基于模糊多尺度特征的遥感图像分割网络  

Remote sensing image segmentation network based on fuzzy multiscale features

作　　者：李子怡 曲婷婷 崇乾鹏 徐金东 LI Ziyi;QU Tingting;CHONG Qianpeng;XU Jindong(School of Computer and Control Engineering,Yantai University,Yantai Shandong 264005,China)

机构地区：[1]烟台大学计算机与控制工程学院,山东烟台264005

出　　处：《计算机应用》2024年第11期3581-3586,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62072391,62066013)。

摘　　要：受成像距离、光照、地物特征、环境等因素影响,遥感图像中同一类别物体可能存在一定差异,而不同类别的物体反而显示相似的视觉特征,这导致在分割时存在着不确定性,即类内异质与类间模糊。为了解决此问题,提出一种用于遥感图像分割的模糊多尺度卷积神经网络(FMCNet)。该网络通过提取图像中不同尺度、大小和宽高比的感受野,充分表征遥感物体的细节信息,并利用模糊逻辑有效地表达像素与其相邻像素之间的关系,进而解决遥感图像分割中的不确定性问题。实验结果表明,FMCNet在ISPR Vaihingen和Potsdam数据集上的整体准确率(OA)分别为85.3%和86.3%,优于现有流行的语义分割方法。Affected by imaging distance,illumination,surface features,environment and other factors,objects of the same category in remote sensing images may have certain differences,while objects of different categories instead show similar visual features,which leads to uncertainty in segmentation,that is intra-class heterogeneity and inter-class ambiguity.To solve these problems,a Fuzzy Multiscale Convolutional Neural Network(FMCNet)was proposed for remote sensing image segmentation.By extracting receptive fields of different scales,sizes and aspect ratios,the detailed information in remote sensing objects was fully represented,and fuzzy logic was used to effectively express the relationship between pixels and their adjacent pixels,thus overcoming the uncertainty problem in remote sensing image segmentation.Experimental results show that the Overall Accuracy(OA)of FMCNet on ISPR Vaihingen and Potsdam datasets is 85.3%and 86.3%respectively,outperforming the existing state-of-the-art semantic segmentation methods.

关 键 词：语义分割 卷积神经网络 模糊逻辑 遥感图像 多尺度特征 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]