面向无人机海岸带生态系统监测的语义分割基准数据集  

Semantic segmentation benchmark dataset for coastal ecosystem monitoring based on unmanned aerial vehicle(UAV)

作　　者：王胜科 王贤栋 曲亮[2] 姚凤芹 刘莹莹[1] 李聪慧 王玉瑱 仲国强 Wang Shengke;Wang Xiandong;Qu Liang;Yao Fengqin;Liu Yingying;Li Conghui;Wang Yuzhen;Zhong Guoqiang(Faculty of Information Science and Engineering,Ocean University of China,Qingdao 266100,China;North China Sea Environmental Monitoring Center,State Oceanic Administration,Qingdao 266033,China)

机构地区：[1]中国海洋大学信息科学与工程学部,青岛266100 [2]国家海洋局北海环境监测中心,青岛266033

出　　处：《中国图象图形学报》2024年第8期2162-2174,共13页Journal of Image and Graphics

基　　金：国家重点研发计划资助(2018AAA0100400);山东省自然科学基金项目(ZR2020MF131,ZR2021ZD19);海洋科技协同创新中心项目(22-05-CXZX-04-03-17);青岛市科技惠民项目(21-1-4-ny-19-ns,24-1-8-cspz-22-nsh);中国海洋大学联合培育项目(202265007)。

摘　　要：目的海岸带生态系统的监测是维护生态平衡和可持续发展的重要任务。语义分割技术在遥感影像中的应用为海岸带生态系统的精准监测提供了有效手段。然而,目前该领域仍面临一个挑战,即缺乏公开的专门面向海岸带生态系统的语义分割基准数据集。方法受到自然环境和人为因素的共同影响,海岸带外部形态变化快,目前的卫星遥感和常规的人工测量、船载作业等监测方式已难以满足实时观测的要求。因此本文首先利用无人机对海岸带典型生态群落进行拍摄、采集和标注,构建了面向海岸带生态系统的基准数据集,名为OUC-UAV-SEG。接着,通过统计的方法,对OUC-UAV-SEG进行定量分析,并对数据集中存在的挑战进行了深入探讨。最后,使用典型视觉语义分割算法对OUC-UAV-SEG展开评估。结果对OUC-UAV-SEG数据集进行了基准测试,评估了当前可用的语义分割算法在该数据集上的性能,并揭示了这些算法的局限性。结论OUC-UAV-SEG数据集的建立为海岸带生态系统的监测提供了新的资源,有助于科学家、生态学家和决策者们更好地了解海岸带生态系统的现状和变化趋势,为海洋生态保护和管理提供更精确的信息。该数据集链接:https://github.com/OucCVLab/OUC-UAV-SEG。Objective The coastal ecosystem is a natural system composed of biological communities and their interactions with the environment,including typical coastal ecosystems such as mangroves,salt marshes,coral reefs,seagrass beds,oyster reefs,sandy shores,as well as complex ecosystems like estuaries and bays.These ecosystems play a crucial role in maintaining high-quality ecological environments and fostering rich marine biodiversity.A healthy coastal ecosystem is not only a crucial support for the sustainable economic development of China but also an essential component of the ecological security of the country.The application of semantic segmentation techniques in remote sensing imagery has provided an effective means for the precise monitoring of coastal ecosystems,which offers scientists,ecologists,and decision makers clear and highly comprehensive information to understand the current state and changing trends of coastal ecosystems.How⁃ever,a significant challenge persists,that is,a specialized,comprehensive,and fine-grained data support system for coastal ecosystems is lacking,which causes difficulty in accurately understanding the distribution,area,and changes in ecosystems such as salt marshes,seagrass beds,and reed beds.This challenge has become a pressing issue in the current national marine ecological conservation efforts.Currently,the monitoring of coastal ecosystems relies primarily on satellite remote sensing and traditional surveying methods.Satellite remote sensing,with its unique advantages of all-weather,allday,large-scale,and long-time observation,is widely used for monitoring marine ecology and resources through the analy⁃sis of satellite data.However,the spatial resolution of satellite remote sensing images has limitations,which introduces errors in cases that require fine-scale monitoring.For example,in narrow rivers,small wetland areas,or islands,the lim⁃ited spatial resolution may result in unclear visibility of small features in the images.Traditional surveying methods often require prof

关 键 词：海岸带生态系统 遥感 无人机(UAV) 基准数据集 语义分割 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]