DGPF-RENet: A Low Data Dependence Network With Low Training Iterations for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

DGPF-RENet: A Low Data Dependence Network With Low Training Iterations for Hyperspectral Image Classification

Published:2023 Issue: Volume:61 Page:1-21
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zhan Jialei¹,Xie Yuhang¹,Guo Jiajia¹,Hu Yaowen¹,Zhou Guoxiong¹^ORCID,Cai Weiwei¹^ORCID,Wang Yanfeng²,Chen Aibin¹^ORCID,Xie Liu¹,Li Maopeng¹,Li Liujun³

Affiliation:

1. College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China

2. College of Systems Engineering, National University of Defense Technology, Changsha, China

3. Department of Soil and Water Systems, Precision Agriculture and Intelligent Robotics (PAIR) Laboratory, University of Idaho, Moscow, ID, USA

Funder

National Natural Science Foundation in China

Natural Science Foundation of Changsha

Natural Science Foundation of Hunan Province

Hunan Provincial Department of Education Research Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10225412.pdf?arnumber=10225412

Reference60 articles.

1. Classification of hyperspectral data from urban areas based on extended morphological profiles

2. Attention-Based Second-Order Pooling Network for Hyperspectral Image Classification

3. Hyperspectral Image Classification With Robust Sparse Representation

4. ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data

5. Probabilistic Fusion of Pixel-Level and Superpixel-Level Hyperspectral Image Classification

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