Multi-granularity siamese transformer-based change detection in remote sensing imagery

Song, Lei; Xia, Min; Xu, Yao; Weng, Liguo; Hu, Kai; Lin, Haifeng; Qian, Ming

doi:10.1016/j.engappai.2024.108960

Engineering Applications of Artificial Intelligence

2024

DOI: 10.1016/j.engappai.2024.108960

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Multi-granularity siamese transformer-based change detection in remote sensing imagery

Lei Song,

Min Xia,

Yao Xu

et al.

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2024

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Cited by 2 publications

References 63 publications

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SPNet: Dual-Branch Network with Spatial Supplementary Information for Building and Water Segmentation of Remote Sensing Images

Zhao,

Xia,

Weng

et al. 2024

Remote Sensing

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Semantic segmentation is primarily employed to generate accurate prediction labels for each pixel of the input image, and then classify the images according to the generated labels. Semantic segmentation of building and water in remote sensing images helps us to conduct reasonable land planning for a city. However, many current mature networks face challenges in simultaneously attending to both contextual and spatial information when performing semantic segmentation on remote sensing imagery. This often leads to misclassifications and omissions. Therefore, this paper proposes a Dual-Branch Network with Spatial Supplementary Information (SPNet) to address the aforementioned issues. We introduce a Context-aware Spatial Feature-Extractor Unit (CSF) to extract contextual and spatial information, followed by the Feature-Interaction Module (FIM) to supplement contextual semantic information with spatial details. Additionally, incorporating the Goal-Oriented Attention Mechanism helps in handling noise. Finally, to obtain more detailed branches, a Multichannel Deep Feature-Extraction Module (MFM) is introduced to extract features from shallow-level network layers. This branch guides the fusion of low-level semantic information with high-level semantic information. Experiments were conducted on building and water datasets, respectively. The results indicate that the segmentation accuracy of the model proposed in this paper surpasses that of other existing mature models. On the building dataset, the mIoU reaches 87.57, while on the water dataset, the mIoU achieves 96.8, which means that the model introduced in this paper demonstrates strong generalization capabilities.

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SPNet: Dual-Branch Network with Spatial Supplementary Information for Building and Water Segmentation of Remote Sensing Images

Zhao,

Xia,

Weng

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review

Saidi,

Idbraim,

Karmoude

et al. 2024

Remote Sensing

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Remote sensing images provide a valuable way to observe the Earth’s surface and identify objects from a satellite or airborne perspective. Researchers can gain a more comprehensive understanding of the Earth’s surface by using a variety of heterogeneous data sources, including multispectral, hyperspectral, radar, and multitemporal imagery. This abundance of different information over a specified area offers an opportunity to significantly improve change detection tasks by merging or fusing these sources. This review explores the application of deep learning for change detection in remote sensing imagery, encompassing both homogeneous and heterogeneous scenes. It delves into publicly available datasets specifically designed for this task, analyzes selected deep learning models employed for change detection, and explores current challenges and trends in the field, concluding with a look towards potential future developments.

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Multi-granularity siamese transformer-based change detection in remote sensing imagery

Cited by 2 publications

References 63 publications

SPNet: Dual-Branch Network with Spatial Supplementary Information for Building and Water Segmentation of Remote Sensing Images

SPNet: Dual-Branch Network with Spatial Supplementary Information for Building and Water Segmentation of Remote Sensing Images

Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review

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