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

Liu, J, Gong, M, Qin, K and Zhang, P 2018, 'A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 3, pp. 545-559.


Document type: Journal Article
Collection: Journal Articles

Title A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images
Author(s) Liu, J
Gong, M
Qin, K
Zhang, P
Year 2018
Journal name IEEE Transactions on Neural Networks and Learning Systems
Volume number 29
Issue number 3
Start page 545
End page 559
Total pages 15
Publisher IEEE
Abstract 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 radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches. © 2016 IEEE.
Subject Neurosciences not elsewhere classified
Keyword(s) Change detection
deep neural network
denoising autoencoder optical images
synthetic aperture radar images
DOI - identifier 10.1109/TNNLS.2016.2636227
Copyright notice © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
ISSN 2162-237X
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