  Currently, this paper is under review in IEEE. Transformers have intrigued
the vision research community with their state-of-the-art performance in
natural language processing. With their superior performance, transformers have
found their way in the field of hyperspectral image classification and achieved
promising results. In this article, we harness the power of transformers to
conquer the task of hyperspectral unmixing and propose a novel deep unmixing
model with transformers. We aim to utilize the ability of transformers to
better capture the global feature dependencies in order to enhance the quality
of the endmember spectra and the abundance maps. The proposed model is a
combination of a convolutional autoencoder and a transformer. The hyperspectral
data is encoded by the convolutional encoder. The transformer captures
long-range dependencies between the representations derived from the encoder.
The data are reconstructed using a convolutional decoder. We applied the
proposed unmixing model to three widely used unmixing datasets, i.e., Samson,
Apex, and Washington DC mall and compared it with the state-of-the-art in terms
of root mean squared error and spectral angle distance. The source code for the
proposed model will be made publicly available at
\url{https://github.com/preetam22n/DeepTrans-HSU}.
