A Dense Feature Iterative Fusion Network for Extracting Building Contours from Remote Sensing Imagery  

遥感图像中提取建筑物轮廓的密集特征迭代融合网络

作　　者：WU Jiangyan WANG Tong 吴江炎;王彤(东华大学信息科学与技术学院,上海201620;东华大学数字化纺织服装技术教育部工程研究中心,上海201620)

机构地区：[1]College of Information Science and Technology,Donghua University,Shanghai 201620,China [2]Engineering Research Center of Digitized Textile&Apparel Technology,Ministry of Education,Donghua University,Shanghai 201620,China

出　　处：《Journal of Donghua University(English Edition)》2024年第6期654-661,共8页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.61903078);Fundamental Research Funds for the Central Universities,China(No.2232021A-10);Shanghai Sailing Program,China(No.22YF1401300);Natural Science Foundation of Shanghai,China(No.20ZR1400400)。

摘　　要：Extracting building contours from aerial images is a fundamental task in remote sensing.Current building extraction methods cannot accurately extract building contour information and have errors in extracting small-scale buildings.This paper introduces a novel dense feature iterative(DFI)fusion network,denoted as DFINet,for extracting building contours.The network uses a DFI decoder to fuse semantic information at different scales and learns the building contour knowledge,producing the last features through iterative fusion.The dense feature fusion(DFF)module combines features at multiple scales.We employ the contour reconstruction(CR)module to access the final predictions.Extensive experiments validate the effectiveness of the DFINet on two different remote sensing datasets,INRIA aerial image dataset and Wuhan University(WHU)building dataset.On the INRIA aerial image dataset,our method achieves the highest intersection over union(IoU),overall accuracy(OA)and F 1 scores compared to other state-of-the-art methods.从航空图像中提取建筑物轮廓是遥感技术的一项基本任务。目前的建筑物提取方法无法准确提取建筑物轮廓信息,而且在提取小规模建筑物时存在误差。该文提出了DFINet,一种用于提取建筑物轮廓的新型密集特征迭代融合网络。该网络使用密集特征迭代解码器融合不同尺度的语义信息,并学习建筑轮廓知识,通过迭代融合产生最后的特征。密集特征融合模块结合了多个尺度的特征,利用轮廓重构模块获取最终预测结果。大量实验验证了DFINet在两个不同遥感数据集(INRIA航空图像数据集和武汉大学建筑数据集)上的有效性。在INRIA航空图像数据集上,与目前其他方法相比,DFINet在重叠度(intersection over union,IoU)、总体准确率(overall accuracy,OA)和F_(1)值等指标上取得了最高分。

关 键 词：remote sensing image building contour extraction feature iteration 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]