System Overview

The purpose of this chapter is to provide you with an overview of the ORFEO Toolbox system. We recommend that you read this chapter to gain an appreciation for the breadth and area of application of OTB. In this chapter, we will make reference either to OTB features or ITK features without distinction. Bear in mind that OTB uses ITK as its core element, so all the fundamental elements of OTB come from ITK. OTB extends the functionalities of ITK for the remote sensing image processing community. We benefit from the Open Source development approach chosen for ITK, which allows us to provide an impressive set of functionalities with much less effort than would have been the case in a closed source universe!

System Organization

The Orfeo Toolbox consists of several subsystems:

Essential System Concepts

Like any software system, OTB is built around some core design concepts. OTB uses those of ITK. Some of the more important concepts include generic programming, smart pointers for memory management, object factories for adaptable object instantiation, event management using the command/observer design paradigm, and multithreading support.


OTB, as ITK uses VXL’s VNL numerics libraries. These are easy-to-use C++ wrappers around the Netlib Fortran numerical analysis routines (

Data Representation and Access

Two principal classes are used to represent data: the otb::Image and itk::Mesh classes. In addition, various types of iterators and containers are used in ITK to hold and traverse the data. Other important but less popular classes are also used to represent data such as histograms.

ITK’s Data Processing Pipeline

The data representation classes (known as data objects) are operated on by filters that in turn may be organized into data flow pipelines. These pipelines maintain state and therefore execute only when necessary. They also support multi-threading, and are streaming capable (i.e., can operate on pieces of data to minimize the memory footprint).

IO Framework

Associated with the data processing pipeline are sources, filters that initiate the pipeline, and mappers, filters that terminate the pipeline. The standard examples of sources and mappers are readers and writers respectively. Readers input data (typically from a file), and writers output data from the pipeline. Viewers are another example of mappers.

Spatial Objects

Geometric shapes are represented in OTB using the ITK spatial object hierarchy. These classes are intended to support modeling of anatomical structures in ITK. OTB uses them in order to model cartographic elements. Using a common basic interface, the spatial objects are capable of representing regions of space in a variety of different ways. For example: mesh structures, image masks, and implicit equations may be used as the underlying representation scheme. Spatial objects are a natural data structure for communicating the results of segmentation methods and for introducing geometrical priors in both segmentation and registration methods.

ITK’s Registration Framework

A flexible framework for registration supports four different types of registration: image registration, multiresolution registration, PDE-based registration, and FEM (finite element method) registration.

FEM Framework

ITK includes a subsystem for solving general FEM problems, in particular non-rigid registration. The FEM package includes mesh definition (nodes and elements), loads, and boundary conditions.

Level Set Framework

The level set framework is a set of classes for creating filters to solve partial differential equations on images using an iterative, finite difference update scheme. The level set framework consists of finite difference solvers including a sparse level set solver, a generic level set segmentation filter, and several specific subclasses including threshold, Canny, and Laplacian based methods.


ITK uses a unique, powerful system for producing interfaces (i.e., “wrappers”) to interpreted languages such as Tcl and Python. The GCC_XML tool is used to produce an XML description of arbitrarily complex C++ code; CSWIG is then used to transform the XML description into wrappers using the SWIG package. OTB does not use this system at present.

Essential System Concepts

This section describes some of the core concepts and implementation features found in ITK and therefore also in OTB.

Generic Programming

Generic programming is a method of organizing libraries consisting of generic—or reusable—software components. The idea is to make software that is capable of “plugging together” in an efficient, adaptable manner. The essential ideas of generic programming are containers to hold data, iterators to access the data, and generic algorithms that use containers and iterators to create efficient, fundamental algorithms such as sorting. Generic programming is implemented in C++ with the template programming mechanism and the use of the STL Standard Template Library.

C++ templating is a programming technique allowing users to write software in terms of one or more unknown types T. To create executable code, the user of the software must specify all types T (known as template instantiation) and successfully process the code with the compiler. The T may be a native type such as float or int, or T may be a user-defined type (e.g., class). At compile-time, the compiler makes sure that the templated types are compatible with the instantiated code and that the types are supported by the necessary methods and operators.

ITK uses the techniques of generic programming in its implementation. The advantage of this approach is that an almost unlimited variety of data types are supported simply by defining the appropriate template types. For example, in OTB it is possible to create images consisting of almost any type of pixel. In addition, the type resolution is performed at compile-time, so the compiler can optimize the code to deliver maximal performance. The disadvantage of generic programming is that many compilers still do not support these advanced concepts and cannot compile OTB. And even if they do, they may produce completely undecipherable error messages due to even the simplest syntax errors.

Include Files and Class Definitions

In ITK and OTB classes are defined by a maximum of two files: a header .h file and an implementation file—.cxx if a non-templated class, and a .hxx if a templated class. The header files contain class declarations and formatted comments that are used by the Doxygen documentation system to automatically produce HTML manual pages.

In addition to class headers, there are a few other important ITK header files.


defines standard system-wide macros (such as Set/Get, constants, and other parameters).


defines numeric characteristics for native types such as its maximum and minimum possible values.


is used to define operating system parameters to control the compilation process.

Object Factories

Most classes in OTB are instantiated through an object factory mechanism. That is, rather than using the standard C++ class constructor and destructor, instances of an OTB class are created with the static class New() method. In fact, the constructor and destructor are protected: so it is generally not possible to construct an OTB instance on the heap. (Note: this behavior pertains to classes that are derived from itk::LightObject. In some cases the need for speed or reduced memory footprint dictates that a class not be derived from LightObject and in this case instances may be created on the heap. An example of such a class is itk::EventObject.)

The object factory enables users to control run-time instantiation of classes by registering one or more factories with itk::ObjectFactoryBase. These registered factories support the method CreateInstance(classname) which takes as input the name of a class to create. The factory can choose to create the class based on a number of factors including the computer system configuration and environment variables. For example, in a particular application an OTB user may wish to deploy their own class implemented using specialized image processing hardware (i.e., to realize a performance gain). By using the object factory mechanism, it is possible at run-time to replace the creation of a particular OTB filter with such a custom class. (Of course, the class must provide the exact same API as the one it is replacing.) To do this, the user compiles their class (using the same compiler, build options, etc.) and inserts the object code into a shared library or DLL. The library is then placed in a directory referred to by the OTB_AUTOLOAD_PATH environment variable. On instantiation, the object factory will locate the library, determine that it can create a class of a particular name with the factory, and use the factory to create the instance. (Note: if the CreateInstance() method cannot find a factory that can create the named class, then the instantiation of the class falls back to the usual constructor.)

In practice object factories are used mainly (and generally transparently) by the OTB input/output (IO) classes. For most users the greatest impact is on the use of the New() method to create a class. Generally the New() method is declared and implemented via the macro itkNewMacro() found in Modules/Core/Common/include/itkMacro.h.

Smart Pointers and Memory Management

By their nature object-oriented systems represent and operate on data through a variety of object types, or classes. When a particular class is instantiated to produce an instance of that class, memory allocation occurs so that the instance can store data attribute values and method pointers (i.e., the vtable). This object may then be referenced by other classes or data structures during normal operation of the program. Typically during program execution all references to the instance may disappear at which point the instance must be deleted to recover memory resources. Knowing when to delete an instance, however, is difficult. Deleting the instance too soon results in program crashes; deleting it too late and memory leaks (or excessive memory consumption) will occur. This process of allocating and releasing memory is known as memory management.

In ITK, memory management is implemented through reference counting. This compares to another popular approach—garbage collection—used by many systems including Java. In reference counting, a count of the number of references to each instance is kept. When the reference goes to zero, the object destroys itself. In garbage collection, a background process sweeps the system identifying instances no longer referenced in the system and deletes them. The problem with garbage collection is that the actual point in time at which memory is deleted is variable. This is unacceptable when an object size may be gigantic (think of a large 3D volume gigabytes in size). Reference counting deletes memory immediately (once all references to an object disappear).

Reference counting is implemented through a Register()/Delete() member function interface. All instances of an OTB object have a Register() method invoked on them by any other object that references an them. The Register() method increments the instances’ reference count. When the reference to the instance disappears, a Delete() method is invoked on the instance that decrements the reference count—this is equivalent to an UnRegister() method. When the reference count returns to zero, the instance is destroyed.

This protocol is greatly simplified by using a helper class called a itk::SmartPointer. The smart pointer acts like a regular pointer (e.g. supports operators -> and *) but automagically performs a Register() when referring to an instance, and an UnRegister() when it no longer points to the instance. Unlike most other instances in OTB, SmartPointers can be allocated on the program stack, and are automatically deleted when the scope that the SmartPointer was created is closed. As a result, you should rarely if ever call Register() or Delete() in OTB. For example:

void MyRegistrationFunction()
{ // Start of scope
  // here an interpolator is created and associated to the
  // SmartPointer "interp".
  InterpolatorType::Pointer interp = InterpolatorType::New();
} // End of scope

In this example, reference counted objects are created (with the New() method) with a reference count of one. Assignment to the SmartPointer interp does not change the reference count. At the end of scope, interp is destroyed, the reference count of the actual interpolator object (referred to by interp) is decremented, and if it reaches zero, then the interpolator is also destroyed.

Note that in ITK SmartPointers are always used to refer to instances of classes derived from itk::LightObject. Method invocations and function calls often return “real” pointers to instances, but they are immediately assigned to a SmartPointer. Raw pointers are used for non-LightObject classes when the need for speed and/or memory demands a smaller, faster class.

Data Representation

otb::Image represents an n-dimensional, regular sampling of data. The sampling direction is parallel to each of the coordinate axes, and the origin of the sampling, inter-pixel spacing, and the number of samples in each direction (i.e., image dimension) can be specified. The sample, or pixel, type in OTB is arbitrary—a template parameter TPixel specifies the type upon template instantiation. (The dimensionality of the image must also be specified when the image class is instantiated.) The key is that the pixel type must support certain operations (for example, addition or difference) if the code is to compile in all cases (for example, to be processed by a particular filter that uses these operations). In practice the OTB user will use a C++ simple type (e.g., int, float) or a pre-defined pixel type and will rarely create a new type of pixel class.

One of the important ITK concepts regarding images is that rectangular, continuous pieces of the image are known as regions. Regions are used to specify which part of an image to process, for example in multithreading, or which part to hold in memory. In ITK there are three common types of regions:

  1. LargestPossibleRegion —the image in its entirety.

  2. BufferedRegion —the portion of the image retained in memory.

  3. RequestedRegion —the portion of the region requested by a filter or other class when operating on the image.

The otb::Image class extends the functionalities of the itk::Image in order to take into account particular remote sensing features as geographical projections, etc.

Data Processing Pipeline

While data objects (e.g., images) are used to represent data, process objects are classes that operate on data objects and may produce new data objects. Process objects are classed as sources, filter objects, or mappers. Sources (such as readers) produce data, filter objects take in data and process it to produce new data, and mappers accept data for output either to a file or some other system. Sometimes the term filter is used broadly to refer to all three types.

The data processing pipeline ties together data objects (e.g., images) and process objects. The pipeline supports an automatic updating mechanism that causes a filter to execute if and only if its input or its internal state changes. Further, the data pipeline supports streaming, the ability to automatically break data into smaller pieces, process the pieces one by one, and reassemble the processed data into a final result.

Typically data objects and process objects are connected together using the SetInput() and GetOutput() methods as follows:

typedef otb::Image<float,2> FloatImage2DType;

itk::RandomImageSource<FloatImage2DType>::Pointer random;
random = itk::RandomImageSource<FloatImage2DType>::New();

itk::ShrinkImageFilter<FloatImage2DType,FloatImage2DType>::Pointer shrink;
shrink = itk::ShrinkImageFilter<FloatImage2DType,FloatImage2DType>::New();

otb::ImageFileWriter::Pointer<FloatImage2DType> writer;
writer = otb::ImageFileWriter::Pointer<FloatImage2DType>::New();
writer->SetInput (shrink->GetOutput());

In this example the source object itk::RandomImageSource is connected to the itk::ShrinkImageFilter, and the shrink filter is connected to the mapper otb::ImageFileWriter. When the Update() method is invoked on the writer, the data processing pipeline causes each of these filters in order, culminating in writing the final data to a file on disk.