Toward Efficient Hyperspectral Anomaly Detection With Subspace Transformation Learning-Reference-Cited by-同舟云学术

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Toward Efficient Hyperspectral Anomaly Detection With Subspace Transformation Learning

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:

Li Qian¹,Wang Changbo²,Yu Laihang¹^ORCID,Zhang Jian³^ORCID,Zhang Li³,Liu Kai⁴,Shen Xiangfei⁵^ORCID

Affiliation:

1. School of Computer Science and Technology, Zhoukou Normal University, Zhoukou, Henan, China

2. Online Teaching Center, Zhengzhou College of Finance and Economics, Zhengzhou, Henan, China

3. Department of Computer Science and Technology, Lvliang University, Lvliang, Shanxi, China

4. School of Software, Chongqing Finance and Economics College, Chongqing, China

5. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

Funder

Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi

University Key Scientific Research Project of Henan Province

Higher Education Teaching Reform Research and Practice Project of Henan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10538265.pdf?arnumber=10538265

Reference25 articles.

1. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

2. Hyperspectral Remote Sensing Data Analysis and Future Challenges

3. FFN: Fountain Fusion Net for Arbitrary-Oriented Object Detection

4. Siamese Transformer Network for Hyperspectral Image Target Detection

5. Hyperspectral Anomaly Detection Based on Machine Learning: An Overview

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