Semi-supervised classification method for PolSAR based on vision transformer (ViT)

Xiangshen Li; Jie Yu; Lihua Han

doi:10.1117/12.3040990

11 September 2024 Semi-supervised classification method for PolSAR based on vision transformer (ViT)

Xiangshen Li, Jie Yu, Lihua Han

Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 132530N (2024) https://doi.org/10.1117/12.3040990
Event: 4th International Conference on Signal Image Processing and Communication, 2024, Xi'an, China

Abstract

Semi-supervised classification methods generate pseudo-labels from unlabeled data, where pseudo-labels' precision is vital for successful classification outcomes. Addressing the challenge of inaccuracies in pseudo-label categories during training, this paper introduces a semi-supervised classification strategy for PolSAR images, leveraging the Vision Transformer (ViT) technology. In this study, the Simple Linear Iterative Clustering (SLIC) method was applied to create superpixel blocks with distinct boundaries and organized structures, facilitating the generation of pseudo-labels with a correctness rate of 85.9%. This paper introduces the Vision Transformer (ViT) network, capitalizing on its multi-head attention mechanism. This approach enhances global image information extraction while simultaneously addressing local details for pseudo-label training. Specifically, on the Flevoland data set, an increase in pseudo-label category accuracy of 7.09% and 5.47% was achieved. For the Wuhan Tongshun River dataset, improvements of 2.44% and 3.41% in pseudo-label category accuracy were recorded, thereby elevating the precision of PolSAR semi-supervised classification.

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

Citation Download Citation

Xiangshen Li, Jie Yu, and Lihua Han "Semi-supervised classification method for PolSAR based on vision transformer (ViT)", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 132530N (11 September 2024); https://doi.org/10.1117/12.3040990

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Transformers

Buildings

Image classification

Deep learning

Land cover

Image segmentation

Matrices

Show All Keywords

Keywords/Phrases

Search In:

Publication Years