DSET-Net: deep self-embedded transformer network for hyperspectral unmixing

Xueer You; Yuanchao Su; Lianru Gao; Xu Sun; Pengfei Li; Dongsheng Liu

doi:10.1117/12.3019479

23 January 2024 DSET-Net: deep self-embedded transformer network for hyperspectral unmixing

Xueer You, Yuanchao Su, Lianru Gao, Xu Sun, Pengfei Li, Dongsheng Liu

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 129780J (2024) https://doi.org/10.1117/12.3019479
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

Recently, transformer self-attention mechanisms have had significant advantages in the deep learning field, and it has been extensively used for natural language processing and video tracking. Furthermore, self-attention mechanisms have also been applied in hyperspectral unmixing. Although self-attention mechanisms are usually efficient and flexible tools, the original transformer might break the inner structure of data during learning, causing negative effects to unmixing. In this work, we employ transformer self-attention mechanisms to achieve a deep self-embedded transformer network(DSET-Net) for hyperspectral unmixing. The proposed DSET-Net adopts an autoencoder framework and achieves local and overall feature parameter sharing in the encoder through a 'Transformer in Transformer (TNT)' strategy. The DSET-Net preserves the spatial details of hyperspectral images and involves only one convolution operation in the encoder, substantially improving the learning performance. The effectiveness of the proposed method is evaluated by using real hyperspectral datasets. Our experimental results indicate that the newly proposed DSET-Net is very competitive compared with other state-of-the-art approaches.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xueer You, Yuanchao Su, Lianru Gao, Xu Sun, Pengfei Li, and Dongsheng Liu "DSET-Net: deep self-embedded transformer network for hyperspectral unmixing", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 129780J (23 January 2024); https://doi.org/10.1117/12.3019479

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available