通过共弦相似度交叉注意力融合几何和语义特征,用于远程传感场景分类
Xuefei Xu1, Chengjun Xu2,3
1School of Information Engineering, Shanghai Dianji University, Shanghai 201306, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究介绍了CBCAM-LGM,这是一个高分辨率遥感图像场景分类的高效框架. 它通过自适应地融合多层次特征来实现最先进的准确性,并显著降低了复杂性.
科学领域:
- 地球观测 地球观测
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 高分辨率遥感图像场景分类 (HRRSI-SC) 对地球表面信息至关重要,但面临诸如背景干扰和特征模糊性等挑战.
- 卷积神经网络 (CNN) 难以处理远程依赖,而视觉转换器 (ViT) 是计算密集型的,可能缺乏本地特征建模.
- 现有的方法往往无法有效地平衡全球和本地特征提取,导致HRRSI-SC.的性能不足最佳.
研究的目的:
- 为HRRSI-SC.提出一个高效和准确的跨层次特征互补分类框架,CBCAM-LGM,用于HRRSI-SC.
- 通过实现多颗粒度特征的自适应融合来解决CNN和ViT的局限性.
- 为了减少计算复杂性,同时保持高分类准确性.
主要方法:
- 一个新的跨层次双向互补注意模块 (CBCAM) 用于使用交叉查询注意力的自适应特征融合.
- 通过全球平均汇聚来减少冗余性的多颗粒度特征的蒸.
- 具有参数共享的并行扩展卷积,以有效地捕获多尺度的上下文信息.
主要成果:
- 在AID数据集上实现了97.81%的最先进的分类准确度,超过了ViT-B-16的1.63%.
- 将计算复杂度降低到1.21个GMAC,只有11237万个参数 (87%的减少).
- 在捕获多尺度上下文信息和处理特征模糊性方面表现出卓越的性能.
结论:
- 拟议的CBCAM-LGM框架为HRRSI-SC提供了一个高效和高度准确的解决方案.
- 跨层次的特征互补方法有效平衡全球和本地特征提取.
- 该模型在遥感图像分析的准确性,参数效率和计算成本方面取得了重大进展.
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