动态封闭融合网络,具有对超谱图像分类的层次多尺度关注
Xianjian Shi1, Lilong Liu2, Xin Bao1
1College of Earth Sciences, Guilin University of Technology, Guilin, 541006, China.
Scientific reports
|December 22, 2025
概括
一个新的有层次的多尺度关注的动态封闭融合网络 (DGFNet) 通过自适应地融合特征来改善超谱图像分类. 该方法在基准数据集中实现了最先进的准确性和稳定性.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 超光谱图像分类面临着固定特征融合策略的挑战.
- 现有的方法难以适应各种数据特征,并有效地将多尺度特征与注意力机制集成在一起.
研究的目的:
- 为改进超光谱图像分类提出一个具有层次多尺度注意力的新型动态封闭融合网络 (DGFNet).
- 解决固定聚变策略的局限性,增强多尺度特征提取和注意力机制之间的协同作用.
主要方法:
- 该DGFNet采用多尺度特征聚合器 (MSFA),使用金字塔膨胀卷积来进行全面的空间特征提取.
- 一个增强的道空间注意力 (ECSA) 模块与多个聚合和级联结构加深功能交互.
- 一个动态封闭的融合模块根据数据特征自适应地调整功能贡献.
主要成果:
- 在基准数据集上,DGFNet实现了高准确率:帕维亚大学 (96.91%),休斯顿 (97.12%),印第安松树 (94.05%) 和WHU-HongHu (94.46%).
- 动态门融合在分类准确性,计算效率和模型稳定性方面优于其他融合策略.
- 废弃实验证实了每个拟议模块的有效性和必要性.
结论:
- DGFNet提供了一种高效,准确和强大的解决方案,用于高光谱图像分类.
- 与传统的聚变方法相比,动态封闭的聚变方法提供了更高的性能.
- 拟议的方法在适应各种遥感数据特征方面取得了重大进展.
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