An Attention-Based Multiscale Spectral–Spatial Network for Hyperspectral Target Detection-Reference-Cited by-同舟云学术

An Attention-Based Multiscale Spectral–Spatial Network for Hyperspectral Target Detection

Published:2023 Issue: Volume:20 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Feng Shou¹^ORCID,Feng Rui¹,Liu Jianfei¹,Zhao Chunhui¹,Xiong Fengchao²^ORCID,Zhang Lifu³^ORCID

Affiliation:

1. College of Information and Communication Engineering and Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, China

2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

3. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

Open Fund of State Key Laboratory of Remote Sensing Science

Heilongjiang Provincial Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10034981/10098614.pdf?arnumber=10098614

Reference20 articles.

1. Hyperspectral Image Classification With Attention-Aided CNNs

2. Spectral–spatial anomaly detection via collaborative representation constraint stacked autoencoders for hyperspectral images;zhao;IEEE Geosci Remote Sens Lett,2021

3. HTD-Net: A Deep Convolutional Neural Network for Target Detection in Hyperspectral Imagery

4. Transferred deep learning for hyperspectral target detection

5. Target Detection With Unconstrained Linear Mixture Model and Hierarchical Denoising Autoencoder in Hyperspectral Imagery

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