Blind hyperspectral unmixing is an important technique in hyperspectral image analysis, aiming at estimating endmembers and their respective fractional abundances. Consider the limitations of using the linear model, nonlinear unmixing methods have been studied under different model assumptions. However, existing nonlinear unmixing algorithms do not fully exploit spectral and spatial correlation information. This paper proposes a nonsymmetric autoencoder network to overcome this issue. The proposed scheme benefits from the universal modeling ability of deep neural networks and enables to learn the nonlinear relation from the data. Particularly, the long short-term memory network (LSTM) structure is included to capture spectral correlation information, and a spatial regularization is introduced to improve the spatial continuity of results. An attention mechanism is also used to further enhance the unmixing performance. Experiments with synthetic and real data are conducted to illustrate the effectiveness of the proposed method.

中文翻译：

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基于 LSTM-DNN 的自动编码器网络用于非线性高光谱图像混合


盲高光谱分解是高光谱图像分析中的一项重要技术，旨在估计端元及其各自的分数丰度。考虑到使用线性模型的局限性，在不同模型假设下研究了非线性解混方法。然而，现有的非线性解混算法没有充分利用光谱和空间相关信息。本文提出了一种非对称自动编码器网络来克服这个问题。所提出的方案受益于深度神经网络的通用建模能力，并且能够从数据中学习非线性关系。特别是，包括长短期记忆网络（LSTM）结构来捕获光谱相关信息，并引入空间正则化来提高结果的空间连续性。还使用注意机制来进一步增强分解性能。使用合成数据和真实数据进行实验来说明所提出方法的有效性。