动态语义-几何指导和结构传输网络,用于跨场景的高光谱图像分类
Qin Xu1, Shuke Wang1, Jie Wei1
1Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, 230601, China; Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, 230601, China; School of Computer Science and Technology, Anhui University, Hefei, 230601, China.
概括
这项研究引入了一个新的动态语义-几何指导和结构传输 (DSGG-ST) 网络,以改善跨场景的高光谱图像分类 (HSIC). DSGG-ST网络有效地应对领域适应挑战,取得最先进的结果.
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:
- 使用域调整的跨场景高光谱图像分类 (HSIC) 是一个不断增长的研究领域.
- 现有的方法往往无法完全挖掘源域信息或使用对噪声敏感的表征,导致负面转移和性能降低.
研究的目的:
- 提出一个新的动态语义-几何指导和结构传输 (DSGG-ST) 网络,以加强跨场景的HSIC.
- 通过改进信息挖掘和结构传输,克服HSIC中现有的域调整方法的局限性.
主要方法:
- 引入了一个动态语义-几何指导 (DSGG) 模块,用于从语义和几何角度进行域不变学习.
- 开发了一个图表注意力学习匹配 (GALM) 模块,利用图表注意力网络和SeedGNN进行有效的结构信息传输和对齐.
主要成果:
- 拟议的DSGG-ST网络在三个共同的跨场景高光谱图像数据集上实现了新的最先进的性能 (SOTA).
- 实验结果验证了DSGG模块在指导域调整方面的有效性,以及GALM模块在结构转移方面的有效性.
结论:
- DSGG-ST网络通过有效处理域转移,为跨场景的高光谱图像分类提供了强大的解决方案.
- 提出的方法显示了与现有方法相比的显著改进,突出了动态语义-几何指导和先进结构转移的重要性.
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