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发表刊物：IEEE Transactions on Geoscience and Remote Sensing

摘要：Change detection (CD) is one of the main applications of remote sensing. With the increasing popularity of
deep learning, most recent developments of CD methods have
introduced the use of deep learning techniques to increase
the accuracy and automation level over traditional methods.
However, when using supervised CD methods, a large amount of
labeled data is needed to train deep convolutional networks with
millions of parameters. These labeled data are difficult to acquire
for CD tasks. To address this limitation, a novel semisupervised
convolutional network for CD (SemiCDNet) is proposed bas

全部作者：Daifeng Peng,Lorenzo Bruzzone,Haiyan Guan,Yongjun Zhang,Haiyong Ding

第一作者：Daifeng Peng

论文类型：期刊论文

通讯作者：Daifeng Peng

卷号：59

期号：7

页面范围：5891-5906

是否译文：否

发表时间：2020-07-21

收录刊物：SCI