融合动态特征增强的遥感建筑物分割  

Fusion of dynamic features enhances remote sensing building segmentation

作　　者：肖振久[1] 田昊 张杰浩 曲海成[1] Xiao Zhenjiu;Tian Hao;Zhang Jiehao;Qu Haicheng(School of Software,Liaoning University of Engineering and Technology,Huludao,Liaoning 125105,China)

机构地区：[1]辽宁工程技术大学软件学院,辽宁葫芦岛125105

出　　处：《光电工程》2025年第3期12-24,共13页Opto-Electronic Engineering

基　　金：辽宁省高等学校基本科研项目(LJKMZ20220699)。

摘　　要：针对遥感地物建筑物图像目标尺度差异大、样本空间分布不均衡、地物边界模糊、场景区域跨度大所导致的分割效果不佳问题,本文提出一种融合动态特征增强高精度遥感建筑物分割算法。首先,构建New_GhostNetV2网络,利用自适应上下文感知卷积,增强算法对样本空间特征的捕捉能力。其次,采用Ghost Convolution结合跳跃连接和特征分支策略设计多层级信息增强模块,增强特征整合。随后引入级联注意力CGA(cascaded group attention),通过组内独立注意力计算,加强模型对多样化地物形态的适应性。最后,通过动态深度特征增强器构造特征融合模块,进一步加强模型捕获能力。在WHU数据集上实验结果表明:改进算法较基线模型F1-Score提高8.57%,mIoU提高12.48%,与其他主流语义分割模型相比,改进DeepLabv3+具有更好的分割精度。Aiming at the poor segmentation effect caused by the large scale difference of objects,uneven spatial distribution of samples,fuzzy boundary of objects and large span of scene area,this paper proposes a high-precision remote sensing building segmentation algorithm enhanced by integrating dynamic features.Firstly,the New_GhostNetV2 network is constructed,and the adaptive context-aware convolution is used to improve the algorithm's ability to capture the features of the sample space.Secondly,multi-level information enhancement modules are designed using ghost convolution combined with skip connections and feature branching strategies to enhance the feature integration.Then CGA(cascaded group attention)is introduced to enhance the adaptability of the model to diverse ground object forms through the calculation of independent attention within the group.Finally,the feature fusion module is constructed by the dynamic depth feature enhancer to further enhance the ability of model capture.The experimental results on the WHU data set show that the improved algorithm is 8.57%higher than the baseline model F1-Score and 12.48%higher than mIoU.Compared with other mainstream semantic segmentation models,the improved DeepLabv3+has better segmentation accuracy.

关 键 词：遥感图像 语义分割 特征增强 信息整合 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]