TGF-Net:变压器和核心CNN融合网络用于多式联接式遥感图像分类
Huiqing Wang1,2, Huajun Wang2, Linfen Wu3
1Center for Information and Educational Technology, Southwest Medical University, Luzhou, Sichuan, China.
PloS one
|February 19, 2025
概括
本研究介绍了TGF-Net,这是一种用于分类多模式遥感数据的新型深度学习模型. TGF-Net有效地从高光谱图像 (HSI) 和合成光圈雷达 (SAR) 数据中提取独特和共同的特征.
科学领域:
- 地球科学 地球科学
- 遥感是一种远程传感.
- 地理空间分析是什么?
背景情况:
- 在遥感中,表面材料的分类是具有挑战性的,特别是多模式数据.
- 深度学习显示出希望,但与复杂的多式联络远程传感数据集扎.
研究的目的:
- 提出一个新的融合网络,TGF-Net,用于增强多模式遥感数据分类.
- 解决信息冗余问题,并改进从各种遥感数据源中提取特征.
主要方法:
- 开发了TGF-Net,这是一个融合网络,结合了变压器和核心卷积神经网络 (CNN) 架构.
- 整合了一个特征重建模块 (FRM),使用矩阵分解和自我注意力进行多式模式特征评估.
- 引入了一种基于变压器的光谱特征提取模块 (TSFEM) 用于高光谱图像 (HSI) 通道序列分析.
- 提出了一种基于Gist的空间特征提取模块 (GSFEM),用于合成孔径雷达 (SAR) 图像的空间目标表示.
主要成果:
- TGF-Net有效地从多式联网遥感数据中提取了不同和共同的特征.
- 拟议的模块 (FRM,TSFEM,GSFEM) 增强了HSI和SAR数据的分类.
- 实验结果表明TGF-Net在HSI和SAR数据集上的有效性和优越性.
结论:
- TGF-Net为多模式遥感图像分类提供了一个强大的解决方案.
- 变压器和CNN方法的融合,以及专门的特征提取模块,显著提高了分类准确性.
- 这项研究提升了地球科学和远程探索领域的深度学习能力.
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