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

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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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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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Data 1

Figure: Fabas forest area - <2013-10-12 Sat> (Pleiades - © CNES 2013, distribution Airbus DS/ Spot Image).

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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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Configuration

Ubuntu (14.04) LTSOTB, Monteverdi from PPARAM: 4 GBCPU: Intel 4 CoresDrive storage: 5 GB/tmp: 2GB

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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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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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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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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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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

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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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Interpretation of SITS

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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

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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

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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

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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

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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

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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

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Introduction Use of OTB & Monteverdi Sample cases Comments

Analysis of the model

Figure: Mean temporal NDVI

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Spatial filteringMorphological operationsMedian and mean filter

Figure: Historical map

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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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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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Positive points

Monteverdi & OTB Apps work really wellFast and lightMore powerful than . . .Free and multiplatformCustomizable

I Template filtersI Geodesic filtersI . . .

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