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Compressive Source Separation: Theory and Methods for Hyperspectral Imaging
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DOI：10.1109/TIP.2013.2281405.png)
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摘要
En 中文
We propose and analyze a new model for hyperspectral images (HSIs) based on the assumption that the whole signal is composed of a linear combination of few sources, each of which has a specific spectral signature, and that the spatial abundance maps of these sources are themselves piecewise smooth and therefore efficiently encoded via typical sparse models. We derive new sampling schemes exploiting this assumption and give theoretical lower bounds on the number of measurements required to reconstruct HSI data and recover their source model parameters. This allows us to segment HSIs into their source abundance maps directly from compressed measurements. We also propose efficient optimization algorithms and perform extensive experimentation on synthetic and real datasets, which reveals that our approach can be used to encode HSI with far less measurements and computational effort than traditional compressive sensing methods.
Keyword:
Compressed sensing
source separation
hyperspectral image
linear mixture model
sparsity
proximal splitting method
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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引用论文
Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery独立分量分析在高光谱图像的端元提取和丰度量化中的应用OAAI
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