Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

• Published in: IEEE transaction on neural networks and learning systems Vol. 27; no. 1; pp. 125 - 138
• Main Authors: Gong, Maoguo, Zhao, Jiaojiao, Liu, Jia, Miao, Qiguang, Jiao, Licheng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Change detection; Change detection algorithms; Deep learning; Guidelines; image change detection; Image detection; Joints; Learning; neural network; Neural networks; Noise; Radar imagery; Radar systems; Synthetic aperture radar; synthetic aperture radar (SAR); Training; Deep learning; synthetic aperture radar (SAR); image change detection; neural network
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2015.2435783

This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. The approach accomplishes the detection of the changed and unchanged areas by designing a deep neural network. The main guideline is to produce a change detection map directly from two images with the trained deep neural network. The method can omit the process of generating a difference image (DI) that shows difference degrees between multitemporal synthetic aperture radar images. Thus, it can avoid the effect of the DI on the change detection results. The learning algorithm for deep architectures includes unsupervised feature learning and supervised fine-tuning to complete classification. The unsupervised feature learning aims at learning the representation of the relationships between the two images. In addition, the supervised fine-tuning aims at learning the concepts of the changed and unchanged pixels. Experiments on real data sets and theoretical analysis indicate the advantages, feasibility, and potential of the proposed method. Moreover, based on the results achieved by various traditional algorithms, respectively, deep learning can further improve the detection performance.