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一个跨层功能融合框架,具有层次交互,用于远程传感变化检测

Xin Meng¹, Chuanbiao Qiu¹, Chong Liu²

¹College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

Sensors (Basel, Switzerland)

|February 27, 2026

概括

本研究介绍了一种跨层特征融合框架 (CLFF),用于改进遥感 (RS) 变化检测 (CD). 新的框架增强了多层特征融合,从而更准确,更稳定地检测了超高分辨率 (VHR) 图像中的变化.

科学领域:

遥感遥感遥感遥感
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

变化检测 (CD) 在遥感 (RS) 中至关重要,但受到VHR图像复杂性的挑战.
现有的混合变压器-CNN模型在外观变化,混乱和照明波动方面扎.
局限性包括难以建模细纹理,特征连续性和抑制假阳性.

研究的目的:

提出一个跨层特征融合框架 (CLFF),用于VHR图像中的准确和稳定的CD.
增强多层特征的协作融合,以改进更改检测.
为了解决传统的CNN在多层上下文建模中的局限性.

主要方法:

引入了多级交互感知块 (MP-Block) 用于语义特征交互.
使用多分支交互融合机制 (MIFM) 与并行重建和重新校准分支 (RFRB,ACGF).
整合了一个轻量级的位置感知注意模块来完善空间反应和抑制背景噪音.

主要成果:

在四个基准数据集 (LEVIR,WHU,SYSU,HRCUS) 中,CLFF表现出显著的性能改善.
与基线模型相比,IOU指标的改善率在1.35%至4.85%之间.
该框架有效地缓解了诸如受体场有限和多层特征相互作用不足等问题.

结论:

拟议的CLFF框架显著提高了VHR图像的变化检测准确性和稳定性.
有效的多层特征融合和相互作用是克服复杂遥感数据挑战的关键.
该方法为可靠的变化检测应用提供了有希望的进步.

关键词:

注意力机制注意力机制变化检测检测的变化检测多层次的核聚变.远程传感是一种遥感技术.非常高分辨率的非常高分辨率

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