CVMFusion:ConvNeXtV2和视觉曼巴融合用于远程传感细分.
Zelin Wang1, Li Qin2, Cheng Xu1
1National Key Laboratory of Complex System Control and Intelligent Agent Cooperation, Beijing 100074, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
通过结合CNN和Mamba进行详细的本地和全球分析,CVMFusion增强了远程传感图像的海岸线提取. 这种新的方法提高了陆海细分的准确性,特别是在复杂的边界和小物体.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 地理信息系统 (GIS) 是一个地理信息系统.
背景情况:
- 从高分辨率遥感图像中提取海岸线,由于复杂的细节和目标可变性,因此存在挑战.
- 现有的卷积神经网络 (CNN) 难以应对远程依赖,而变压器在计算上昂贵.
- 需要有效和准确的陆海细分方法,能够处理复杂的细节和全球背景.
研究的目的:
- 提出CVMFusion,这是一个新的陆海细分网络,旨在克服当前方法的局限性.
- 整合CNN在本地特征提取和Mamba在全球上下文建模方面的优势.
- 在海岸线开采中实现高精度,特别是在涉及小物体和复杂边界的具有挑战性的场景中.
主要方法:
- 开发了CVMFusion,这是一个具有层次组织的U形编码器解码器网络.
- 在编码器中使用并行ConvNeXtV2和VMamba分支来进行本地和全球特征捕获.
- 集成动态多尺度注意力 (DyMSA) 和动态加权交叉注意力 (DyWCA) 模块,通过跳过连接进行自适应特征融合.
主要成果:
- 在两个公共数据集上,CVMFusion实现了98.05%和96.28%的平均交叉对欧盟 (MIoU) 准确率.
- 拟议的网络性能优于现有的陆海细分方法.
- 在细分小物体和复杂的边界区域方面表现出卓越的性能.
结论:
- CVMFusion有效地融合了当地和全球特征,以精确地提取海岸线.
- 适应性融合机制和层次结构有助于提高细分性能.
- CVMFusion代表着陆海细分在高分辨率遥感图像中的重大进步.
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