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12-07-2016 | Methodologies and Application

SVM or deep learning? A comparative study on remote sensing image classification

Authors: Peng Liu, Kim-Kwang Raymond Choo, Lizhe Wang, Fang Huang

Published in: Soft Computing | Issue 23/2017

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Abstract

With constant advancements in remote sensing technologies resulting in higher image resolution, there is a corresponding need to be able to mine useful data and information from remote sensing images. In this paper, we study auto-encoder (SAE) and support vector machine (SVM), and to examine their sensitivity, we include additional umber of training samples using the active learning frame. We then conduct a comparative evaluation. When classifying remote sensing images, SVM can also perform better than SAE in some circumstances, and active learning schemes can be used to achieve high classification accuracy in both methods.

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Title: SVM or deep learning? A comparative study on remote sensing image classification
Authors: Peng Liu
Kim-Kwang Raymond Choo
Lizhe Wang
Fang Huang
Publication date: 12-07-2016
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 23/2017
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2247-2

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