FENet: feature enhancement network for land cover classification-Reference-Cited by-同舟云学术

FENet: feature enhancement network for land cover classification

Published:2023-03-04 Issue:5 Volume:44 Page:1702-1725
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Ma Zhanming¹,Xia Min¹^ORCID,Lin Haifeng²,Qian Ming³,Zhang Yonghong¹

Affiliation:

1. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China

2. College of Information Science and Technology Nanjing, Nanjing Forestry University, Nanjing, China

3. State Key Laboratory LIESMARS, Wuhan University, Wuhan, China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2190471

Reference53 articles.

1. Semantic Unsupervised Change Detection of Natural Land Cover With Multitemporal Object-Based Analysis on SAR Images

2. Boguszewski, A., D. Batorski, N. Ziemba-Jankowska, A. Zambrzycka, and T. Dziedzic. 2020. “LandCover. Ai: Dataset for Automatic Mapping of Buildings, Woodlands and Water from Aerial Imagery.” arXiv preprint arXiv:2005.02264.

3. A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 Chinese cities

4. LinkNet: Exploiting encoder representations for efficient semantic segmentation

5. Chen, L.C., G. Papandreou, F. Schroff, and H. Adam. 2017. “Rethinking Atrous Convolution for Semantic Image Segmentation.” arXiv preprint arXiv:1706.05587. https://arxiv.org/abs/1706.05587v3

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