论文标题
高光谱像素与潜在的Dirichlet变异自动编码器混合
Hyperspectral Pixel Unmixing with Latent Dirichlet Variational Autoencoder
论文作者
论文摘要
我们提出了一种高光谱像素{\ it umixing}的方法。所提出的方法假设可以将（1）{\ it丰度}编码为dirichlet分布，并且（2）{\ it end -eNd -eNd -Members}的光谱可以表示为多变量正常分布。该方法解决了差异自动编码器设置内的丰度估计和末端提取的问题，其中dirichlet瓶颈层建模丰度，而解码器则执行最终成员的提取。所提出的方法还可以利用传输学习范式，其中该模型仅在包含一个或多个感兴趣的末端成员的线性组合的合成数据上训练。在这种情况下，我们从美国地质调查谱库中检索末端成员（Spectra）。因此，经过训练的模型可以随后用于对包含用于生成合成数据的最终成员的子集的“真实数据”进行像素脉络。该模型在几个基准下实现了最先进的结果：库层，城市Hydice和Samson。我们还提出了新的合成数据集Ontech-HSI-SYN-21，可用于研究高光谱像素固定方法。我们展示了在库层和Ontech-HSI-Syn-21数据集上提出的模型的转移学习能力。总而言之，可以将提出的方法应用于像素，不将各种领域混合，包括农业，林业，矿物学，材料分析，医疗保健等。此外，提出的方法避免了对培训数据进行培训的需求，以利用该模型的转移范式来利用该模型，从而在其中培训了使用合成数据生成的eNdthetty数据，该模型是实时的。
We present a method for hyperspectral pixel {\it unmixing}. The proposed method assumes that (1) {\it abundances} can be encoded as Dirichlet distributions and (2) spectra of {\it endmembers} can be represented as multivariate Normal distributions. The method solves the problem of abundance estimation and endmember extraction within a variational autoencoder setting where a Dirichlet bottleneck layer models the abundances, and the decoder performs endmember extraction. The proposed method can also leverage transfer learning paradigm, where the model is only trained on synthetic data containing pixels that are linear combinations of one or more endmembers of interest. In this case, we retrieve endmembers (spectra) from the United States Geological Survey Spectral Library. The model thus trained can be subsequently used to perform pixel unmixing on "real data" that contains a subset of the endmembers used to generated the synthetic data. The model achieves state-of-the-art results on several benchmarks: Cuprite, Urban Hydice and Samson. We also present new synthetic dataset, OnTech-HSI-Syn-21, that can be used to study hyperspectral pixel unmixing methods. We showcase the transfer learning capabilities of the proposed model on Cuprite and OnTech-HSI-Syn-21 datasets. In summary, the proposed method can be applied for pixel unmixing a variety of domains, including agriculture, forestry, mineralogy, analysis of materials, healthcare, etc. Additionally, the proposed method eschews the need for labelled data for training by leveraging the transfer learning paradigm, where the model is trained on synthetic data generated using the endmembers present in the "real" data.
