Journal:IEEE Transactions on Geoscience and Remote Sensing
Abstract: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
All the Authors:Daifeng Peng,Lorenzo Bruzzone,Haiyan Guan,Yongjun Zhang,Haiyong Ding
First Author:Daifeng Peng
Indexed by:Journal paper
Correspondence Author:Daifeng Peng
Volume:59
Issue:7
Page Number:5891-5906
Translation or Not:no
Date of Publication:2020-07-21
Included Journals:SCI
