Automatic Urban Scene-Level Binary Change Detection Based on a Novel Sample Selection Approach and Advanced Triplet Neural Network-Reference-Cited by-同舟云学术

Automatic Urban Scene-Level Binary Change Detection Based on a Novel Sample Selection Approach and Advanced Triplet Neural Network

Published:2023 Issue: Volume:61 Page:1-18
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Fang Hong¹^ORCID,Guo Shanchuan¹,Wang Xin²^ORCID,Liu Sicong³^ORCID,Lin Cong⁴^ORCID,Du Peijun¹^ORCID

Affiliation:

1. School of Geography and Ocean Science, the Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, the Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, and the Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing University, Nanjing, China

2. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China

3. College of Surveying and Geoinformatics, Tongji University, Shanghai, China

4. Nanjing Research Institute of Surveying, Mapping and Geotechnical Investigation Company Ltd., Nanjing, China

Funder

National Natural Science Foundation of China

Special Fund of Natural Resources Development

Research Project of Nanjing Research Institute of Surveying, Mapping and Geotechnical Investigation Company Ltd.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10013781.pdf?arnumber=10013781

Reference83 articles.

1. Receptive Field Block Net for Accurate and Fast Object Detection

2. Pyramid Scene Parsing Network

3. Going deeper with convolutions

4. Multi-Scale Dense Networks for Hyperspectral Remote Sensing Image Classification

5. ADS-Net:An Attention-Based deeply supervised network for remote sensing image change detection

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Change detection of multisource remote sensing images: a review;International Journal of Digital Earth;2024-09-09

2. A context-structural feature decoupling change detection network for detecting earthquake-triggered damage;International Journal of Applied Earth Observation and Geoinformation;2024-07

3. Unsupervised Change Detection in HR Remote Sensing Imagery Based on Local Histogram Similarity and Progressive Otsu;Remote Sensing;2024-04-12

4. Accurate vegetation destruction detection using remote sensing imagery based on the three-band difference vegetation index (TBDVI) and dual-temporal detection method;International Journal of Applied Earth Observation and Geoinformation;2024-03

5. Large-Scale High-Altitude UAV-Based Vehicle Detection via Pyramid Dual Pooling Attention Path Aggregation Network;IEEE Transactions on Intelligent Transportation Systems;2024