A Nonoverlapping Sampling Approach With Peak Data Utilization for Hyperspectral Classification-Reference-Cited by-同舟云学术

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A Nonoverlapping Sampling Approach With Peak Data Utilization for Hyperspectral Classification

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Shi Yanzi¹^ORCID,Yin Yaping²,Song Xiangyu³^ORCID,Cui Hua⁴,Zhu Dan¹,Song Huansheng⁵

Affiliation:

1. School of Automobile, Chang’an University, Xi’an, China

2. PLA Naval Submarine Academy, Qingdao, China

3. New Network Research Division, Peng Cheng Laboratory, Shenzhen, China

4. School of Future Transportation, Chang’an University, Xi’an, China

5. School of Information Engineering, Chang’an University, Xi’an, China

Funder

Fundamental Research Funds for the Central Universities, CHD

Postdoctoral Research Project of Shaanxi Province

Postdoctoral Science Foundation

Natural Science Basic Research Program of Shaanxi

Xi’an Science and Technology Planning Project

National Nature Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10416224.pdf?arnumber=10416224

Reference19 articles.

1. Hyperspectral Time-Series Target Detection Based on Spectral Perception and Spatial–Temporal Tensor Decomposition

2. Feature Mutual Representation-Based Graph Domain Adaptive Network for Unsupervised Hyperspectral Change Detection

3. A Dual-Branch Detail Extraction Network for Hyperspectral Pansharpening

4. Hyperspectral Pansharpening With Adaptive Feature Modulation-Based Detail Injection Network

5. Spectral–Spatial Feature Tokenization Transformer for Hyperspectral Image Classification

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