Land Cover Classification From RGB and NIR Satellite Images Using Modified U-Net Model-Reference-Cited by-同舟云学术

Land Cover Classification From RGB and NIR Satellite Images Using Modified U-Net Model

Published:2024 Issue: Volume:12 Page:69445-69455
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Baek Won-Kyung¹,Lee Moung-Jin²,Jung Hyung-Sup³^ORCID

Affiliation:

1. Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology, Busan, South Korea

2. Division for Environmental Planning, Korea Environment Institute, Sejong, South Korea

3. Department of Geoinformatics, University of Seoul, Seoul, South Korea

Funder

1) the Institute of Civil Military Technology Cooperation, the Defense Acquisition Program Administration, and the Ministry of Trade, Industry and Energy of Korea

2)the National Research Foundation of Korea(NRF) grant funded by the Korea Government

Korea Institute of Marine Science & Technology (KIMST), backed by the Ministry of Oceans and Fisheries

Development of Optimization Techniques for Reducing Heat Wave Considering Urban Environment

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10530973.pdf?arnumber=10530973

Reference35 articles.

1. Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

2. Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

3. Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches

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