LKAFFNet:一个新的大型核心注意力特征融合网络,用于土地覆盖区分
Bochao Chen1, An Tong1, Yapeng Wang1
1Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
一个新的框架,LKAFFNet,在遥感中改善了土地覆盖的细分. 它有效地平衡了当地细节和上下文信息,在基准数据集上表现优于现有的模型.
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:
- 准确的土地覆盖细分对于城市规划,环境监测和灾害管理至关重要.
- 传统的卷积神经网络 (CNN) 在高分辨率图像中面临着将本地细节与大规模上下文集成的挑战.
研究的目的:
- 开发一个新的框架,LKAFFNet,通过解决传统CNN的局限性来增强土地覆盖的细分.
- 改善微粒度局部特征与远程传感图像分析中的广泛上下文信息之间的平衡.
主要方法:
- 引入了LKAFFNet,这是一个结合大型内核卷积,注意力机制和多尺度特征融合的框架.
- 开发了三个关键模块:LkResNet以大内核卷积增强特征提取,大内核注意聚合 (LKAA) 集成空间和通道注意,以及通道差异特征转移融合 (CDFSF) 以实现高效的多尺度融合.
主要成果:
- 与之前的模型相比,LKAFFNet在LandCover和WHU建筑数据集上表现出更高的性能.
- 在LandCover数据集上获得了0.8155的平均交叉点 (mIoU),在WHU建筑数据集上达到0.9326的平均交叉点.
- 该框架在将土地覆盖面分为不同规模的细分方面表现特别有效.
结论:
- 在高分辨率遥感图像中,LKAFFNet显著提高了土地覆盖细分精度.
- 拟议的框架为各种需要精确地分类土地覆盖的遥感应用提供了更有效的工具.
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