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功能从基于多值二进制的多光谱遥感图像中提取特征

Bohdan Rusyn^1,2, Oleksiy Lutsyk³, Rostyslav Kosarevych¹

¹Department of Remote Sensing Information Technologies, Karpenko Physico-Mechanical Institute, NAS of Ukraine, Lviv, Ukraine.

Scientific reports

|November 11, 2023

概括

这项研究引入了多门二元化,以实现更快的遥感图像分类. 与深度CNN模型相比,新方法提高了小型数据集的准确性,并大大减少了训练和推断时间.

科学领域:

计算机科学计算机科学
遥感遥感遥感遥感
机器学习机器学习

背景情况:

由于计算需求,深度卷积神经网络 (CNN) 在实时应用中面临限制.
遥感图像分类需要有效的模型来及时分析.

研究的目的:

为实时遥感图像分类提出一个高效的解决方案.
为了克服深度CNN模型的计算局限性.

主要方法:

在多光谱遥感图像上使用多值二元化.
为分类提取歧视性特征.
将拟议的方法与ResNet和Ensemble CNN模型在准确性和训练时间方面进行比较.

主要成果:

拟议的方法在小数据集上显示出卓越的准确性.
它在更大的数据集上保持了与深度CNN模型相比较的回忆得分.
在所有数据集大小中实现了约5倍的培训和推断时间.

结论:

多值二元化为远程传感图像分类提供了一个计算效率高的替代方案.
该方法平衡了准确性和显著的速度改进,使其适合实时应用.

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