Welcome to OTB CookBook’s documentation!¶
Orfeo ToolBox (OTB) is an open source project for state-of-the-art remote sensing. Built on the shoulders of the open-source geospatial community, it can process high resolution optical, multispectral and radar images at the terabyte scale. A wide variety of applications are available: from ortho-rectification or pansharpening, all the way to classification, SAR processing, and much more!
All of OTB’s algorithms are accessible from its graphical interface called Monteverdi, from QGIS, Python, the command line or C++. Monteverdi is an easy to use, hardware accelerated visualization tool for satellite images in sensor geometry. With it, end-users can visualize huge raw imagery products and access all of the applications in the toolbox. From ressource limited laptops to high performance clusters, OTB is available on Windows, Linux and Mac. It is community driven, extensible and heavily documented. Orfeo ToolBox is not a black box!
This is the CookBook documentation for users. If you are new to OTB and Monteverdi, start here. It will go through how to install OTB on your system, how to start using Monteverdi and OTB applications to view and process your data, and recipies on how to accomplish typical remote sensing tasks. Finally, there is also documentation on every application shipped with OTB.
For other documentation, be sure to read:
- OTB Software Guide for advanced users and developers. The software guide contains documented code examples, descriptions of the ITK pipeline model, multithreading and streaming functionalities, and an introduction to the C++ API.
- Doxygen, for exhaustive documentation of the C++ API.
Contents¶
- Installation
- A brief tour of OTB-Applications
- A brief tour of Monteverdi
- Recipes
- Using Pleiades images in OTB Applications and Monteverdi
- From raw image to calibrated product
- SAR processing
- Residual registration
- Image processing and information extraction
- Classification
- Feature extraction
- Stereoscopic reconstruction from VHR optical images pair
- BandMathImageFilterX (based on muParserX)
- Numpy processing in OTB Applications
- Applications
- Image Manipulation
- Vector Data Manipulation
- Calibration
- Geometry
- Bundle to perfect sensor
- Cartographic to geographic coordinates conversion
- Convert Sensor Point To Geographic Point
- Ply 3D files generation
- Generate a RPC sensor model
- Grid Based Image Resampling
- Image Envelope
- Ortho-rectification
- Pansharpening
- Refine Sensor Model
- Image resampling with a rigid transform
- Superimpose sensor
- Image Filtering
- Feature Extraction
- Binary Morphological Operation
- Compute Polyline Feature From Image
- Fuzzy Model estimation
- Edge Feature Extraction
- Grayscale Morphological Operation
- Haralick Texture Extraction
- Homologous Points Extraction
- Line segment detection
- Local Statistic Extraction
- Multivariate alteration detector
- Radiometric Indices
- SFS Texture Extraction
- Vector Data validation
- Stereo
- Learning
- Classification Map Regularization
- Confusion matrix Computation
- Compute Images second order statistics
- Fusion of Classifications
- Polygon Class Statistics
- Sample Extraction
- Sample Selection
- Image Classification
- Unsupervised KMeans image classification
- SOM Classification
- Train a classifier from multiple images
- Train Vector Classifier
- Predict Regression
- Train a regression model
- Segmentation
- ComputeOGRLayersFeaturesStatistics
- Connected Component Segmentation
- Hoover compare segmentation
- Exact Large-Scale Mean-Shift segmentation, step 2
- Exact Large-Scale Mean-Shift segmentation, step 3 (optional)
- Exact Large-Scale Mean-Shift segmentation, step 4
- OGRLayerClassifier
- Segmentation
- TrainOGRLayersClassifier (DEPRECATED)
- Miscellaneous