Let the loss impartial: a hierarchical unbiased loss for small object segmentation in high-resolution remote sensing images-Reference-Cited by-同舟云学术

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Let the loss impartial: a hierarchical unbiased loss for small object segmentation in high-resolution remote sensing images

Published:2023-09-05 Issue:1 Volume:56 Page:
ISSN:2279-7254
Container-title:European Journal of Remote Sensing
language:en
Short-container-title:European Journal of Remote Sensing

Author:

Chong Qianpeng¹,Ni Mengying¹,Huang Jianjun¹,Wei Guangyi¹,Li Ziyi¹,Xu Jindong¹

Affiliation:

1. School of Computer and Control Engineering, Yantai University, Yantai, China

Funder

National Natural Science Foundation of China

the Graduate Science and Technology Innovation Fund Project of Yantai University

Publisher

Informa UK Limited

Subject

Applied Mathematics,Atmospheric Science,Computers in Earth Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2023.2254473

Reference47 articles.

1. RoadVecNet: a new approach for simultaneous road network segmentation and vectorization from aerial and google earth imagery in a complex urban set-up

2. UCL: Unsupervised Curriculum Learning for water body classification from remote sensing imagery

3. Programmable Hierarchical Kirigami

4. Statistically validated hierarchical clustering: Nested partitions in hierarchical trees

5. MANet: a multi-level aggregation network for semantic segmentation of high-resolution remote sensing images

Cited by 1 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. Pos-DANet: A dual-branch awareness network for small object segmentation within high-resolution remote sensing images;Engineering Applications of Artificial Intelligence;2024-07

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