T-UNet: triplet UNet for change detection in high-resolution remote sensing images-Reference-Cited by-同舟云学术

T-UNet: triplet UNet for change detection in high-resolution remote sensing images

Published:2024-04-17 Issue: Volume: Page:1-18
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Zhong Huan¹,Wu Chen¹

Affiliation:

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2024.2338224

Reference45 articles.

1. A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection

2. Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network

3. Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning

4. DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images

5. Daudt, R. C., B. Le Saux, and A. Boulch. 2018. “Fully Convolutional Siamese Networks for Change Detection.” In 25th IEEE International Conference on Image Processing (ICIP), Athens, GREECE, October 07-10.

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1. RFL-CDNet: Towards accurate change detection via richer feature learning;Pattern Recognition;2024-09

2. AFPF-Net: Adjacent-Level Feature Progressive Fusion Full Convolutional Network for Remote Sensing Change Detection;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

3. UNet-Like Remote Sensing Change Detection: A review of current models and research directions;IEEE Geoscience and Remote Sensing Magazine;2024

4. CBSASNet: A Siamese Network Based on Channel Bias Split Attention for Remote Sensing Change Detection;IEEE Transactions on Geoscience and Remote Sensing;2024