期刊
APPLIED SCIENCES-BASEL
卷 8, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/app8101785
关键词
change detection; convolutional network; deep learning; panchromatic; remote sensing
类别
资金
- MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program [IITP-2018-2016-0-00288]
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Science, ICT & Future Planning [NRF-2018R1A2B2008238]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2016-0-00288-003] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2018R1A2B2008238] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper presents a robust change detection algorithm for high-resolution panchromatic imagery using a proposed dual-dense convolutional network (DCN). In this work, a joint structure of two deep convolutional networks with dense connectivity in convolution layers is designed in order to accomplish change detection for satellite images acquired at different times. The proposed network model detects pixel-wise temporal change based on local characteristics by incorporating information from neighboring pixels. Dense connection in convolution layers is designed to reuse preceding feature maps by connecting them to all subsequent layers. Dual networks are incorporated by measuring the dissimilarity of two temporal images. In the proposed algorithm for change detection, a contrastive loss function is used in a learning stage by running over multiple pairs of samples. According to our evaluation, we found that the proposed framework achieves better detection performance than conventional algorithms, in area under the curve (AUC) of 0.97, percentage correct classification (PCC) of 99%, and Kappa of 69, on average.
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