Local and global semantic relationship network for remote sensing scene classification

Fei Song; Ruofei Ma; Tao Lei; Zhenming Peng

doi:10.1117/12.2683561

26 June 2023 Local and global semantic relationship network for remote sensing scene classification

Fei Song, Ruofei Ma, Tao Lei, Zhenming Peng

Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 127210C (2023) https://doi.org/10.1117/12.2683561
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China

Abstract

Remote sensing scene (RSS) classification is an important research topic for high-resolution HR remote sensing image understanding. Recently, many approaches have been presented for the task, including data-driven and machine learning methods. However, accurately identifying scenes from HR remote sensing images remains challenging since it is difficult to effectively extract multiscale and key features from the complex geometrical structures and spatial patterns of large-scale ground object. In this paper, we propose a novel local and global semantic relationship network (LGSRNet) for RSS classification. ConvNeXt-T with the same performance as the local vision Swin Transformer is adopted to extract feature map with powerful discriminative ability. Meanwhile, the semantic relation learning (SRL) with graph convolutional networks is presented to further learn semantic relationships between labels of RSS categories within spatial domain. Subsequently, cosine similarity is adopted to incorporate the ConvNeXt-T and SRL. Extensive experiments on two attribute-classification datasets (AID and NWPU-RESISC45) demonstrate that LGSRNet outperforms several other state-of-the-art methods.

Citation Download Citation

Fei Song, Ruofei Ma, Tao Lei, and Zhenming Peng "Local and global semantic relationship network for remote sensing scene classification", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 127210C (26 June 2023); https://doi.org/10.1117/12.2683561

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