Texture-Aware Self-Attention Model for Hyperspectral Tree Species Classification-Reference-Cited by-同舟云学术

Texture-Aware Self-Attention Model for Hyperspectral Tree Species Classification

Published:2024 Issue: Volume:62 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Li Nanying¹^ORCID,Jiang Shuguo¹,Xue Jiaqi¹,Ye Songxin¹,Jia Sen¹^ORCID

Affiliation:

1. College of Computer Science and Software Engineering, Guangdong–Hong Kong–Macau Joint Laboratory for Smart Cities, Key Laboratory for Geo-Environmental Monitoring of Coastal Zone, Ministry of Natural Resources, Shenzhen University, Shenzhen, China

Funder

National Natural Science Foundation of China

Project of Department of Education of Guangdong Province

Basic and Applied Basic Research Foundation of Guangdong Province

Shenzhen Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10366290.pdf?arnumber=10366290

Reference61 articles.

1. China's carbon neutrality faces the challenges of “three highs and one short”, and requires “five carbon implementations” to achieve dual carbon goals

2. Understandings of carbon peaking, carbon neutrality, and energy development strategy of China

3. Challenges of developing a power system with a high renewable energy proportion under China's carbon targets

4. Shedding light on relationships between plant diversity and tropical forest ecosystem services across spatial scales and plot sizes

5. Trends in global research in forest carbon sequestration: A bibliometric analysis

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