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

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…

• NN
  National Natural Science Foundation of China

  Awards: 61422209, 61273317
• FR
  Fundamental Research Funds for the Central Universities

  Award: K5051202053
• SR
  Specialized Research Fund for the Doctoral Program of Higher Education of China

  Award: 20130203110011