CESA-MCFormer:通过消除冗余信息来实现高光谱图像分类的高效变压器网络
Shukai Liu1, Changqing Yin1, Huijuan Zhang1
1School of Software, Tongji University, Shanghai 201800, China.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
超光谱图像分类通过新的CESA-MCFormer模型得到了改进. 这种基于变压器的方法有效地提取关键的空间和光谱特征,以最小的数据实现高精度.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 超光谱图像 (HSI) 分类由于光谱和空间冗余性而面临挑战.
- 农业和基础设施检测中的应用需要强大的特征提取.
研究的目的:
- 提出一个创新的模型,CESA-MCFormer,用于有效的HSI分类.
- 解决HSI数据中的信息冗余问题,以提高分类准确性.
主要方法:
- 基于变压器架构开发了CESA-MCFormer模型.
- 引入了中心增强空间注意力 (CESA) 模块,用于先前的空间信息.
- 嵌入式形态卷积 (MC) 用于特征提取和合并.
主要成果:
- CESA-MCFormer在IP,UP和Chikusei数据集上实现了高分类性能.
- 卡帕系数分别达到了96.38%,98.24%和99.53%.
- 该模型以最少的样本进行了精确的分类,没有PCA.
结论:
- CESA-MCFormer模型为HSI分类提供了一个强大的解决方案.
- 集成CESA和MC模块增强了特征处理能力.
- 这种方法为各种应用程序的HSI分析提供了显著的进步.
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