测试科学与仪器2025，Vol.16Issue(2)：216-223,8.DOI:10.62756/jmsi.1674-8042.2025021
一种基于PCA多特征融合的EnFCM遥感图像林地提取方法
An EnFCM remote sensing image forest land extraction method based on PCA multi-feature fusion
摘要
Abstract
The traditional EnFCM(Enhanced fuzzy C-means)algorithm only considers the grey-scale features in image segmentation,resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction.An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed.Firstly,histogram equalization was applied to improve the image contrast.Secondly,the texture and edge features of the image were extracted,and a multi-feature fused pixel image was generated using the PCA technique.Moreover,the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature.Finally,an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation.The experimental results showed that the error was between 1.5%and 4.0%compared with the forested area counted by experts'hand-drawing,which could obtain a high accuracy segmentation and extraction result.关键词
图像分割/林地提取/PCA变换/多特征融合/EnFCM算法Key words
image segmentation/forest land extraction/PCA transform/multi-feature fusion/EnFCM algorithm引用本文复制引用
朱生阳,王小鹏,魏统艺,樊炜玮,宋宇博..一种基于PCA多特征融合的EnFCM遥感图像林地提取方法[J].测试科学与仪器,2025,16(2):216-223,8.基金项目
This work was supported by National Natural Science Foundation of China(No.61761027) （No.61761027）
Gansu Young Doctor's Fund for Higher Education Institutions(No.2021QB-053) （No.2021QB-053）
