Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery-Reference-Cited by-同舟云学术

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Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Zheng Zhuo¹,Ma Ailong¹,Zhang Liangpei¹,Zhong Yanfei¹

Affiliation:

1. Wuhan University,Wuhan,China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09709950.pdf?arnumber=9709950

Reference30 articles.

1. Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

2. Damage detection from aerial images via convolutional neural networks

3. xbd: A dataset for assessing building damage from satellite imagery;gupta,2019

5. Deep Residual Learning for Image Recognition

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1. Unifying remote sensing change detection via deep probabilistic change models: From principles, models to applications;ISPRS Journal of Photogrammetry and Remote Sensing;2024-09

2. TMLNet: Triad Multitask Learning Network for multiobjective based change detection;Neurocomputing;2024-09

3. Semantic-aware transformer with feature integration for remote sensing change detection;Engineering Applications of Artificial Intelligence;2024-09

4. Unsupervised change detection based on image reconstruction loss with segment anything;Remote Sensing Letters;2024-08-18

5. PRX-Change: Enhancing remote sensing change detection through progressive feature refinement and Cross-Attention interaction;International Journal of Applied Earth Observation and Geoinformation;2024-08

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