eCite Digital Repository

Synthetic aperture radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks

Citation

Schwegmann, CP and Kleynhans, W and Salmon, BP and Mdakane, LW and Meyer, RGV, Synthetic aperture radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks, Proceedings from the 2017 IEEE International Geoscience and Remote Sensing Symposium, 23-28 July 2017, Texas, United State, pp. 1-4. ISBN 9781509049516 (2017) [Refereed Conference Paper]


Preview		PDF
655Kb		  

Copyright Statement

Copyright 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Official URL: http://dx.doi.org/10.1109/IGARSS.2017.8127440

Abstract

A major task in any discrimination scenario requires the collection and validation of as many examples as possible. Depending on the type of data this can be a time consuming process, especially when dealing with large remote sensing data such as Synthetic Aperture Radar imagery. To aid in the creation of improved machine learning-based ship detection and discrimination methods this paper applies a type of neural network known as an Information Maximizing Generative Adversarial Network. Generative Adversarial Networks pit a generating and discriminating network against each other. A generator tries to create samples that are indistinguishable from real data whereas the discriminator tries to identify whether a sample is real or generated. Information Maximizing Generative Adversarial Network extend this idea by extracting untangled latent variables as part of the discrimination process which help to classify the data in terms of categories/classes and properties such as ship rotation. Despite the limited size and class distribution of the dataset, the paper showed that the trained network was able to generate convincing samples from the three given classes as well as create a discriminator that performs similarly to state-of-the-art ship discrimination methods despite using no labels for training.

Item Details

Item Type:Refereed Conference Paper
Keywords:synthetic aperture radar, machine learning, marine technology
Research Division:Engineering
Research Group:Communications engineering
Research Field:Signal processing
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Salmon, BP (Dr Brian Salmon)
ID Code:124430
Year Published:2017
Deposited By:Engineering
Deposited On:2018-02-21
Last Modified:2018-06-18
Downloads:114 View Download Statistics

Repository Staff Only: item control page