基于无人机影像的树种分割实践

doi:10.13466/j.cnki.lyzygl.2023.01.014

林业资源管理 ›› 2023›› Issue (1): 115-126.doi: 10.13466/j.cnki.lyzygl.2023.01.014

基于无人机影像的树种分割实践

蒲涛¹^,²^,³(), 王妮⁴(), 龚育红⁵, 王安⁵

1.安徽理工大学空间信息与测绘工程学院,安徽淮南 232001
2.安徽理工大学矿山采动灾害空天地协同监测与预警安徽普通高校重点实验室,安徽淮南 232001
3.安徽理工大学矿区环境与灾害协同监测煤炭行业工程研究中心,安徽淮南 232001
4.滁州学院地理信息与旅游学院,安徽滁州 239000
5.安徽大学资源与环境工程学院,合肥 230601

收稿日期:2022-11-18 修回日期:2023-02-01 出版日期:2023-02-28 发布日期:2023-05-05
通讯作者: 王妮(1984-),女,山东烟台人,副教授,主要研究方向为遥感信息挖掘与特征识别、GIS集成应用技术与地理信息处理与分析等。Email:wangni@chzu.edu.cn
作者简介:蒲涛(1998-),男,四川达州人,硕士研究生,主要研究方向为树种分类与制图、遥感场景分类等。Email:2021201639@Aust.edu.cn
基金资助:
高校优秀青年人才支持计划一般项目(gxyq2021217);安徽省高校自然科学项目重点项目(KJ2021A1075);安徽理工大学研究生创新基金(2022CX2163)

Tree Species Segmentation Practice based on UAV Imagery

PU Tao¹^,²^,³(), WANG Ni⁴(), GONG Yuhong⁵, WANG An⁵

1. School of Geomatics,Anhui University of Science and Technology,Huainan,Anhui 232001,China
2. Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes,Anhui University of Science and Technology,KLAHEI (KLAHEI18015),Huainan,Anhui 232001,China
3. Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring,Anhui University of Science and Technology,Huainan,Anhui 232001,China
4. School of Geographic Information and Tourism,Chuzhou University,Chuzhou,Anhui 239000,China
5. School of Resources and Environmental Engineering,Anhui University,Hefei 230601,China

Received:2022-11-18 Revised:2023-02-01 Online:2023-02-28 Published:2023-05-05

摘要/Abstract

摘要：

郁闭度超过60%的林地具有树种种类复杂、种内特征差距小及种间位置间距小等特点。虽然传统卷积神经网络(CNN)较其他分类方法具有精度高与自动化水平高等优势,但其存在学习效率低、识别精度提升困难及可解释性差等缺点。此外,传统规则分割绘制树种图的方法忽视了树种及遥感地物边界特征的变化,易在高郁闭度的林区产生椒盐现象。为解决上述问题,提出基于类激活映射及自注意力模型(ST)的新的树种分类方法(G-ST),它集成了迁移学习、ST分类模型与梯度下降的类激活映射,通过综合长距离特征、数据增强、其余领域的特征知识及预测训练关注度,提升G-ST分类精度、模型泛化能力及可解释性,结合简单线性迭代聚类方法生成树种专题地图。结果表明,该方法得到的树种图精度较传统CNN结合规则分割的制图方法更高,林木及遥感地物边界更趋近于矢量化结果,能有效为树种影像分割、制图及分布统计工作提供参考。

关键词: G-ST, 自注意力模型, 数据增强, 卷积神经网络, 迁移学习, 树种影像分割与制图

Abstract:

The characteristics of the forest land with a canopy density of more than 60% include complex tree species,a narrow interspecific character gap,and modest interspecific distances.Traditional convolutional neural networks (CNN) are superior to other classification approaches in terms of accuracy and automation,but they also have a low learning efficiency,a tough time enhancing recognition accuracy,and poor interpretability.Additionally,the typical approach of creating maps of tree species through regular segmentation overlooks changes in tree species and boundary characteristics of remote sensing objects,making it simple to create the salt and pepper phenomenon in forest areas with dense canopies.Therefore,a Grad-Swin transformer (G-ST) based on class activation mapping and Swin transformer (ST) classification model was suggested as the solution to the aforementioned issues.It incorporates transfer learning,the ST classification model,and gradient descent class activation mapping,and by integratinglong-distance features,data enhancement,feature knowledge from other fields,and prediction training attention,it increases the G-ST classification accuracy as well as the model's generalization and interpretability abilities.Thematic maps of tree species are created by using a straightforward linear iterative clustering algorithm.The results demonstrated that the accuracy of the tree species map produced by this method was greater than that of the conventional CNN combined with regular segmentation,and the boundary between trees and remote sensing objects was closer to the vectorization result.This method can therefore serve as a useful reference for tree species image segmentation,mapping,and distribution statistics.

Key words: G-ST, ST, data augmentation, CNN, transfer learning, tree species imagery segmentation and mapping

中图分类号:

TP753

蒲涛, 王妮, 龚育红, 王安. 基于无人机影像的树种分割实践[J]. 林业资源管理, 2023,(1): 115-126.

PU Tao, WANG Ni, GONG Yuhong, WANG An. Tree Species Segmentation Practice based on UAV Imagery[J]. FOREST RESOURCES WANAGEMENT, 2023,(1): 115-126.

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参考文献 20

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类别	训练集	验证集	合计
香樟叶	84	21	105
香樟(Cinnamomum camphora)	112	28	140
女贞(Ligustrum lucidum)	205	51	256
龙柏(Sabina chinensis)	140	35	175
栀子花(Gardenia jasminoides)	82	20	102
石楠叶	128	31	159
石楠(Photinia serratifolia)	109	27	136
鹤望兰(Strelitzia reginae)	80	20	100
建筑	84	20	104
月桂(Laurus nobilis)	81	20	101
月桂叶	32	7	39
马路	111	27	138
裸地	84	20	104
湖	87	21	108
合计	1419	348	1767

类别	VT模型				G-ST模型								RegNet模型
类别	Vitb/ 16	Vitb/ 32	Vitl/ 16	Vitl/ 32	Swin_ small		Swin_ base		Swin_ tiny		Swin_ large		RegNetY_ 400MF		RegNetY_ 200MF		RegNetX_ 600MF
香樟叶	0.24	0.00	0.48	0.48		0.29		0.14		0.76		0.00		0.52		0.38		0.52
香樟(Cinnamomum camphora)	0.18	0.00	0.11	0.36		0.32		032		0.25		0.75		0.79		0.71		0.68
女贞(Ligustrum lucidum)	1.00	0.22	0.94	0.98		0.78		0.98		1.00		0.96		0.90		0.92		0.86
龙柏(Sabina chinensis)	0.91	0.00	0.97	0.94		0.89		0.69		0.49		0.43		0.34		0.37		0.69
栀子花(Gardenia jasminoides)	0.05	0.15	0.05	0.00		0.00		0.00		0.00		0.00		0.15		0.25		0.20
石楠叶	0.65	0.48	0.26	0.23		0.55		0.26		0.58		0.61		0.61		0.74		0.65
石楠(Photinia serratifolia)	0.19	0.00	0.37	0.37		0.70		0.85		0.85		0.19		0.04		0.04		0.04
鹤望兰(Strelitzia reginae)	0.40	0.00	0.45	0.35		0.45		0.30		0.05		0.30		0.20		0.25		0.15
建筑	0.40	0.00	0.35	0.25		0.20		0.15		0.20		0.35		0.35		0.30		0.30
月桂(Laurus nobilis)	0.00	0.00	0.00	0.00		0.26		0.38		0.28		0.22		0.00		0.00		0.00
月桂叶	0.74	0.15	0.78	0.78		0.67		0.78		0.70		0.59		0.89		0.89		0.93
马路	0.20	0.00	0.50	0.70		0.50		0.65		0.40		0.25		0.50		0.60		0.35
裸地	0.29	0.00	0.05	0.33		0.48		0.24		0.57		0.52		0.33		0.52		0.43
湖	0.80	0.40	0.80	0.95		0.95		0.85		0.80		0.75		1.00		1.00		1.00
总分类精度	0.52	0.12	0.51	0.56		0.55		0.52		0.55		0.49		0.53		0.56		0.56
平均分类精度	0.43	0.10	0.44	0.48		0.48		0.44		0.51		0.41		0.47		0.50		0.49
Kappa系数	0.47	0.05	0.46	0.51		0.51		0.47		0.48		0.43		0.49		0.51		0.51
运行时间/s	30	25	50	28		25		30		24		40		26		23		22

类别	VT模型				G-ST模型								RegNet模型
类别	Vitb/ 16	Vitb/ 32	Vitl/ 16	Vitl/ 32	Swin_ small		Swin_ base		Swin_ tiny		Swin_ large		RegNetY_ 400MF		RegNetY_ 200MF		RegNetX_ 600MF
香樟叶	0.95	0.86	1.00	0.86		1.00		0.95		1.00		1.00		0.95		1.00		0.95
香樟(Cinnamomum camphora)	0.82	0.71	0.93	0.93		0.93		0.93		0.96		1.00		0.96		0.96		0.96
女贞(Ligustrum lucidum)	0.94	0.98	0.98	1.00		0.96		0.98		1.00		0.98		0.92		0.94		1.00
龙柏(Sabina chinensis)	0.86	0.83	0.97	0.89		0.97		1.00		1.00		1.00		1.00		1.00		1.00
栀子花(Gardenia jasminoides)	1.00	0.75	0.95	0.90		1.00		1.00		1.00		1.00		1.00		1.00		1.00
石楠叶	0.94	0.87	0.97	0.90		1.00		1.00		1.00		1.00		1.00		0.94		0.90
石楠(Photinia serratifolia)	0.74	0.85	0.89	0.78		0.85		0.96		0.85		0.93		0.96		0.93		0.93
鹤望兰(Strelitzia reginae)	0.75	0.85	1.00	0.75		1.00		1.00		1.00		1.00		1.00		1.00		0.95
建筑	0.90	0.96	0.95	0.95		0.95		1.00		1.00		1.00		1.00		1.00		1.00
月桂(Laurus nobilis)	0.00	0.00	0.00	0.00		0.86		0.43		0.71		0.714		0.13		0.00		0.14
月桂叶	0.70	0.85	1.00	0.78		1.00		1.00		0.96		1.00		1.00		1.00		1.00
马路	0.70	0.80	0.80	0.70		1.00		1.00		1.00		1.00		1.00		0.90		1.00
裸地	0.81	0.81	0.91	0.86		1.00		1.00		1.00		1.00		0.95		1.00		0.91
湖	0.95	0.90	1.00	1.00		1.00		1.00		1.00		1.00		0.95		1.00		0.85
总分类精度	0.84	0.84	0.93	0.86		0.97		0.97		0.98		0.99		0.97		0.95		0.95
平均分类精度	0.79	0.79	0.88	0.81		0.97		0.95		0.96		0.97		0.96		0.91		0.90
Kappa系数	0.82	0.82	0.93	0.85		0.97		0.97		0.98		0.98		0.97		0.95		0.94
运行时间/s	23	20	46	28		20		23		19		34		21		17		16

基于无人机影像的树种分割实践

Tree Species Segmentation Practice based on UAV Imagery

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