A Probabilistic Sample Boosting Approach With Adaptive Representation Coefficient Consistency for China Coastal Wetland Land Cover Classification Using GF-5 Hyperspectral Imagery-Reference-Cited by-同舟云学术

A Probabilistic Sample Boosting Approach With Adaptive Representation Coefficient Consistency for China Coastal Wetland Land Cover Classification Using GF-5 Hyperspectral Imagery

Published:2023 Issue: Volume:61 Page:1-18
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Lu Hongliang¹^ORCID,Su Hongjun¹^ORCID,Zheng Pan¹^ORCID,Gao Yihan¹^ORCID,Zheng Hengyi¹^ORCID,Sun Weiwei²^ORCID,Du Qian³^ORCID

Affiliation:

1. School of Earth Sciences and Engineering, Hohai University, Nanjing, China

2. Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, China

3. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Postgraduate Research and Practice Innovation Program of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10226228.pdf?arnumber=10226228

Reference60 articles.

1. InteractE: Improving Convolution-Based Knowledge Graph Embeddings by Increasing Feature Interactions

2. Unsupervised Hyperspectral Band Selection via Hybrid Graph Convolutional Network

3. An Unsupervised Domain Adaptation Method Towards Multi-Level Features and Decision Boundaries for Cross-Scene Hyperspectral Image Classification

4. Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

5. Cross-Scale Mixing Attention for Multisource Remote Sensing Data Fusion and Classification

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