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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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 29; číslo 3; s. 545 - 559
Hlavní autoři:	Liu, Jia, Gong, Maoguo, Qin, Kai, Zhang, Puzhao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.03.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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.
AbstractList	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.
Author	Zhang, Puzhao Gong, Maoguo Liu, Jia Qin, Kai
Author_xml	– sequence: 1 givenname: Jia surname: Liu fullname: Liu, Jia organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, China – sequence: 2 givenname: Maoguo orcidid: 0000-0002-0415-8556 surname: Gong fullname: Gong, Maoguo email: gong@ieee.org organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, China – sequence: 3 givenname: Kai surname: Qin fullname: Qin, Kai organization: School of Computer Science and Information Technology, RMIT University, Melbourne, VIC, Australia – sequence: 4 givenname: Puzhao surname: Zhang fullname: Zhang, Puzhao organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/28026789$$D View this record in MEDLINE/PubMed
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Snippet	We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on...
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SubjectTerms	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
Title	A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images
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