 conference in Swansea, UK.
A Bug Report Analysis and Search Tool (presentation for M.Sc. degree)yguarata
A M.Sc. Dissertation presented to the Federal University of Pernambuco in partial fulfillment of the requirements for the degree of M.Sc. in Computer Science.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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
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.
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:
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash)
1. Introduction Use of OTB & Monteverdi Sample cases Comments
Teaching Remote Sensing with OTB Applications & Monterverdi
(and a little of Bash)
Mathieu Fauvel
Since 2014
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash)
2. Introduction Use of OTB & Monteverdi Sample cases Comments
Outline
Introduction
Use of OTB & Monteverdi
Sample cases
Comments
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash)
3. Introduction Use of OTB & Monteverdi Sample cases Comments
Introduction
Use of OTB & Monteverdi
Sample cases
Comments
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 3 of 23
4. Introduction Use of OTB & Monteverdi Sample cases Comments
Context
ENSAT: Graduate School of Life Sciences of Toulouse
Ministry of Higher Education and Research
National Polytechnic Institute of Toulouse
3 years program (after 2 years post-bac)
Year 1 2 3
Program Basics Image analysis and classification Case study
Session Only Lectures Lectures and labworks Project
Students ~150 ~70 ~25
Background
Life science (biology, agronomy, entomology . . . )
Applied Statistics with R
Knowledge in programming (Python)
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 4 of 23
5. Introduction Use of OTB & Monteverdi Sample cases Comments
Objectives
How to use remote sensing data for agricultural and environmental sciences?
Overview of main applications
Basics of physics
Basics of image processing and applied statistics for processing in
Spectral domain
Spatial domain
Temporal domain
Links with
GIS
Data bases
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 5 of 23
6. Introduction Use of OTB & Monteverdi Sample cases Comments
Overview of the program
1. Radiometric analysis (Monteverdi)
1.1 Colorization and spectral/radiometric analysis
1.2 Manual segmentation (1 band and 2 bands)
2. NDVI (Monteverdi then OTB Applications)
2.1 Computation and segmentation
2.2 Change detection
3. Supervised classification (Monteverdi then OTB Applications)
3.1 Classification of one date
3.2 Classification of two dates
3.3 Classification of SITS
4. Case study (Monteverdi then OTB Applications)
4.1 Detection of forest in historical maps
4.2 Classification of urban areas
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 6 of 23
9. Introduction Use of OTB & Monteverdi Sample cases Comments
Introduction
Use of OTB & Monteverdi
Sample cases
Comments
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 9 of 23
10. Introduction Use of OTB & Monteverdi Sample cases Comments
Configuration
Ubuntu (14.04) LTS
OTB, Monteverdi from PPA
RAM: 4 GB
CPU: Intel 4 Cores
Drive storage: 5 GB
/tmp: 2GB
Mathieu Fauvel
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11. Introduction Use of OTB & Monteverdi Sample cases Comments
Monteverdi
Open and visualize remote sensing images
Use visualization tools of Monteverdi
Radiometric analysis
Simple segmentation (BandMath)
-exp "(im1b1<-0.29?1:(im1b1<0.1297?2:(im1b1<0.2597?3:4)))"
Mathieu Fauvel
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12. Introduction Use of OTB & Monteverdi Sample cases Comments
From Monteverdi to OTB Apps
Change detection
Figure: Before Figure: After
Analysis the changes in
terms of radiometric
variation using Monteverdi
tools
Set-up an algorithm
Implement it using OTB
Apps
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 12 of 23
13. Introduction Use of OTB & Monteverdi Sample cases Comments
OTB Apps
#!/bin/bash
# NDVI
for i in *tif
do
otbcli_RadiometricIndices -in $i -list Vegetation:NDVI
-channels.red 3 -channels.nir 4 -out ${i%%.tif}_ndvi.tif
done
# Difference NDVI after/before
otbcli_BandMath -il *_ndvi.tif -out diff_ndvi.tif -exp "im1b1-im2b1"
# Threshold
otbcli_BandMath -il diff_ndvi.tif -out change_detection.tif
-exp "(im1b1<-0.34?1:0)"
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 13 of 23
14. Introduction Use of OTB & Monteverdi Sample cases Comments
Introduction
Use of OTB & Monteverdi
Sample cases
Comments
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 14 of 23
15. Introduction Use of OTB & Monteverdi Sample cases Comments
Comparison of classifiers 1/2
Different data sets
<2013-12-10 Tue>
<2013-10-12 Sat>
<2013-10-12 Sat> and <2013-12-10 Tue>
Different methods
K-nn
Linear SVM
GMM (Bayes)
RF
Different training/validation set (15 Groups)
otbcli_TrainImagesClassifier -io.il $name -io.vd ref_data.shp
-sample.mv 5000 -sample.mt 1000 -sample.vtr 0.5
-sample.edg false -sample.vfn Class -classifier knn
-io.out classif_model/knnModel.txt
-io.confmatout classif_model/KnnConfusionMatrix.csv
Collaborative spreadsheet https://www.ethercalc.org
Discussion on:
Classification accuracy
Processing time
Model
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 15 of 23
16. Introduction Use of OTB & Monteverdi Sample cases Comments
Comparison of classifiers 2/2
Figure: Classification using two dates
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 16 of 23
17. Introduction Use of OTB & Monteverdi Sample cases Comments
Construction of SITS
Data sets: 13 FORMOSAT-2 images
Figure: Dates available
Workplan:
Build the SITS
Discriminate between winter/summer crops
One solution:
# Compute the NDVI for each date
for i in Sud*.tif
do
otbcli_BandMath -il $i -out ${i%%.tif}_ndvi.tif
-exp "(im1b4-im1b3)/(im1b4+im1b3)"
done
# Concatenate all dates
otbcli_ConcatenateImages -il *_ndvi.tif -out serie_2012.tif
# Clean
rm *_ndvi.tif
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 17 of 23
18. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
19. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
20. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
21. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
22. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
23. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
24. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
25. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
26. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
27. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
28. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
29. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
30. Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
31. Introduction Use of OTB & Monteverdi Sample cases Comments
Analysis of the model
Figure: Mean temporal NDVI
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 19 of 23
32. Introduction Use of OTB & Monteverdi Sample cases Comments
Spatial filtering
Morphological operations
Median and mean filter
Figure: Historical map
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 20 of 23
33. Introduction Use of OTB & Monteverdi Sample cases Comments
Spatial filtering
Morphological operations
Median and mean filter
Figure: Filtered image
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 20 of 23
34. Introduction Use of OTB & Monteverdi Sample cases Comments
Introduction
Use of OTB & Monteverdi
Sample cases
Comments
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 21 of 23
35. Introduction Use of OTB & Monteverdi Sample cases Comments
Could be improved
Installation
Visualization of data (?)
Dynamic (for labeled data)
Profile (temporal, spectral . . . )
Classification process
Number of selected pixels ?
Which ones ?
Separate process training/validation
Regression, estimation ?
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 22 of 23
36. Introduction Use of OTB & Monteverdi Sample cases Comments
Positive points
Monteverdi & OTB Apps work really well
Fast and light
More powerful than . . .
Free and multiplatform
Customizable
Template filters
Geodesic filters
. . .
Mathieu Fauvel
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 23 of 23