Computational Intelligence in Remote Sensing

Wu, Yue; Gong, Maoguo; Miao, Qiguang; Qin, Kai

doi:10.3390/rs15225325

Remote Sensing

2023

DOI: 10.3390/rs15225325

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Computational Intelligence in Remote Sensing

Yue Wu,

Maoguo Gong,

Qiguang Miao

et al.

Abstract: With the development of Earth observation techniques, vast amounts of remote sensing data with a high spectral–spatial–temporal resolution are captured all the time, and remote sensing data processing and analysis have been successfully used in numerous fields, including geography, environmental monitoring, land survey, disaster management, mineral exploration and more [...]

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2024

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Cited by 1 publication

References 12 publications

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D2Former: Dual-Domain Transformer for Change Detection in VHR Remote Sensing Images

Zheng,

Liu,

et al. 2024

Electronics

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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 (D2Former) 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 D2Former another aspect and dimension to enhance the ability to detect change information. Therefore, the proposed D2Former 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 D2Former 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.

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D2Former: Dual-Domain Transformer for Change Detection in VHR Remote Sensing Images

Zheng,

Liu,

et al. 2024

Electronics

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show abstract

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Computational Intelligence in Remote Sensing

Cited by 1 publication

References 12 publications

D2Former: Dual-Domain Transformer for Change Detection in VHR Remote Sensing Images

D2Former: Dual-Domain Transformer for Change Detection in VHR Remote Sensing Images

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