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Machine Learning For Remote Sensing : Orfeo ToolBox Meets OpenCV

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and the and the app Irit like to bring the British described very but as something that is coming next with the at data which other I'd resolution data in 0 points out 4 of the 5 metre resolution is the river to the city's changed because because of that it originated today so instead we can do Object-based justification which means we are justified the the region's segmented for from segmentation of the case and is can capture more information like statistics all shape all of it enabled us to get that kind of thing about the application to the from and the exact matchday segmentation very large image so it shouldn't to to of this England facial as a should find so you enter the majority of distinguish was issued by the best or most no size limitations because they images of the produced and we have the Victoria justification application which is basically the same as what officials are shown here but for the Little data who because they put with down to get with the same tourism and the same the same Walker look for a kick in the case that said so it was an
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so this is quite from using and we come in the next 3 days may be and a flight to Boston and its so
tax and figure and the last time but is but this is everything you need to know that I'd just
jumped to this year as a result is still best thought is with websites and if you want to close for the year we ball in the air and
just to take a message to know we are the best them and order messianically under development simulating extensive more for we provides are decide task and providing took place that the reason to upset the teenagers and you can read this tourist industry is the saviour of the application to which in turn can be removed from 1 to the other 2 are changes in the way to talk about the case but we see a talk that and coming next to the direct daughter of the rest of the city dishing to and tourist office implementation of the road rapturous thank you so I
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Metadaten

Formale Metadaten

Titel Machine Learning For Remote Sensing : Orfeo ToolBox Meets OpenCV
Serientitel FOSS4G Nottingham 2013
Autor Michel, Julien (CNES (DCT/SI/AP))
Jaen , Arnaud (CS SI)
Grizonnet, Manuel (CNES (DCT/SI/AP))
Lizenz CC-Namensnennung - keine kommerzielle Nutzung - Weitergabe unter gleichen Bedingungen 3.0 Unported:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben
DOI 10.5446/15551
Herausgeber FOSS4G, Open Source Geospatial Foundation (OSGeo)
Erscheinungsjahr 2013
Sprache Englisch
Produktionsort Nottingham

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract Orfeo ToolBox is an open-source library developed by CNES in the frame of the Orfeo program since 2006, which aimed at preparing institutional and scientific users to the use of the Very High Resolution optical imagery delivered by the Pleiades satellites. It is written in C++ on top of ITK, a medical imagery toolkit, and relies on many other open-source libraries such as GDAL or OSSIM. The OTB aims at providing generic means of pre-processing and information extraction from optical satellites imagery. In this talk, we will focus on recent advances in the machine learning functionality allowing to use the full extent of OpenCV algorithms. Historically, supervised classification of satellite images with OTB mainly relies on libSVM. The Orfeo ToolBox provides tools to train the SVM algorithm from images and raster or vector training areas, to use a trained SVM algorithm to classify satellite images of arbitrary size in a multithreaded way, and to estimate the accuracy of the classification. The SVM algorithm has also been used for other applications such as change detection or object detection. But even if it is one of the most used function of the OTB, the supervised classification function did not offer a single alternative to the SVM algorithm. However, the open-source world offers plenty of implementations of state-of-the-art machine learning algorithms. For instance OpenCV, a computer vision C++ library distributed under the BSD licence, includes a statistical machine learning module that contains no less than height different algorithms (including SVM). We therefore created an API to represent a generic machine learning algorithm. This API can then be specialized to encapsulate a given algorithm implementation. The machine learning algorithm API assumes very few properties for such algorithms. A method has to be specialized to train the algorithm from a samples vector and a set of target labels or values, and another to predict labels or values from a samples vector. Thanks to templating, these methods handle both classification and regression. Two other methods are in charge of saving and loading back the parameters from training. File format for saving is left to the underlying implementation, and the load method is expected to return a success flag. This success flag is used in a factory pattern, designed to be able to seamlessly instantiate the appropriate machine learning algorithm specialization upon file reading. It is therefore not necessary to know which algorithms the trained parameters files refer to. This new set of classes has been embedded into a new OTB application. Its purpose is to train one of the machine learning algorithm from a set of images and GIS file describing training areas, and output the trained parameters file. Another application is in charge of reading back this file and applying the classification algorithm to a given image. With these two tools, it is very easy to train different algorithms against the same dataset, evaluate them with the help of another application which can compute confusion matrix and classification performances measurement so as to choose one or several best algorithm along with their parameters. The resulting classification maps could then be combined into a more robust one using yet another OTB application, using classes majority voting or Dempster-Shafer combination. Our perspectives for using and improving this new API are manyfold. First, we would like to investigate further the use of the regression mode. We also would like to investigate the performances of the new machine learning algorithms for other tasks achievable with OTB, such as object detection for instance. Last, we would like to evolve the API so as to export any confidence or quality indices an algorithm can output regarding its predictions. This would open the way to the implementation of new active learning tools.

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