基于多个子带深度特征聚变的远程传感场景分类
Song Yang1,2, Huibin Wang1, Hongmin Gao1
1College of Computer and Information, Hohai University, Nanjing 211100, China.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
这项研究引入了一种新的基于离散波纹的方法,用于使用有限样本进行远程传感 (RS) 图像分类. 该方法有效地融合了深层特征,提高了分类准确性,即使每个类只有少数训练示例.
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:
- 卷积神经网络 (CNN) 在对象识别方面表现出色,但在遥感 (RS) 中与有限的标记数据作斗争.
- 标记样本不足阻碍了深度学习在RS图像处理中的实际应用.
- 在数据稀缺的情况下,现有的方法往往无法有效利用RS图像中的丰富信息.
研究的目的:
- 开发一个有效的方法,用于小样本遥感图像分类.
- 为了应对RS图像分析中标记数据不足的挑战.
- 为了改善RS图像中容易混的类别的歧视.
主要方法:
- 使用预训练的深度CNN和离散波量变换 (DWT) 来从RS图像中提取深度特征.
- 提出修改的歧视性相关性分析 (DCA),以增强基于类间距离系数的特征歧视.
- 集成来自各种频段的深度特征,使用拟议的DCA方法.
主要成果:
- 拟议的方法有效地将RS图像的多层次深度特征融合在一起.
- 实现了具有强大的区分能力的低维特征.
- 在四个基准数据集上表现出色,特别是在每班一个或两个培训样本上.
结论:
- 基于离散波段的多级深度特征融合方法显著提高了RS图像分类准确性,使用有限的样本.
- 修改后的DCA有效地区分了类似的类别,克服了RS数据分析的关键限制.
- 这种方法为实际的RS图像分类任务提供了有希望的解决方案,因为标记数据很少.
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