Hyperspectral Unmixing Using Transformer Network

Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With superior performance, transformers found way field of hyperspectral image classification and achieved promising results. In this article, we harness power to conquer task unmixing propose a novel deep neural network-based model transformers. A transformer network captures nonlocal feature dependencies by interactions between patches, which are not employed CNN models, hereby has ability enhance quality endmember spectra abundance maps. The proposed is combination convolutional autoencoder transformer. data encoded encoder. long-range representations derived from reconstructed using decoder. We applied three widely used datasets, i.e., Samson, Apex, Washington DC mall compared it terms root mean squared error spectral angle distance. source code for will be made publicly available at https://github.com/preetam22n/DeepTrans-HSU.