An efficient change detection method for disaster-affected buildings based on a lightweight residual block in high-resolution remote sensing images
Author:
Affiliation:
1. Software Engineering Technology Research Center, School of Information Engineering, Institute of Disaster Prevention, Hebei, China
Funder
National Natural Science Foundation of China
Science and Technology Innovation Program for Postgraduate students in IDP subsidized by Fundamental Research Funds for the Central Universities
Publisher
Informa UK Limited
Subject
General Earth and Planetary Sciences
Link
https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2214274
Reference59 articles.
1. Ayinde, B. O., and J. M. Zurada. 2018. “Building Efficient Convnets Using Redundant Feature Pruning.” arXiv preprint arXiv:1802.07653.
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3. Chen, S. A., A. Escay, C. Haberland, T. Schneider, V. Staneva, and Y. Choe. 2018. “Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery.” arXiv preprint arXiv:1812.05581.
4. Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images
5. A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection
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