MLFA-Net: multi-level feature-aggregated network for semantic change detection in remote sensing images-Reference-Cited by-同舟云学术

MLFA-Net: multi-level feature-aggregated network for semantic change detection in remote sensing images

Published:2024-09-09 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Ding Qing¹,Shao Zhenfeng²,Huang Xiao³,Wang Fengyan¹,Wang Mingchang¹

Affiliation:

1. College of Geo-Exploration Science and Technology, Jilin University, Changchun, People’s Republic of China

2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, People’s Republic of China

3. Department of Environmental Sciences, Emory University, Atlanta, GA, USA

Funder

National Key Research and Development Program of China

Jilin Province Youth Talent Support Project

National Natural Science Foundation of China

Shanxi Provincial Science and Technology Major Special Project

Hubei Province Key R&D Project

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2398070

Reference58 articles.

1. A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images

2. An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images

3. TEMDnet: A Novel Deep Denoising Network for Transient Electromagnetic Signal With Signal-to-Image Transformation

4. ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model;Chen H.;IEEE Transactions on Geoscience and Remote Sensing,2024

5. Exchange means change: An unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange