Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images  

作　　者：Supeng Yu Fen Huang Chengcheng Fan 

机构地区：[1]College of Artificial Intelligence,Nanjing Agricultural University,Nanjing,210095,China [2]Innovation Academy for Microsatellites of CAS,Shanghai,201210,China [3]Shanghai Engineering Center for Microsatellites,Shanghai,201210,China [4]Key Laboratory of Satellite Digital Technology,Shanghai,201210,China

出　　处：《Computers, Materials & Continua》2024年第4期549-562,共14页计算机、材料和连续体（英文）

基　　金：the National Natural Science Foundation of China(42001408,61806097).

摘　　要：Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.

关 键 词：Semantic segmentation road extraction weakly supervised learning scribble supervision remote sensing image 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]