PDF(Pubmed)
Abstract：
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.
摘要：
遥感（RS）中的变压器研究，2021年后开始增加，面临着相对缺乏审查的问题。为了了解RS变压器的发展趋势，我们通过将变压器的应用分为八个领域，对变压器的主要研究进行了定量分析：土地利用/土地覆盖（LULC）分类，分割，聚变,变化检测，物体检测,物体识别,注册，和其他人。定量结果表明，变压器在LULC分类和融合中获得了更高的准确性，在分割和目标检测方面具有更稳定的性能。结合LULC分类和分割的分析结果，我们发现变压器比卷积神经网络(CNN)需要更多的参数。此外,还需要进一步研究推理速度，以提高变压器的性能。确定我们数据库中变压器最常见的应用场景是城市，农田,和水体。我们还发现，变压器用于自然科学，如农业和环境保护，而不是人文或经济学。最后,这项工作总结了在研究过程中获得的遥感变压器的分析结果，并为未来的发展方向提供了展望。
