高光谱成像
计算机科学
模式识别(心理学)
人工智能
先验概率
深度学习
光谱斜率
谱线
贝叶斯概率
物理
天文
作者
Ge Zhang,Shaohui Mei,Bobo Xie,Yan Feng,Qian Du
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:9
标识
DOI:10.1109/lgrs.2022.3214843
摘要
Deep learning-based methods have drawn great attention in hyperspectral unmixing and obtained promising performance due to their powerful learning capability. However, few existing networks explicitly deal with the spectral variability inevitably present in hyperspectral images, limiting their fitting performance. In this letter, a spectral variability augmented two-stream network (SVATN) is designed to explicitly address the problem of spectral variability in a deep convolutional network for sparse unmixing. Specifically, the proposed SVATN maps a random input to coefficients of spectral variability in addition to abundances of endmembers, in which spectral variability is accommodated by the linear mixture model as an augmented item. Moreover, a spatial-spectral correlation-based variability extraction method (SSCVE) is proposed to construct a spectral variability library, which serves as priors in the loss function to optimize the proposed SVATN. Experiments over synthetic and real data sets demonstrate the superiority of the proposed SVATN over several state-of-the-art methods. The code of our proposed method is released at: https://github.com/MeiShaohui/SVATN.
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