MAFMamba:一个多尺度的自适应融合网络,用于高分辨率遥感图像的语义细分
Boxu Li1, Xiaobing Yang1, Yingjie Fan1
1College of Information Engineering, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
一个新的视觉Mamba网络MAFMamba通过有效处理尺度变化和平衡局部细节与全球背景来增强高分辨率遥感图像的语义细分.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 高分辨率的遥感图像对于地理空间信息的获取至关重要.
- 当前的语义细分模型在规模变化和保存细节方面扎.
- 挑战包括模糊的边界,小物体的丢失,以及模拟各种物体的外观.
研究的目的:
- 推出MAFMamba,一个多尺度的自适应融合视觉Mamba网络,用于高分辨率的遥感图像细分.
- 解决捕捉全球背景和本地细节的局限性,并建模极端尺度变化.
- 在复杂的遥感场景中提高语义细分精度和效率.
主要方法:
- 开发了一种轻量级的混合编码器,配备了自适应多尺度Mamba Block (AMMB) 和多尺度自适应融合 (MSAF),用于强大的多尺度表示.
- 在解码器中引入了全球本地特征增强Mamba (GLMamba),以平衡卷积本地特征与VSS全球依赖.
- 提出了一个多尺度交叉注意力融合 (MSCAF) 模块,以弥合编码器和解码器特征之间的语义差距.
主要成果:
- 在mIoU和mF1分数中,MAFMamba在ISPRS波茨坦和Vaihingen数据集上的最新CNN,变压器和Mamba基于的方法中表现出色.
- 该网络在高分辨率遥感图像的语义细分方面取得了卓越的准确性.
- 证明了线性计算复杂性和低内存使用量,表明高效率.
结论:
- 在远程传感图像细分中,MAFMamba有效地解决了尺度变化和局部细节与全球语义之间的权衡.
- 拟议的架构为准确和高效的地理空间信息获取提供了重大进展.
- 对于复杂的遥感场景,MAFMamba提供了一个有前途的解决方案,需要精确的对象划分.
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