Presentation of the Monteverdi application

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Presentation of the Orfeo Toolbox framework Monteverdi

Presentation of the Orfeo Toolbox framework Monteverdi

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Contents of the presentation

Introduction

Presentation of the menu and modules

Special features

Future evolutions

Monteverdi Monteverdi

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Introduction


Special features

Future evolutions


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Framework to simply and interactively build a image processing streaming pipeline

Orfeo Toolbox components :

Functions and filters

Applications

Global framework

=> Monteverdi

What is Monteverdi ?What is Monteverdi ?

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Introduction


Special features

Future evolutions


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Main menu

File

SAR

Filtering

Learning

Geometry

Visualization

Help

MonteverdiMonteverdi

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File

Open dataset

Save dataset : save results (no pipeline module created)

Extract ROI from dataset : extract ROI for pipeline computation

Save dataset (advanced) : save with choice of output format, channels,

etc.

Cache dataset : execute previous pipelines elements and cache results

Concatenate images : concatenate several images

Quit


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File > Extract ROI


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File > Save dataset (advanced)

Choice of data type

Choice of output channels


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File > Concatenate images


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Visualization

Viewer (1/4)

Monteverdi – ViewerMonteverdi – Viewer

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Visualization

Viewer (2/4)


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Visualization

Viewer (3/4)


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Visualization

Viewer (4/4)


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Geometry

Orthorectification

Reproject image : reprojection of ortho-images

Superimpose two images

Homologous points extraction : manual selection of homologous points,

then re-sampling of the image on the fixed one.

GCP to Sensor Model : manual set of longitude / latitude of

geographical points, then re-sample of the image.


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Menu Geometry > Orthorectification (1/2)


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Menu Geometry > Orthorectification (2/2)


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Menu Geometry > Reproject image

Reproject the image (existence of geometric model) into another

ground reference


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Menu Geometry > Superimpose two images

Combination of geometric models of the two images (note : works with

images with geometric model)

➢ Image to be superimposed : use of the direct geometric model to

build Longitude/Latitude projection

➢Combination with the inverse model of the reference image.

➢Reprojection


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Menu Geometry > Homologous points extraction


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Menu Geometry > GCP to sensor model


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Filtering

Feature Extraction

Meanshift clustering

Pan-sharpen an image

Band math

Change detection

Threshold


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Filtering

Feature Extraction (1/3)


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Filtering



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Filtering


➢More than 65 features !

Mean, variance, Gradient, spectral angleTextures (energy, entropy, contrast, etc)

Morphological filters

Radiometric indexes• Vegetation (NDVI, ARVI, etc), Soil, Built up, Water

Edge density

Mean shiftOriginal data (=> no need to concatenate channels after filtering)

Etc

➢Use before SVM classification for example


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Filtering

Meanshift clustering


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Filtering

Pan-sharpen an image

➢Requires Panchromatic and Multispectral input images at the same

resolution


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Filtering

Band math

Addition, Substraction, Multiplication, Ratio, Shift-Scale (A*X+B)


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Filtering

Change

Detection


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Filtering

Threshold


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Learning

SVM classification

K-Means clustering


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Menu Learning > SVM classification (1/3)


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Menu Learning > K-means


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SAR

Despeckle image : apply Frost / Lee filter

Compute intensity and log-intensity


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Menu SAR > Despeckle image


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Menu SAR > Compute intensity and log-intensity


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Introduction


Special features

Future evolutions


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Right-click on a module of the pipeline

Rename : rename the label into the framework

Show module : reload the parameter window, modify them

for update

Right-click on an image

Rename : rename the label into the framework

Display viewer : direct access to the viewer

Export dataset : direct access to save window

Caching data : compute the previous pipeline processing


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Introduction


Special features

Future evolutions


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Future evolutions

Integration of new OTB modules

Delete pipeline module functionality

Load / Save project functions

Change input images option into the pipeline

+ Future needs of the Monteverdi users !


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Thank you for your attention !


Presentation of the Monteverdi application

Technology

Transcript of Presentation of the Monteverdi application