A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 29; H. 3; S. 545 - 559
Hauptverfasser:	Liu, Jia, Gong, Maoguo, Qin, Kai, Zhang, Puzhao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Change detection Coupling Couplings deep neural network denoising autoencoder optical images Feature extraction Image acquisition Image detection Laser radar Neural networks Optical imaging Optical measuring instruments Optical properties Optical sensors Radar Radar imaging Sensors synthetic aperture radar images
ISSN:	2162-237X, 2162-2388
Online-Zugang:	Volltext
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