%0 Conference Paper
%A Memon, Mohammad Shahbaz
%A Cavallaro, Gabriele
%A Riedel, Morris
%A Neukirchen, Helmut
%T Facilitating efficient data analysis of remotely sensed images using standards-based parameter sweep models
%I IEEE
%M FZJ-2018-00537
%P 3680-3683
%D 2017
%< [Proceedings] - IEEE, 2017. - ISBN 978-1-5090-4951-6
%X Classification of remote sensing images often use Support Vector Machines (SVMs) that require an n-fold cross-validation phase in order to do model selection. This phase is characterized by sweeping through a wide set of parameter combinations of SVM kernel and cost parameters. As a consequence this process is computationally expensive but represents a principled way of tuning a model for better accuracy and to prevent overfitting together with regularization that is in SVMs inherently solved in the optimization. Since the cross-validation technique is done in a principled way also known as &#x2018;gridsearch&#x2019;, we aim at supporting remote sensing scientists in two ways. Firstly by reducing the time-to-solution of the cross-validation by applying state-of-the-art parallel processing methods because the sweep of parameters and cross-validation runs itself can be nicely parallelized. Secondly by reducing manual labour by automating the parallel submission processes since manually performing cross-validation is very time consuming, unintuitive, and error-prone especially in large-scale cluster or supercomputing environments (e.g., batch job scripts, node/core/task parameters, etc.).
%B 2017 IEEE International Geoscience and Remote Sensing Symposium
%C 23 Jul 2017 - 28 Jul 2017, Fort Worth, TX (USA)
Y2 23 Jul 2017 - 28 Jul 2017
M2 Fort Worth, TX, USA
%F PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
%9 Contribution to a conference proceedingsContribution to a book
%R 10.1109/IGARSS.2017.8127797
%U https://juser.fz-juelich.de/record/842290