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D 2 Former: Dual-Domain Transformer for Change Detection in VHR Remote Sensing Images.

Authors :
Zheng, Huanhuan
Liu, Hui
Lu, Lei
Li, Shiyin
Lin, Jiyan
Source :
Electronics (2079-9292); Jun2024, Vol. 13 Issue 11, p2204, 23p
Publication Year :
2024

Abstract

Computational intelligence technologies have been extensively applied for the interpretation of remote sensing imagery. Recently, the computational-intelligence-based Transformer change detection (CD) approach has attracted increasing attention. However, the current Transformer-based CD method can better capture global features, but there is no good solution for the loss of local detail information. For this reason, introducing semantic and frequency information from the perspective of a dual-domain can be beneficial for improving the representation of detailed features to improve CD performance. To overcome this limitation, a dual-domain Transformer (D<superscript>2</superscript>Former) is proposed for CD. Firstly, we adopt a semantic tokenizer to capture the semantic information, which promotes the enrichment and refinement of semantic change information in the Transformer. Secondly, a frequency tokenizer is introduced to acquire the frequency information of the features, which offers the proposed D<superscript>2</superscript>Former another aspect and dimension to enhance the ability to detect change information. Therefore, the proposed D<superscript>2</superscript>Former employs dual-domain tokenizers to acquire and fuse the feature representation with rich semantic and frequency information, which can refine the features to acquire more fine-grained CD ability. Extensive experiments on three CD benchmark datasets demonstrate that the proposed D<superscript>2</superscript>Former obviously outperforms some other existing approaches. The results present the competitive performance of our method on the WHU-CD, LEVIR-CD, and GZ-CD datasets, for which it achieved F1-Score metrics of 92.85%, 90.60%, and 87.02%, respectively. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
COMPUTATIONAL intelligence

Details

Language :
English
ISSN :
20799292
Volume :
13
Issue :
11
Database :
Complementary Index
Journal :
Electronics (2079-9292)
Publication Type :
Academic Journal
Accession number :
177857319
Full Text :
https://doi.org/10.3390/electronics13112204