结合SAM视觉分割模型与随机森林机器学习的无人机影像盐沼植被“精灵圈”提取  

Extraction of salt-marsh vegetation “fairy circles” from UAV images by the combination of SAM visual segmentation model and random forest machine learning algorithm

作　　者：周若彤 谭凯 杨建儒 韩江涛[1] 张卫国[1] Zhou Ruotong;Tan Kai;Yang Jianru;Han Jiangtao;Zhang Weiguo(State Key Laboratory of Estuarine and Coastal Research,East China Normal University,Shanghai 200241,China)

机构地区：[1]华东师范大学河口海岸学国家重点实验室,上海200241

出　　处：《海洋学报》2024年第5期116-126,共11页

基　　金：国家自然科学基金项目(4217010220,41901399);上海市自然科学基金项目(22ZR1420900);重庆市自然科学基金项目(CSTB2022NSCQMSX1254);测绘遥感信息工程湖南省重点实验室开放基金项目(E22335);上海市科委社发研究项目(20DZ1204700)。

摘　　要：“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素在色彩信息和外形特征上差异较小,如何从二维影像中智能精准地识别“精灵圈”像素并对识别的单个像素形成个体“精灵圈”是目前的技术难点。本文提出了一种结合分割万物模型(Segment Anything Model,SAM)视觉分割模型与随机森林机器学习的无人机影像“精灵圈”分割及分类方法,实现了单个“精灵圈”的识别和提取。首先,通过构建索伦森-骰子系数(S?rensen-Dice coefficient,Dice)和交并比(Intersection over Union,IOU)评价指标,从SAM中筛选预训练模型并对其参数进行优化,实现全自动影像分割,得到无属性信息的分割掩码/分割类;然后,利用红、绿、蓝(RGB)三通道信息及空间二维坐标将分割掩码与原图像进行信息匹配,构造分割掩码的特征指标,并根据袋外数据(Out of Bag,OOB)误差减小及特征分布规律对特征进行分析和筛选;最后,利用筛选的特征对随机森林模型进行训练,实现“精灵圈”植被、普通植被和光滩的自动识别与分类。实验结果表明:本文方法“精灵圈”平均正确提取率96.1%,平均错误提取率为9.5%,为精准刻画“精灵圈”时空格局及海岸带无人机遥感图像处理提供了方法和技术支撑。The “fairy circle” represents a unique form of spatial self-organization found within coastal salt marsh ecosystems,profoundly influencing the productivity,stability,and resilience of these wetlands.Unmanned Aerial Vehicle(UAV) imagery plays a pivotal role in precisely pinpointing the “fairy circle” locations and deciphering their temporal and spatial development trends.However,identifying “fairy circle” pixels within two-dimensional images poses a considerable technical challenge due to the subtle differences in color and shape characteristics between these pixels and their surroundings.Therefore,intelligently and accurately identify “fairy circle” pixels from two-dimensional images and form individual “fairy circle” for the identified pixels were the current technical difficulties.This paper introduced an innovative approach to extract “fairy circle” from UAV images by integrating the SAM(Segment Anything Model) visual segmentation model with random forest machine learning.This novel method accomplished the recognition and extraction of individual “fairy circle” through a two-step process:segmentation followed by classification.Initially,we established Dice(S?rensen-Dice coefficient) and IOU(Intersection Over Union) evaluation metrics,and optimize SAM's pre-trained model parameters,which produced segmentation mask devoid of attribute information by fully automated image segmentation.Subsequently,we aligned the segmentation mask with the original image,and utilized RGB(red,green,and blue) color channels and spatial coordinates to construct a feature index for the segmentation mask.These features underwent analysis and selection based on Out-of-Bag(OOB) error reduction and feature distribution patterns.Ultimately,the refined features were employed to train a random forest model,enabling the automatic identification and classification of “fairy circle”vegetation,common vegetation,and bare flat areas.The experimental results show that the average correct extraction rate of “fairy c

关 键 词：盐沼植被 精灵圈 segment anything model(SAM) 无人机影像 机器学习 

分 类 号：P237[天文地球—摄影测量与遥感]