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国土资源遥感  2019, Vol. 31 Issue (2): 118-124    DOI: 10.6046/gtzyyg.2019.02.17
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
高分二号影像树种识别及龄组划分
傅锋1,王新杰1(),汪锦1,王娜2,佟济宏1
1.北京林业大学林学院,北京 100083
2.北京林业大学生物科学与技术学院,北京 100083
Tree species and age groups classification based on GF-2 image
Feng FU1,Xinjie WANG1(),Jin WANG1,Na WANG2,Jihong TONG1
1.College of Forestry, Beijing Forestry University, Beijing 100083, China
2.College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
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摘要 

以福建将乐国有林场为研究区,探索高分二号(GF-2)影像在树种识别及龄组划分上的潜力。实测研究区主要树种的冠层光谱曲线,分析不同树种在光谱上的反射差异。在影像预处理后结合归一化植被指数(normalized difference vegetation index,NDVI)和地形因子构建多波段遥感影像,采用面向对象的多尺度分割,提取光谱和纹理属性并进行属性筛选; 然后,基于光谱、纹理和辅助数据不同组合的7种分类方案,采用随机森林法对研究区马尾松、毛竹及杉木3个龄组进行分类,定量分析光谱、纹理和辅助数据在树种分类中的作用。结果表明,光谱结合4方向纹理方案的总体分类精度为87.4%,Kappa系数为0.85,马尾松、毛竹和杉木各龄组得到有效分类; 在最优属性集下随机森林分类器能达到较好的分类效果。研究可为GF-2影像应用于南方集体林区森林资源调查和管理提供借鉴。

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傅锋
王新杰
汪锦
王娜
佟济宏
关键词 高分二号(GF-2)树种分类面向对象随机森林法    
Abstract

With the Jiangle state-owned forest farm of Fujian Province as the study area, the potential of classification in tree species and age groups through GF-2 image were explored. First, the canopy spectral curve of main tree species were measured and the reflectance differences between them were analyzed. After image preprocessing and in combination with normalized difference vegetation index (NDVI) and topographic factors, multi band remote sensing images were constructed. Object-oriented multi-scale segmentation technology was applied to extracting the spectral and texture attributes, followed by attributes filter. On the basis of 7 kinds of schemes, Cunninghamia lanceolata (3 age groups),Pinus massoniana and Phyllostachys edulis were classified by random forest classifier. The role of spectrum, texture and auxiliary data in classification was quantitatively analyzed. The results show that the scheme of spectra combined with 4 directions of texture attributes has overall accuracy of 87.4% with Kappa coefficient being 0.85, and age groups in Cunninghamia lanceolate were effectively classified. Random forest classifier can achieve better classification results based on the optimal attribute set. GF-2 has great potential in tree species and age group classification and provides reliable data source for forest resources investigation and management.

Key wordsGF-2    tree species classification    object-oriented    random forest
收稿日期: 2018-01-16      出版日期: 2019-05-23
ZTFLH:  TP701  
基金资助:国家重点研发计划项目“东北天然次生林抚育更新技术研究与示范”资助(2017YFC050410101)
通讯作者: 王新杰     E-mail: xinjiew@bjfu.edu.cn
作者简介: 傅 锋(1990-),男,硕士研究生,主要从事森林资源监测研究。Email: 425239289@qq.com。
引用本文:   
傅锋,王新杰,汪锦,王娜,佟济宏. 高分二号影像树种识别及龄组划分[J]. 国土资源遥感, 2019, 31(2): 118-124.
Feng FU,Xinjie WANG,Jin WANG,Na WANG,Jihong TONG. Tree species and age groups classification based on GF-2 image. Remote Sensing for Land & Resources, 2019, 31(2): 118-124.
链接本文:  
http://www.gtzyyg.com/CN/10.6046/gtzyyg.2019.02.17      或      http://www.gtzyyg.com/CN/Y2019/V31/I2/118
类型 属性 参与
波段
纹理
方向
属性
数量
光谱 最小像元值(minimum pixel value,MIN) Blue,Green,Red,NIR,NDVI 25
最大像元值(maximum pixel value)
像元均值(mean)
像元标准差(standard deviation,SD)
像元偏度(skewness)
纹理 同质性(homogeneity,HOM) Blue,Green,Red,NIR 全方向 32
对比度(contrast,CON)
非相似性(dissimilarity,DIS)
熵(entropy,ENT)
4方向 128
纹理均值(mean,MEA)
纹理标准差(standard deviation,STD)
相关性(correlation,COR)
角二阶矩(angular second moment,ASM)
辅助数据 最小像元值(minimum pixel value,MIN) DEM,坡向(Aspect),坡度(Slope) 15
最大像元值(maximum pixel value)
像元均值(mean)
像元标准差(standard deviation,SD)
像元偏度(skewness)
Tab.1  面向对象的影像属性提取
分类方案 属性类型 属性数量
方案1 光谱+4方向纹理+辅助数据 25+128+15=168
方案2 光谱+4方向纹理 25+128=153
方案3 光谱+全方向纹理 25+32=57
方案4 光谱+辅助数据 25+15=40
方案5 光谱 5×5=25
方案6 4方向纹理 8×4×4=128
方案7 全方向纹理 8×4=32
Tab.2  不同分类方案及其属性数量
Fig.1  将乐林场5种树种(龄组)冠层光谱曲线
分类方案 最优属性子集包含的属性 最优属性数/
属性总数
随机森林参数优化
K I
1 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; HOM_0_NIR; COR_45_NIR; COR_45_Green; DIS_135_Green; mean_Aspect 10/168 5 1 200
2 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; HOM_0_NIR; COR_45_NIR; COR_45_Green; DIS_135_Green 9/153 4 100
3 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Blue; CON_All_Red; CON_All_Blue; DIS_All_NIR; DIS_All_Blue; COR_All_NIR; COR_All_Red 11/57 3 1 100
4 mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; skewness_Blue; mean_Aspect; SD_DEM 8/40 3 1 600
5 MIN_Green; mean_NDVI; mean_NIR; SD_NDVI; SD_Blue; skewness_Green; skewness_Blue 7/25 3 3 000
6 HOM_0_NIR; COR_45_NIR; COR_45_Green; DIS_135_Green; COR_135_Red; COR_135_NIR; COR_135_Blue 7/128 3 3 000
7 CON_All_Red; CON_All_Blue; DIS_All_NIR; DIS_All_Green; DIS_All_Blue; COR_All_NIR; COR_All_Red 7/32 3 100
Tab.3  不同分类方案下属性筛选及参数优化
Fig.2  7种分类方案下树种分类F精度
树种 马尾松 毛竹 杉木
幼龄林
杉木
中龄林
杉木
成熟林
马尾松 92 4 0 0 3
毛竹 2 54 9 4 0
杉木幼龄林 0 4 86 5 0
杉木中龄林 0 2 4 49 7
杉木成熟林 3 0 0 5 52
制图精度/% 94.8 84.4 86.9 77.8 83.9
用户精度/% 92.8 77.0 90.8 79.2 86.8
Tab.4  基于GF-2影像的树种分类混淆矩阵
Fig.3  面向对象的随机森林分类结果
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