@inproceedings{3a25437938674a7dbbcfb8a1f6ec851f,
title = "MULTITEMPORAL CHANGE TYPE IDENTIFICATION IN COASTAL ZONE BASED ON SFANET AND LSTM",
abstract = "Coastal zone has become an important area for the study of global change. Remote sensing technology has been widely used in coastal zone monitoring due to its numerous advantages. Therefore, in this paper, Chinese GaoFen 1 Wide Field of View (GF1WFV) multitemporal data are used owe to its short revisiting period. Multitemporal change type identification in costal zone is achieved by firstly extracting the slowly changing features through slow feature analysis network (SFANet) and then identifying types of change with some category labels through long short-term memory (LSTM) network. Experimental results have shown the effectiveness of SFANet feature and LSTM. This study can provide technical feasible scheme and theoretical support for the analysis of coastal zone land change.",
keywords = "change type identification, GF-1 satellite, LSTM, Multitemporal remote sensing images, SFA",
author = "Tianzhu Liu and Min Yang and Meiling Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",
year = "2021",
doi = "10.1109/IGARSS47720.2021.9555070",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6163--6166",
booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "美国",
}