An Object Fine-Grained Change Detection Method Based on Frequency Decoupling Interaction for High-Resolution Remote Sensing Images-Reference-Cited by-同舟云学术

An Object Fine-Grained Change Detection Method Based on Frequency Decoupling Interaction for High-Resolution Remote Sensing Images

Published:2024 Issue: Volume:62 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Tang Yingjie¹,Feng Shou¹^ORCID,Zhao Chunhui¹,Fan Yuanze¹,Shi Qian²^ORCID,Li Wei³^ORCID,Tao Ran³^ORCID

Affiliation:

1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

2. Guangdong Provincial Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China

3. School of Information and Electronics, Beijing Institute of Technology, Beijing, China

Funder

National Natural Science Foundation of China

Project Funded by China Postdoctoral Science Foundation

Heilongjiang Provincial Natural Science Foundation

Open Fund of State Key Laboratory of Remote Sensing Science

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10335719.pdf?arnumber=10335719

Reference59 articles.

1. SDMNet: A Deep-Supervised Dual Discriminative Metric Network for Change Detection in High-Resolution Remote Sensing Images

2. Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set

3. Semisupervised Adaptive Ladder Network for Remote Sensing Image Change Detection

4. An Unsupervised Domain Adaptation Method Towards Multi-Level Features and Decision Boundaries for Cross-Scene Hyperspectral Image Classification

5. Deep Siamese Multi-scale Convolutional Network for Change Detection in Multi-temporal VHR Images

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