Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer

IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1973–1976

Abstract

Support Vector Machine (SVM) is a popular supervised Machine Learning (ML) method that is widely used for classification and regression problems. Recently, a method to train SVMs on a D-Wave 2000Q Quantum Annealer (QA) was proposed for binary classification of some biological data. First, ensembles of weak quantum SVMs are generated by training each classifier on a disjoint training subset that can be fit into the QA. Then, the computed weak solutions are fused for making predictions on unseen data. In this work, the classification of Remote Sensing (RS) multispectral images with SVMs trained on a QA is discussed. Furthermore, an open code repository is released to facilitate an early entry into the practical application of QA, a new disruptive compute technology.

Authors 5

1. Gabriele Cavallaro

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre, Forschungszentrum Jülich,Germany

2. Dennis Willsch Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Aachen,Germany,D-52056

3. Madita Willsch Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Aachen,Germany,D-52056

4. Kristel F. L. Michielsen Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Aachen,Germany,D-52056

5. Morris Riedel

   University of Iceland

   Affiliation as printed

   School of Engineering and Natural Sciences, University of Iceland,Iceland

   School of Engineering and Natural Sciences, University of Iceland, Iceland