The differential SAR interferometry (DInSAR) technique relies on the processing of two SAR images of the same portion of the Earth’s surface taken at different time. The aim is to analyze potential events (earthquake, destruction, …) by highlighting differences between SAR images. DInSAR involves a set of tools such as creation of deformation grids , coregistration or building of interferograms.
The Orfeo Toolbox remote module DiapOTB contains all necessary steps and allows to launch a complete DInSAR chain. This module has been used with Sentinel-1 data with satisfactory results.
This module implements a method to perform a fast forward feature selection using a Gaussian Mixture Model for the classification of high dimensional remote sensing images. The algorithm is describes in the following paper https://hal.archives-ouvertes.fr/hal-01382500.
This module provides the GRM OTB application to perform multi-scale region-merging segmentation on satellite images. Three local homogeneity criteria are available: the Baatz & Schäpe criterion, the Full Lambda Schedule criterion and the simple Euclidean Distance criterion. This module was contributed by Pierre Lassalle who also provides a tutorial to learn how to use the library.
GKSVM contains a modified version of libsvm to support generic kernels, as well as a set of Orfeo ToolBox classes to use them. This code once belonged to Orfeo ToolBox source code, but has been removed to enforce the new third party policy (no modified third party in source code). Code has been turned into a legacy remote module.
This remote module of the Orfeo ToolBox provides a generic, multi purpose deep learning framework, targeting remote sensing images processing. It contains a set of new process objects that internally invoke Tensorflow, and a bunch of user-oriented applications to perform deep learning with real-world remote sensing images. Applications can be used to build OTB pipelines from Python or C++ APIs.