Ship Detection in SAR Images Using Convolutional Variational Autoencoders

We propose an unsupervised framework for ship detection in SAR image data, based on anomaly detection. We first learn representations of the SAR images with a convolutional Variational Autoencoder. Aftwerwards, we perform anomaly detection based on those representations, with a clustering algorithm....

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Bibliographic Details
Published in:IEEE International Geoscience and Remote Sensing Symposium proceedings pp. 2503 - 2506
Main Authors: Ferreira, Nuno, Silveira, Margarida
Format: Conference Proceeding
Language:English
Published: IEEE 26.09.2020
Subjects:
Data models
Decoding
Image reconstruction
Marine vehicles
Radar polarimetry
ship detection
Synthetic aperture radar
Training
unsupervised learning
Variational autoencoders
ISSN:2153-7003
Online Access:Get full text
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Description
Summary:We propose an unsupervised framework for ship detection in SAR image data, based on anomaly detection. We first learn representations of the SAR images with a convolutional Variational Autoencoder. Aftwerwards, we perform anomaly detection based on those representations, with a clustering algorithm. Experimental results with real SAR data are provided to illustrate the performance of the proposed algorithm.
ISSN:2153-7003
DOI:10.1109/IGARSS39084.2020.9324389