Sparse Autoencoder Based Hyperspectral Anomaly Detection with the Singular Spectrum Analysis Based Spectral Denoising-Reference-Cited by-同舟云学术

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Sparse Autoencoder Based Hyperspectral Anomaly Detection with the Singular Spectrum Analysis Based Spectral Denoising

Published:2024-07-07 Issue: Volume: Page:9210-9213
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Li Yinhe¹,Ren Jinchang¹,Gao Zhi²,Sun Genyun³

Affiliation:

1. Robert Gordon University,National Subsea Centre,Aberdeen,U.K.

2. Wuhan University,School of Remote Sensing and Information Engineering,Wuhan,China

3. China University of Petroleum (East China),College of Oceanography and Space Informatics,Qingdao,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10641314.pdf?arnumber=10641314

Reference12 articles.

1. CBANet: An End-to-End Cross-Band 2-D Attention Network for Hyperspectral Change Detection in Remote Sensing

2. Nondestructive Quantitative Measurement for Precision Quality Control in Additive Manufacturing Using Hyperspectral Imagery and Machine Learning

3. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution

4. Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery

5. Analysis and Optimizations of Global and Local Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Data

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