A<sup>2</sup>-FPN for semantic segmentation of fine-resolution remotely sensed images-Reference-Cited by-同舟云学术

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A²-FPN for semantic segmentation of fine-resolution remotely sensed images

Published:2022-02-01 Issue:3 Volume:43 Page:1131-1155
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Li Rui¹^ORCID,Wang Libo¹^ORCID,Zhang Ce²³^ORCID,Duan Chenxi⁴^ORCID,Zheng Shunyi¹^ORCID

Affiliation:

1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

2. Lancaster Environment Centre, Lancaster University, Lancaster, UK

3. UK Centre for Ecology & Hydrology, Lancaster, UK

4. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2022.2030071

Reference43 articles.

1. Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks

2. Comprehensively analyzing optical and polarimetric SAR features for land-use/land-cover classification and urban vegetation extraction in highly-dense urban area

3. BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation

4. Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity Regularized Tensor Optimization

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