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

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 3; pp. 545 - 559
• Main Authors: Liu, Jia, Gong, Maoguo, Qin, Kai, Zhang, Puzhao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TNNLS.2016.2636227

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.