Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes
Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes
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DOI：
10.1109/icassp.2012.6288115
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
Y. Altmann;N. Dobigeon;S. Mclaughlin;J. Tourneret
中科院分区：
文献类型：
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作者：
Y. Altmann;N. Dobigeon;S. Mclaughlin;J. Tourneret
提出了一种基于高斯过程的非线性高光谱图像解混方法。该模型假定丰度向量与受高斯白噪声污染的像素反射率之间存在非线性映射。该模型中涉及的参数满足在贝叶斯框架中自然表达的物理约束。所提出的丰度估计程序通过最大化适当的不依赖于端元的后验分布，同时应用于图像的所有像素。在确定所有图像像素的丰度后，使用高斯过程回归估计图像中包含的端元。通过对合成数据的仿真，对所得到的无监督解混策略的性能进行了评价。
This paper describes a Gaussian process based method for nonlinear hyperspectral image unmixing. The proposed model assumes a nonlinear mapping from the abundance vectors to the pixel reflectances contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy physical constraints that are naturally expressed within a Bayesian framework. The proposed abundance estimation procedure is applied simultaneously to all pixels of the image by maximizing an appropriate posterior distribution which does not depend on the endmembers. After determining the abundances of all image pixels, the endmembers contained in the image are estimated by using Gaussian process regression. The performance of the resulting unsupervised unmixing strategy is evaluated through simulations conducted on synthetic data.