teaching remote sensing with otb applications & monterverdi (and a little of bash)
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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 FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash)
Introduction Use of OTB & Monteverdi Sample cases Comments
Outline
Introduction
Use of OTB & Monteverdi
Sample cases
Comments
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash)
Introduction Use of OTB & Monteverdi Sample cases Comments
Introduction
Use of OTB & Monteverdi
Sample cases
Comments
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 3 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
ContextENSAT: Graduate School of Life Sciences of Toulouse
I Ministry of Higher Education and ResearchI National Polytechnic Institute of Toulouse
3 years program (after 2 years post-bac)
Year 1 2 3Program Basics Image analysis and classification Case studySession Only Lectures Lectures and labworks ProjectStudents ~150 ~70 ~25
BackgroundI Life science (biology, agronomy, entomology . . . )I Applied Statistics with RI Knowledge in programming (Python)
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Introduction Use of OTB & Monteverdi Sample cases Comments
Objectives
How to use remote sensing data for agricultural and environmental sciences?
Overview of main applicationsBasics of physicsBasics of image processing and applied statistics for processing in
I Spectral domainI Spatial domainI Temporal domain
Links withI GISI Data bases
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Introduction Use of OTB & Monteverdi Sample cases Comments
Overview of the program
1. Radiometric analysis (Monteverdi)1.1 Colorization and spectral/radiometric analysis1.2 Manual segmentation (1 band and 2 bands)
2. NDVI (Monteverdi then OTB Applications)2.1 Computation and segmentation2.2 Change detection
3. Supervised classification (Monteverdi then OTB Applications)3.1 Classification of one date3.2 Classification of two dates3.3 Classification of SITS
4. Case study (Monteverdi then OTB Applications)4.1 Detection of forest in historical maps4.2 Classification of urban areas
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Introduction Use of OTB & Monteverdi Sample cases Comments
Data 1
Figure: Fabas forest area - <2013-10-12 Sat> (Pleiades - © CNES 2013, distribution Airbus DS/ Spot Image).
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Introduction Use of OTB & Monteverdi Sample cases Comments
Data 2
Figure: Fabas forest area - <2013-12-10 Tue> (Pleiades - © CNES 2013, distribution Airbus DS/ Spot Image).
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Introduction Use of OTB & Monteverdi Sample cases Comments
Introduction
Use of OTB & Monteverdi
Sample cases
Comments
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Introduction Use of OTB & Monteverdi Sample cases Comments
Configuration
Ubuntu (14.04) LTSOTB, Monteverdi from PPARAM: 4 GBCPU: Intel 4 CoresDrive storage: 5 GB/tmp: 2GB
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Introduction Use of OTB & Monteverdi Sample cases Comments
MonteverdiOpen and visualize remote sensing images
Use visualization tools of MonteverdiRadiometric analysis
Simple segmentation (BandMath)-exp "(im1b1<-0.29?1:(im1b1<0.1297?2:(im1b1<0.2597?3:4)))"
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Introduction Use of OTB & Monteverdi Sample cases Comments
From Monteverdi to OTB Apps
Change detection
Figure: Before Figure: After
Analysis the changes interms of radiometricvariation using MonteverditoolsSet-up an algorithmImplement it using OTBApps
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Introduction Use of OTB & Monteverdi Sample cases Comments
OTB Apps
#!/bin/bash
# NDVIfor i in *tifdo
otbcli_RadiometricIndices -in $i -list Vegetation:NDVI \-channels.red 3 -channels.nir 4 -out ${i%%.tif}_ndvi.tif
done
# Difference NDVI after/beforeotbcli_BandMath -il *_ndvi.tif -out diff_ndvi.tif -exp "im1b1-im2b1"
# Thresholdotbcli_BandMath -il diff_ndvi.tif -out change_detection.tif \-exp "(im1b1<-0.34?1:0)"
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Introduction Use of OTB & Monteverdi Sample cases Comments
Introduction
Use of OTB & Monteverdi
Sample cases
Comments
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Introduction Use of OTB & Monteverdi Sample cases Comments
Comparison of classifiers 1/2Different data sets
I <2013-12-10 Tue>I <2013-10-12 Sat>I <2013-10-12 Sat> and <2013-12-10 Tue>
Different methodsI K-nnI Linear SVMI GMM (Bayes)I 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.orgDiscussion on:
I Classification accuracyI Processing timeI Model
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Introduction Use of OTB & Monteverdi Sample cases Comments
Comparison of classifiers 2/2
Figure: Classification using two datesMathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 16 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Construction of SITSData sets: 13 FORMOSAT-2 images
Figure: Dates available
Workplan:I Build the SITSI Discriminate between winter/summer crops
One solution:# Compute the NDVI for each datefor i in Sud*.tifdo
otbcli_BandMath -il $i -out ${i%%.tif}_ndvi.tif \-exp "(im1b4-im1b3)/(im1b4+im1b3)"
done# Concatenate all datesotbcli_ConcatenateImages -il *_ndvi.tif -out serie_2012.tif# Cleanrm *_ndvi.tif
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Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
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Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Interpretation of SITS
Mathieu FauvelTeaching Remote Sensing with OTB Applications & Monterverdi (and a little of Bash) 18 of 23
Introduction Use of OTB & Monteverdi Sample cases Comments
Analysis of the model
Figure: Mean temporal NDVI
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Introduction Use of OTB & Monteverdi Sample cases Comments
Spatial filteringMorphological operationsMedian and mean filter
Figure: Historical map
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Introduction Use of OTB & Monteverdi Sample cases Comments
Spatial filteringMorphological operationsMedian and mean filter
Figure: Filtered image
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Introduction Use of OTB & Monteverdi Sample cases Comments
Introduction
Use of OTB & Monteverdi
Sample cases
Comments
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Introduction Use of OTB & Monteverdi Sample cases Comments
Could be improved
InstallationVisualization of data (?)
I Dynamic (for labeled data)I Profile (temporal, spectral . . . )
Classification processI Number of selected pixels ?I Which ones ?I Separate process training/validation
Regression, estimation ?
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Introduction Use of OTB & Monteverdi Sample cases Comments
Positive points
Monteverdi & OTB Apps work really wellFast and lightMore powerful than . . .Free and multiplatformCustomizable
I Template filtersI Geodesic filtersI . . .
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