Fast semantic segmentation for remote sensing images with an improved Short-Term Dense-Connection (STDC) network-Reference-Cited by-同舟云学术

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Fast semantic segmentation for remote sensing images with an improved Short-Term Dense-Connection (STDC) network

Published:2024-06-03 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Liu Mengjia¹²,Liu Peng¹,Zhao Lingjun¹,Ma Yan¹^ORCID,Chen Lajiao¹,Xu Mengzhen³

Affiliation:

1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of China

2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People’s Republic of China

3. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, People’s Republic of China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2356122

Reference61 articles.

1. Al-Amri Salem Saleh Namdeo V Kalyankar and others. 2010. “Image Segmentation by Using Threshold Techniques.” arXiv Preprint arXiv:1005.4020.

2. Gcnet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond;Cao Yue;In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops,2019

3. Adaptive image region-growing

4. Contributors MMSegmentation. 2020. “MMSegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark.” https://github.com/open-mmlab/mmsegmentation.

5. NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm

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