Although considerable effort has been dedicated to improving the solution to
the hyperspectral unmixing problem, non-idealities such as complex radiation
scattering and endmember variability negatively impact the performance of most
existing algorithms and can be very challenging to address. Recently, deep
learning-based frameworks have been explored for hyperspectral umixing due to
their flexibility and powerful representation capabilities. However, such
techniques either do not address the non-idealities of the unmixing problem, or
rely on black-box models which are not interpretable. In this paper, we propose
a new interpretable deep learning method for hyperspectral unmixing that
accounts for nonlinearity and endmember variability. The proposed method
leverages a probabilistic variational deep-learning framework, where
disentanglement learning is employed to properly separate the abundances and
endmembers. The model is learned end-to-end using stochastic backpropagation,
and trained using a self-supervised strategy which leverages benefits from
semi-supervised learning techniques. Furthermore, the model is carefully
designed to provide a high degree of interpretability. This includes modeling
the abundances as a Dirichlet distribution, the endmembers using
low-dimensional deep latent variable representations, and using two-stream
neural networks composed of additive piecewise-linear/nonlinear components.
Experimental results on synthetic and real datasets illustrate the performance
of the proposed method compared to state-of-the-art algorithms.

本文提出了一种新的可解释的深度学习方法，用于处理高光谱混合问题，该方法考虑了非线性和端元变异性，并利用概率变分深度学习框架和解缠态学习来实现丰度和端元的分离，通过使用两个流的神经网络以及多个组成元素的附加分段线性 / 非线性组件来提供高度的可解释性，并通过合成和真实数据集的实验证明了该方法的性能优于现有算法。

学习可解释的深度卷积神经网络用于高光谱成分解混

Learning Interpretable Deep Disentangled Neural Networks for  Hyperspectral Unmixing

In-Memory Computing (IMC) platforms such as analog crossbars are gaining
focus as they facilitate the acceleration of low-precision Deep Neural Networks
(DNNs) with high area- & compute-efficiencies. However, the intrinsic
non-idealities in crossbars, which are often non-deterministic and non-linear,
degrade the performance of the deployed DNNs. In addition to quantization
errors, most frequently encountered non-idealities during inference include
crossbar circuit-level parasitic resistances and device-level non-idealities
such as stochastic read noise and temporal drift. In this work, our goal is to
closely examine the distortions caused by these non-idealities on the
dot-product operations in analog crossbars and explore the feasibility of a
nearly training-less solution via crossbar-aware fine-tuning of batchnorm
parameters in real-time to mitigate the impact of the non-idealities. This
enables reduction in hardware costs in terms of memory and training energy for
IMC noise-aware retraining of the DNN weights on crossbars.

本文研究了模拟交叉开关中的非理想误差，通过在线优化进行微调，以减少训练成本和内存，并减少量化误差和非理想误差，提高深度神经网络的性能。