STANet: A Novel Predictive Neural Network for Ground-Based Remote Sensing Cloud Image Sequence Extrapolation

Lu, Zhiying; Zhou, Zhiyi; Li, Xin; Zhang, Jianfeng

doi:10.1109/tgrs.2023.3268503

Cited by 2 publications

(1 citation statement)

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“…This proximity indicates that the performance of attention-based methods is closer to transformer-based methods, since attention mechanisms imitate human attention and generate more discriminative features. So, attention mechanism can enhance the effectiveness of the network when compared with CNNs [ 27 ].…”

Section: Results Of Quantitative Analysismentioning

confidence: 99%

Transformers for Remote Sensing: A Systematic Review and Analysis

Wang,

Ma,

et al. 2024

Sensors

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Research on transformers in remote sensing (RS), which started to increase after 2021, is facing the problem of a relative lack of review. To understand the trends of transformers in RS, we undertook a quantitative analysis of the major research on transformers over the past two years by dividing the application of transformers into eight domains: land use/land cover (LULC) classification, segmentation, fusion, change detection, object detection, object recognition, registration, and others. Quantitative results show that transformers achieve a higher accuracy in LULC classification and fusion, with more stable performance in segmentation and object detection. Combining the analysis results on LULC classification and segmentation, we have found that transformers need more parameters than convolutional neural networks (CNNs). Additionally, further research is also needed regarding inference speed to improve transformers’ performance. It was determined that the most common application scenes for transformers in our database are urban, farmland, and water bodies. We also found that transformers are employed in the natural sciences such as agriculture and environmental protection rather than the humanities or economics. Finally, this work summarizes the analysis results of transformers in remote sensing obtained during the research process and provides a perspective on future directions of development.

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Section: Results Of Quantitative Analysismentioning

confidence: 99%