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
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Zusammenfassung: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.
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ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2016.2636227