Mining Hard Negative Samples for SAR-Optical Image Matching Using Generative Adversarial Networks

Mining Hard Negative Samples for SAR-Optical Image Matching Using Generative Adversarial Networks

In this paper, we propose a generative framework to produce similar yet novel samples for a specified image. We then propose the use of these images as hard-negatives samples, within the framework of hard-negative mining, in order to improve the performance of classification networks in applications...

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Bibliographic Details
Main Authors: Lloyd Haydn Hughes, Michael Schmitt, Xiao Xiang Zhu
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Remote Sensing
Subjects:
synthetic aperture radar
generative adversarial networks
data fusion
dataset augmentation
Online Access:http://www.mdpi.com/2072-4292/10/10/1552

Internet

http://www.mdpi.com/2072-4292/10/10/1552

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