Part III
User’s guide

5 Data Representation
 5.1 Image
 5.2 PointSet
 5.3 Mesh
 5.4 Path
6 Reading and Writing Images
 6.1 Basic Example
 6.2 Pluggable Factories
 6.3 IO Streaming
 6.4 Reading and Writing RGB Images
 6.5 Reading, Casting and Writing Images
 6.6 Extracting Regions
 6.7 Reading and Writing Vector Images
 6.8 Reading and Writing Multiband Images
 6.9 Reading Image Series
7 Reading and Writing Auxiliary Data
 7.1 Reading DEM Files
 7.2 Elevation management with OTB
 7.3 Reading and Writing Shapefiles and KML
 7.4 Handling large vector data through OGR
8 Basic Filtering
 8.1 Thresholding
 8.2 Mathematical operations on images
 8.3 Gradients
 8.4 Second Order Derivatives
 8.5 Edge Detection
 8.6 Neighborhood Filters
 8.7 Smoothing Filters
 8.8 Distance Map
9 Image Registration
 9.1 Registration Framework
 9.2 ”Hello World” Registration
 9.3 Features of the Registration Framework
 9.4 Multi-Modality Registration
 9.5 Centered Transforms
 9.6 Transforms
 9.7 Metrics
 9.8 Optimizers
 9.9 Landmark-based registration
10 Disparity Map Estimation
 10.1 Disparity Maps
 10.2 Regular grid disparity map estimation
 10.3 Irregular grid disparity map estimation
 10.4 Stereo reconstruction
11 Orthorectification and Map Projection
 11.1 Sensor Models
 11.2 Map Projections
 11.3 Orthorectification with OTB
 11.4 Vector data projection manipulation
 11.5 Geometries projection manipulation
 11.6 Elevation management with OTB
 11.7 Vector data area extraction
12 Radiometry
 12.1 Radiometric Indices
 12.2 Atmospheric Corrections
13 Image Fusion
 13.1 Simple Pan Sharpening
 13.2 Bayesian Data Fusion
14 Feature Extraction
 14.1 Textures
 14.2 Interest Points
 14.3 Alignments
 14.4 Lines
 14.5 Density Features
 14.6 Geometric Moments
 14.7 Road extraction
 14.8 Cloud Detection
15 Multi-scale Analysis
 15.1 Introduction
 15.2 Morphological Pyramid
16 Image Segmentation
 16.1 Region Growing
 16.2 Segmentation Based on Watersheds
 16.3 Level Set Segmentation
17 Image Simulation
 17.1 PROSAIL model
 17.2 Image Simulation
18 Dimension Reduction
 18.1 Principal Component Analysis
 18.2 Noise-Adjusted Principal Components Analysis
 18.3 Maximum Noise Fraction
 18.4 Fast Independent Component Analysis
 18.5 Maximum Autocorrelation Factor
19 Classification
 19.1 Introduction
 19.2 Machine Learning Framework
 19.3 Supervised classification
 19.4 Unsupervised classification
 19.5 Fusion of Classification maps
 19.6 Classification map regularization
20 Object-based Image Analysis
 20.1 From Images to Objects
 20.2 Object Attributes
 20.3 Object Filtering based on radiometric and statistics attributes
 20.4 Hoover metrics to compare segmentations
21 Change Detection
 21.1 Introduction
 21.2 Change Detection Framework
 21.3 Simple Detectors
 21.4 Statistical Detectors
 21.5 Multi-Scale Detectors
 21.6 Multi-components detectors
22 Hyperspectral
 22.1 Unmixing
 22.2 Dimensionality reduction
 22.3 Anomaly detection
23 Image Visualization and output
 23.1 Images
24 Online data
 24.1 Name to Coordinates
 24.2 Open Street Map