In this article, we proposed a novel method based on deep learning shape priors for object extraction in high-resolution (HR) remote-sensing images. Specifically, the deep Boltzmann machines (DBMs) are applied to model the shape priors via the unsupervised training process, which qualify
for the advantages of deep learning method, especially the powerful feature learning and modelling ability. The deep shape model is integrated into a new energy function to eliminate the influence of disturbing background. The energy function combines image appearance information and region
information. A new region term in the function is proposed to eliminate the influence of object shadow. The process of object extraction is achieved by minimizing the energy function with an iterative optimization algorithm and the Split Bregman method is applied to derive a global solution
during the minimization process. Quantitative and qualitative experiments are conducted on the aircraft data set acquired by QuickBird with 60 cm resolution and the results demonstrate the effectiveness of the proposed method.
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Document Type: Research Article
College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China
Key Laboratory of Spatial Information Processing and Application System Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
Publication date: December 16, 2016
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