Hyperspectral unmixing is an important task in the analyses and applications of hyperspectral images. Recently, the autoencoder network has been intensively studied to unmix hyperspectral image, recovering the material signatures and their corresponding abundance maps from the hyperspectral pixels. However, the autoencoder network cannot get a unique solution since the loss function is nonconvex. In addition, the data often contain a lot of noise. To address these problems, we propose an autoencoder network, referred to as MDC-SAE, that introduces two different constraints to optimize the spectral unmixing problem. Specifically, we adopt the L1/2 norm regularizer to constrict the abundance vectors, making them sparse. At the same time, we apply the minimum distance constraint on the endmember matrix to push each endmember toward its centroid. We evaluate our method on both synthetic and real data sets, and experimental results demonstrate that the proposed method can achieve the desired solutions and outperforms several state-of-the-art methods.

中文翻译：

---

用于高光谱解混的最小距离受限稀疏自动编码器网络

高光谱分解是高光谱图像分析和应用中的重要任务。近来，已经对自动编码器网络进行了深入研究，以取消混合高光谱图像，从高光谱像素中恢复材料特征及其相应的丰度图。但是，由于损失函数是非凸的，因此自动编码器网络无法获得唯一的解决方案。另外，数据经常包含很多噪声。为了解决这些问题，我们提出了一种称为MDC-SAE的自动编码器网络，该网络引入了两种不同的约束条件来优化频谱解混问题。具体来说，我们采用L1 / 2范数正则化器来压缩丰度矢量，使其稀疏。同时，我们在端构件矩阵上应用最小距离约束，以将每个端构件推向其质心。