ABSTRACT Hyperspectral unmixing technique is a conventional approach addressing the mixed pixel issue. In this paper, we present an autoencoder (AE) network that deals with estimating the abundances in hyperspectral images (HSIs) given the endmembers. In the suggested network, the mixed pixel issue in a supervised scenario is investigated since the weights of the decoder are set equal to the endmembers. More importantly, the network is trained by a blend of two celebrated objective functions, mean squared error and spectral angle distance, in order to have both privileges of sensitivity to small errors and being scale invariant. To assure the convenience, the sparsity and physical constraints are imposed on the abundances, and the regularization techniques are employed. The abundances are initialized via the fully constrained least squares method thanks to the setting of the initial encoder weights. The superiority of the presented AE is demonstrated via conducting several experiments on synthetic and real HSIs and comparing the results quantitatively and visually with several existing methods.

中文翻译：

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一种基于自编码器网络的线性高光谱解混方法

摘要 高光谱解混技术是解决混合像素问题的传统方法。在本文中，我们提出了一个自动编码器 (AE) 网络，该网络处理在给定端元的情况下估计高光谱图像 (HSI) 中的丰度。在建议的网络中，研究了监督场景中的混合像素问题，因为解码器的权重设置为等于端成员。更重要的是，该网络由两个著名的目标函数（均方误差和光谱角度距离）的混合训练，以便同时具有对小误差的敏感性和尺度不变性的特权。为了保证方便，对丰度施加了稀疏性和物理约束，并采用了正则化技术。由于初始编码器权重的设置，丰度通过完全约束最小二乘法进行初始化。通过对合成和真实 HSI 进行多次实验并将结果与​​几种现有方法进行定量和可视化比较，证明了所提出的 AE 的优越性。