Personal Information
- Doctoral Supervisor
- Master Tutor
Gender:
MaleDiscipline:
Photogrammetry and Remote SensingSchool/Department:
测绘遥感信息工程国家重点实验室Education Level:
With Certificate of Graduation for Doctorate StudySchool/Department:
测绘遥感信息工程国家重点实验室Discipline:
1 Photogrammetry and Remote Sensing
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Other Contact Information
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Paper Publications
Current position: Home > Scientific Research > Paper Publications
Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network
- Time:2021-05-11
- Hits:
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Journal:
IEEE Transactions on Geoscience and Remote Sensing -
Key Words:
Feature extraction , Radiometry , Recurrent neural networks , Sensors , Data mining , Remote sensing -
Abstract:
With the rapid development of Earth observation technology, very-high-resolution (VHR) images from various satellite sensors are more available, which greatly enrich the data source of change detection (CD). Multisource multitemporal images can provide abundant information on observed landscapes with various physical and material views, and it is exigent to develop efficient techniques to utilize these multisource data for CD. In this article, we propose a novel and general deep siamese convolutional multiple-layers recurrent neural network (RNN) (SiamCRNN) for CD in multitemporal VHR images. -
Co-author:
Bo Du,Liangpei Zhang,Le Wang -
Indexed by:
Journal paper -
Correspondence Author:
Chen Wu -
Document Type:
J -
Page Number:
2848 - 2864 -
ISSN No.:
0196-2892 -
Translation or Not:
no -
Date of Publication:
2019-12-20 -
Included Journals:
SCI -
Links to published journals:
https://ieeexplore.ieee.org/document/8937755