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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 neural network-based unmixing model with transformers. A transformer network captures nonlocal feature dependencies by interactions between image patches, which are not employed in CNN models, and hereby has the ability 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 https://github.com/preetam22n/DeepTrans-HSU.

Spectral measurements are commonly applied for the nondestructive estimation of leaf parameters, such as the concentrations of chlorophyll ab, carotenoid, anthocyanin, and brown pigment, the leaf water content, and the leaf mass per area for quantification of vegetation physiology. The most popular way to estimate these parameters is by using spectral vegetation indices. The use of biochemical models allows to employ the full wavelength range (400-2500 nm) and to physically interpret the result. However, their performance is usually lower than that of supervised machine learning regression techniques. Machine learning regression techniques, on the other hand, have the disadvantage that the relation between estimated parameters and the reflectance/transmission spectra is unclear.In this paper, a hybrid between a supervised learning method and physical modeling for the estimation of leaf parameters is proposed. In this method, a machine learning regression technique is applied to learn a mapping from the true hyperspectral dataset to a dataset that follows the PROSPECT model. The PROSPECT model then reveals the actual leaf parameters. Two mapping methods, based on gaussian processes (GP) and kernel ridge regression (KRR) are proposed. As an alternative, a mapping onto the leaf absorption spectra is proposed as well. The proposed methodology not only estimates the leaf parameters with a lower error but also solves the interpretation problem of the parameters estimated by the advanced machine learning regression techniques. This method is validated on the ANGERS and LOPEX dataset.