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

周若彤; 谭凯; 杨建儒; 韩江涛; 张卫国

doi:10.12284/hyxb2024048

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

doi: 10.12284/hyxb2024048

华东师范大学河口海岸学国家重点实验室，上海 200241

基金项目: 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（CSTB2022NSCQ-MSX1254）；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）。

详细信息

作者简介:
周若彤（2000—），女，山东省菏泽市人，主要从事海岸带遥感研究。E-mail：51253904045@stu.ecnu.edu.cn

通讯作者:
谭凯（1987—），男，湖南省娄底市人，副研究员，主要从事海岸带多源遥感及应用研究。E-mail：ktan@sklec.ecnu.edu.cn

中图分类号: P237
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出版历程
- 收稿日期: 2023-09-29
- 录用日期: 2024-03-01
- 修回日期: 2023-12-28
- 网络出版日期: 2024-03-11
- 刊出日期: 2024-05-01

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

State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China

摘要

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（Sørensen-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。
- 盐沼植被 /
- 精灵圈 /
- segment anything model (SAM) /
- 无人机影像 /
- 机器学习
Abstract: 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 (Sørensen-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 circle” is 96.1%, and the average wrong extraction rate is 9.5%, which provides methodological and technological support for the accurate depiction of the spatial and temporal pattern of “fairy circle” as well as the processing of coastal remote sensing images by UAVs.
- salt marsh vegetation /
- fairy circle /
- segment anything model (SAM) /
- unmanned Aerial Vehicle images /
- machine learning

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图 1 研究区域概况

Fig. 1 Overview of the study area

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图 2 I区域正射影像（a）、整个研究区域正射影像（b）和II区域正射影像（c）

Fig. 2 Orthophoto of Region I (a), orthophoto of the entire study area (b), and orthophoto of Region II (c)

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图 3 无人机摄像系统（a）和数据采集飞行路线（b）

Fig. 3 UAV camera system (a) and data collection flight path (b)

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图 4 研究方法流程

Fig. 4 Research method flow

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图 5 ViT 3种模型变体的IOU与Dice箱式图

Fig. 5 Box chart of IOU and Dice for the three variants of ViT model

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图 6 特征指标重要性情况

Fig. 6 Importance of the selected indicators

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图 7 位置与形态指标分布（a, b）和RGB指数指标分布（c, d）

横坐标为样本序号，纵坐标为特征值，红色星号代表掩码的类型（从左到右分别为“精灵圈”植被、普通植被和光滩），彩色折线代表特征值的分布情况

Fig. 7 Distributions of position and shape indexes (a, b), and distributions of RGB indexes (c, d)

Where the horizontal coordinate is the sample serial number, the vertical coordinate is the feature index value, the red asterisk represents the type of mask (from left to right are "fairy circle" vegetation, common vegetation, and bare mudflat), and the color line represents the distribution of feature value

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图 8 研究区I提取结果

a. 原始正射影像；b. SAM分割结果RGB显示；c. RF分类结果；d. “精灵圈”提取结果叠加原始正射影像；e. 删除特征bbox-x0、bbox-y0、IKAW和MVARI 4个特征前“精灵圈”提取结果

Fig. 8 Extraction results of Region I

a. Original orthophoto; b. RGB display of SAM segmentation results; c. random forest classification result; d. “ fairy circle” extraction result superimposed on original orthophoto; e. "fairy circle" extraction result superimposed on original orthophoto before removing the features of bbox-x0, bbox-y0, IKAW, and MVARI

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图 9 研究区II提取结果

Fig. 9 Extraction results of Region II

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表 1 RGB植被指数

Tab. 1 RGB vegetation index

指数	公式
EXG	2 × G − R − B
GCC	G/B + G + R
GRVI	(G − R)/(G + R)
IKAW	(R − B)/(R + B)
MGRVI	(G² − R²)/(G² + R²)
MVARI	(G − B)/(G + R − B)
RGBVI	(G² − B × R)/(G² + B × R)
TGI	G − (0.39 × R) − (0.61 × B)
VARI	(G − R)/(G + R − B)
VDVI	(2 × G − R − B)/(2 × G + R + B)

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表 2 “精灵圈”提取混淆矩阵，类别1、2、3分别代表“精灵圈”、背景植被和光滩

Tab. 2 “ Fairy circle” extraction confusion matrix, and categories 1, 2 and 3 represent fairy circle, background vegetation, and bare flat, respectively

研究区I（13个特征）				研究区II（13个特征）
实际类别	预测类别			实际类别	预测类别
实际类别	1	2	3	实际类别	1	2	3
1	172	11	1	1	359	4	1
2	27	157	5	2	18	35	0
3	1	1	112	3	1	1	39
正确识别率	93.5%			正确识别率	98.6%
错误识别率	14%			错误识别率	5.0%

研究区I（17个特征）				研究区II（17个特征）
实际类别	预测类别			实际类别	预测类别
实际类别	1	2	1	实际类别	1	2	3
1	170	14	0	1	354	4	1
2	35	151	3	2	31	35	0
3	3	1	110	3	3	1	39
正确识别率	92.4%			正确识别率	97.3%
错误识别率	18.3%			错误识别率	8.8%

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