Free lunch for federated remote sensing target fine-grained classification: A parameter-efficient framework

Author:

Chen ShengchaoORCID,Shu TingORCID,Zhao HuanORCID,Wang Jiahao,Ren Sufen,Yang Lina

Funder

Guangdong Provincial Natural Science Foundation

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference69 articles.

1. Remote sensing;Di,2023

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3. MHA-CNN: Aircraft fine-grained recognition of remote sensing image based on multiple hierarchies attention;Yi,2022

4. EFM-Net: An essential feature mining network for target fine-grained classification in optical remote sensing images;Yi;IEEE Trans. Geosci. Remote Sens.,2023

5. Fine-grained ship detection in high-resolution satellite images with shape-aware feature learning;Guo;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2023

Cited by 2 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. Advancing high-resolution remote sensing: a compact and powerful approach to semantic segmentation;International Journal of Remote Sensing;2024-09-08

2. Transformer-Based Federated Learning For Multi-Label Remote Sensing Image Classification;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

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