PIC _________________________________________________________________________________________________________

The Orfeo ToolBox Cookbook, a guide for non-developers
Updated for OTB-5.6.0

OTB Development Team
July 27, 2016
http://www.orfeo-toolbox.org

PIC

The ORFEO Toolbox is not a black box.

      Ch.D.

Foreword
Contents
List of Figures
List of Tables
1 A brief tour of OTB-Applications
 1.1 Introduction
 1.2 Installation
  1.2.1 Windows
  1.2.2 Linux 64bit
  1.2.3 MacOS X
  1.2.4 Other packages
 1.3 Using the applications
  1.3.1 Simplified use
  1.3.2 Using the command-line launcher
  1.3.3 Using the GUI launcher
  1.3.4 Using the Python interface
  1.3.5 Load/Save OTB-Applications parameters from/to file
  1.3.6 Using OTB from QGIS
2 A brief tour of Monteverdi
 2.1 Introduction
 2.2 Installation
  2.2.1 Windows
  2.2.2 Linux 64bit
  2.2.3 MacOS X
  2.2.4 Other packages
 2.3 GUI : what does it look like ?
  2.3.1 Main menu
  2.3.2 Top toolbar
  2.3.3 Image displaying
  2.3.4 Right side dock
  2.3.5 Layer stack
 2.4 Examples
  2.4.1 Optical calibration
  2.4.2 BandMath
  2.4.3 Segmentation
  2.4.4 Polarimetry
  2.4.5 Pansharpening
  2.4.6 Conclusion
3 Recipes
 3.1 From raw image to calibrated product
  3.1.1 Optical radiometric calibration
  3.1.2 Pan-sharpening
  3.1.3 Digital Elevation Model management
  3.1.4 Ortho-rectification and map projections
  3.1.5 Residual registration
 3.2 SAR processing
  3.2.1 Calibration
  3.2.2 Despeckle
  3.2.3 Polarimetry
 3.3 Image processing and information extraction
  3.3.1 Simple calculus with channels
  3.3.2 Images with no-data values
  3.3.3 Segmentation
  3.3.4 Large-Scale Mean-Shift (LSMS) segmentation
  3.3.5 Dempster Shafer based Classifier Fusion
 3.4 Classification
  3.4.1 Pixel based classification
  3.4.2 Fusion of classification maps
  3.4.3 Majority voting based classification map regularization
  3.4.4 Regression
  3.4.5 Samples selection
 3.5 Feature extraction
  3.5.1 Local statistics extraction
  3.5.2 Edge extraction
  3.5.3 Radiometric indices extraction
  3.5.4 Morphological features extraction
  3.5.5 Textural features extraction
 3.6 Stereoscopic reconstruction from VHR optical images pair
  3.6.1 Estimate epipolar geometry transformation
  3.6.2 Resample images in epipolar geometry
  3.6.3 Disparity estimation: Block matching along epipolar lines
  3.6.4 From disparity to Digital Surface Model
  3.6.5 One application to rule them all in multi stereo framework scheme
  3.6.6 Stereo reconstruction good practices
  3.6.7 Algorithm outline
 3.7 BandMathX application (based on muParserX)
  3.7.1 Syntax : first elements
  3.7.2 New operators and functions
4 Applications Reference Documentation
 4.1 Image Manipulation
  4.1.1 Color Mapping
  4.1.2 Images Concatenation
  4.1.3 Image Conversion
  4.1.4 DEM Conversion
  4.1.5 Download or list SRTM tiles related to a set of images
  4.1.6 Extract ROI
  4.1.7 No Data management
  4.1.8 Multi Resolution Pyramid
  4.1.9 Quick Look
  4.1.10 Read image information
  4.1.11 Rescale Image
  4.1.12 Split Image
  4.1.13 Image Tile Fusion
 4.2 Vector Data Manipulation
  4.2.1 Concatenate
  4.2.2 Rasterization
  4.2.3 VectorData Extract ROI
  4.2.4 Vector Data reprojection
  4.2.5 Vector data set field
  4.2.6 Vector Data Transformation
 4.3 Calibration
  4.3.1 Optical calibration
  4.3.2 SAR Radiometric calibration
  4.3.3 SAR Radiometric calibration (DEPRECATED)
 4.4 Geometry
  4.4.1 Bundle to perfect sensor
  4.4.2 Cartographic to geographic coordinates conversion
  4.4.3 Convert Sensor Point To Geographic Point
  4.4.4 Ply 3D files generation
  4.4.5 Generate a RPC sensor model
  4.4.6 Grid Based Image Resampling
  4.4.7 Image Envelope
  4.4.8 Ortho-rectification
  4.4.9 Pansharpening
  4.4.10 Refine Sensor Model
  4.4.11 Image resampling with a rigid transform
  4.4.12 Superimpose sensor
 4.5 Image Filtering
  4.5.1 Despeckle
  4.5.2 Dimensionality reduction
  4.5.3 Exact Large-Scale Mean-Shift segmentation, step 1 (smoothing)
  4.5.4 Smoothing
 4.6 Feature Extraction
  4.6.1 Binary Morphological Operation
  4.6.2 Compute Polyline Feature From Image
  4.6.3 Fuzzy Model estimation
  4.6.4 Edge Feature Extraction
  4.6.5 Grayscale Morphological Operation
  4.6.6 Haralick Texture Extraction
  4.6.7 Homologous Points Extraction
  4.6.8 Line segment detection
  4.6.9 Local Statistic Extraction
  4.6.10 Multivariate alteration detector
  4.6.11 Radiometric Indices
  4.6.12 SFS Texture Extraction
  4.6.13 Vector Data validation
 4.7 Stereo
  4.7.1 Pixel-wise Block-Matching
  4.7.2 Disparity map to elevation map
  4.7.3 Fine Registration
  4.7.4 Stereo Framework
  4.7.5 Stereo-rectification deformation grid generator
 4.8 Learning
  4.8.1 Classification Map Regularization
  4.8.2 Confusion matrix Computation
  4.8.3 Compute Images second order statistics
  4.8.4 Fusion of Classifications
  4.8.5 Image Classification
  4.8.6 Unsupervised KMeans image classification
  4.8.7 Polygon Class Statistics
  4.8.8 Predict Regression
  4.8.9 SOM Classification
  4.8.10 Sample Extraction
  4.8.11 Sample Selection
  4.8.12 Train a classifier from multiple images
  4.8.13 Train a regression model
  4.8.14 Train Vector Classifier
 4.9 Segmentation
  4.9.1 ComputeOGRLayersFeaturesStatistics
  4.9.2 Connected Component Segmentation
  4.9.3 Hoover compare segmentation
  4.9.4 Exact Large-Scale Mean-Shift segmentation, step 2
  4.9.5 Exact Large-Scale Mean-Shift segmentation, step 3 (optional)
  4.9.6 Exact Large-Scale Mean-Shift segmentation, step 4
  4.9.7 OGRLayerClassifier
  4.9.8 Segmentation
  4.9.9 TrainOGRLayersClassifier (DEPRECATED)
 4.10 Miscellanous
  4.10.1 Band Math
  4.10.2 Band Math X
  4.10.3 Images comparaison
  4.10.4 Hyperspectral data unmixing
  4.10.5 Image to KMZ Export
  4.10.6 Open Street Map layers importations applications
  4.10.7 Obtain UTM Zone From Geo Point
  4.10.8 Pixel Value
  4.10.9 SARDecompositions
  4.10.10 SARPolarMatrixConvert
  4.10.11 SARPolarSynth
  4.10.12 Vertex Component Analysis