MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing

Publication Type:

Journal Article



IEEE Transactions on Geoscience and Remote Sensing, Volume VOL. 60 (2022)




In this article, we propose a minimum simplex convolutional network (MiSiCNet) for deep hyperspectral unmixing.
Unlike all the deep learning-based unmixing methods proposed
in the literature, the proposed convolutional encoder–decoder
architecture incorporates spatial information and geometrical
information of the hyperspectral data in addition to the spectral
information. The spatial information is incorporated using convolutional filters and implicitly applying a prior on the abundances.
The geometrical information is exploited by incorporating a
minimum simplex volume penalty term in the loss function for
the endmember estimation. This term is beneficial when there
are no pure material pixels in the data, which is often the
case in real-world applications. We generated simulated datasets,
where we consider two different no-pure pixel scenarios. In the
first scenario, there are no pure pixels but at least two pixels
on each facet of the data simplex (i.e., mixtures of two pure
materials). The second scenario is a complex case with no pure
pixels and only one pixel on each facet of the data simplex.
In addition, we evaluate the performance of MiSiCNet in three
real datasets. The experimental results confirm the robustness
of the proposed method to both noise and the absence of pure
pixels. In addition, MiSiCNet considerably outperforms the stateof-the-art unmixing approaches. The results are given in terms of
spectral angle distance in degree for the endmember estimation
and the root mean square error in percentage for the abundance
estimation. MiSiCNet was implemented in Python (3.8) using
PyTorch as the platform for the deep network and is available
online: https://github.com/BehnoodRasti/MiSiCNet.

Research area: