Advancing high-resolution remote sensing: a compact and powerful approach to semantic segmentation-Reference-Cited by-同舟云学术

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Advancing high-resolution remote sensing: a compact and powerful approach to semantic segmentation

Published:2024-09-08 Issue:18 Volume:45 Page:6860-6883
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Zhang Hua¹²³^ORCID,Jiang Zhengang¹^ORCID,Xu Jun³^ORCID,Pan Xin²³^ORCID

Affiliation:

1. School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China

2. School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun, China

3. Jilin Key Laboratory of Changbai Historical Culture and VR Technology Reconstruction, Changchun Institute of Technology, Changchun, China

Funder

Jilin Province Science and Technology Development Plan Project

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2024.2398226

Reference58 articles.

1. VNet: An End-to-End Fully Convolutional Neural Network for Road Extraction From High-Resolution Remote Sensing Data

2. Remote sensing and GIS techniques for reconstructing the military fort system on the Roman boundary (Tunisian section) and identifying archaeological sites

3. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

4. Transformer-Based Masked Autoencoder With Contrastive Loss for Hyperspectral Image Classification

5. MSF-Net: A Multiscale Supervised Fusion Network for Building Change Detection in High-Resolution Remote Sensing Images

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