IRU-Net: An Efficient End-to-End Network for Automatic Building Extraction From Remote Sensing Images-Reference-Cited by-同舟云学术

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IRU-Net: An Efficient End-to-End Network for Automatic Building Extraction From Remote Sensing Images

Published:2022 Issue: Volume:10 Page:37811-37828
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Sheikh Md. Abdul Alim¹^ORCID,Maity Tanmoy²^ORCID,Kole Alok³

Affiliation:

1. Department of Electronics and Communication Engineering, Aliah University, Kolkata, India

2. Department of Mining Machinery Engineering, Indian Institute of Technology (ISM), Dhanbad, Dhanbad, India

3. Department of Electrical Engineering, RCC Institute of Information Technology, Kolkata, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09748124.pdf?arnumber=9748124

Reference83 articles.

1. EU-Net: An Efficient Fully Convolutional Network for Building Extraction from Optical Remote Sensing Images

2. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

3. MobileNets: Efficient convolutional neural networks for mobile vision applications;howard;arXiv 1704 04861,2017

4. Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation

5. Deep-ReID: deep features and autoencoder assisted image patching strategy for person re-identification in smart cities surveillance

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1. Research on building extraction from remote sensing imagery using efficient lightweight residual network;PeerJ Computer Science;2024-05-02

2. Transformer-based semantic segmentation for large-scale building footprint extraction from very-high resolution satellite images;Advances in Space Research;2024-05

3. Extraction of building from remote sensing imagery base on multi-attention L-CAFSFM and MFFM;Frontiers in Earth Science;2023-10-19

4. Scattered Mountainous Area Building Extraction From an Open Satellite Imagery Dataset;IEEE Geoscience and Remote Sensing Letters;2023

5. MSRF-Net: Multiscale Receptive Field Network for Building Detection From Remote Sensing Images;IEEE Transactions on Geoscience and Remote Sensing;2023

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