高光谱成像
自编码
基本事实
计算机科学
合成数据
人工智能
模式识别(心理学)
深度学习
作者
Burkni Palsson,Magnus Ö. Ulfarsson,Jóhannes R. Sveinsson
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:19: 1-5
被引量:2
标识
DOI:10.1109/lgrs.2022.3150245
摘要
Spectral variability in hyperspectral images (HSIs) has received lot of attention over the last years, especially in the field of hyperspectral unmixing (HU) where it is a major issue. In this letter, we propose a method utilizing a variational autoencoder (VAE) for creating synthetic HSIs having controllable degree of spectral variability from existing HSIs with established ground-truth abundance maps and endmembers. Such synthetic datasets can be useful for developing HU methods that can handle spectral variability in HSIs. We investigate how the variability in the synthetic images differs from the original images and perform blind unmixing experiments using generated datasets to illustrate the effect of increasing variability. Code for method is available at https://github.com/burknipalsson/vae_synthetic_hsi .
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