多模式四边形代表网络用于多源远程传感数据分类数据分类
IEEE transactions on neural networks and learning systems
|September 24, 2025
概括
这项研究引入了一种多模式四边子表示网络 (MMQRN),用于分类超光谱图像 (HSI) 和光检测和距离 (LiDAR) 数据. 该MMQRN有效地融合了多源遥感数据,提高了分类准确性.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 数据融合数据融合
背景情况:
- 高光谱图像 (HSI) 和光检测和距离 (LiDAR) 数据的有效整合对于地球观测至关重要.
- 挑战包括信息利用不足和多源遥感 (RS) 数据的特征异质.
研究的目的:
- 为增强多源RS数据分类提出一种新型的多式四元体表示网络 (MMQRN).
- 为改善地球观测解决特征融合和利用方面的局限性.
主要方法:
- 开发了一种多模式四边形表示 (MMQR) 来建模互补特征之间的复杂非线性相互作用.
- 设计了一种多式联通功能交叉融合 (MFCF) 框架,用于整合多源,多式联通和多级别功能.
- 使用四卷积变压器网络 (QCTN) 来捕获全球和本地空间光谱信息.
主要成果:
- 拟议的MMQRN与现有的最先进的分类方法相比,表现优越.
- 在三个多源RS数据集上的实验验验证了MMQRN方法的有效性.
结论:
- 该MMQRN有效地融合了HSI和LiDAR数据,克服了特征异质性和信息利用方面的挑战.
- 这个网络为地球观测的多源遥感数据分类提供了重大进展.
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