高分二号影像树种识别及龄组划分
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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。
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.
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.
引文
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[18]白金婷.结合高分辨率遥感影像多维特征的森林分类[D].北京:北京林业大学,2016.Bai J T. The Forest Classification Combining Multidimensional Features Based on High-resolution Remote Sensing Images[D]. Beijing:Beijing Forestry University,2016.
[19]李光,姜春雪,刘争战,等. Laws纹理能量结合灰度共生矩阵的遥感影像面状地物提取[J].测绘与空间地理信息,2017,40(7):179-181.Li G,Jiang C X,Liu Z Z,et al. Polygon feature extraction of remote sensing image based on Laws texture energy and gray level co-occurrence matrix[J]. Geomatics and Spatial Information Technology,2017,40(7):179-181.
[20] Shahshahani B M,Landgrebe D. The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon[J]. IEEE Transactions on Geoscience and Remote Sensing,1994,32(5):1087-1095.
[21]袁梅宇.数据挖掘与机器学习[M].北京:清华大学出版社,2014.Yuan M Y. Data Mining and Machine Learning[M]. Beijing:Tsinghua University Press,2014.
[22]方匡南,吴见彬,朱建平,等.随机森林方法研究综述[J].统计与信息论坛,2011,26(3):32-38.Fang K N,Wu J B,Zhu J P,et al. A review of technologies on random forests[J]. Statistics and Information Forum,2011,26(3):32-38.
[23]曹正凤.随机森林算法优化研究[D].北京:首都经济贸易大学,2014.Cao Z F. Study on Optimization of Random Forests Algorithm[D].Beijing:Capital University of Economics and Business,2014.
[24] Rodriguez-Galiano V F,Ghimire B,Rogan J,et al. An assessment of the effectiveness of a random forest classifier for land-cover classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2012,67(1):93-104.
[25] Gislason P O,Benediktsson J A,Sveinsson J R. Random forests for land cover classification[J]. Pattern Recognition,2006,27(4):294-300.
[26]吕杰,汪康宁,李崇贵,等.基于小波变换和随机森林的森林类型分类研究[J].西北林学院学报,2016,31(6):264-267.Lyu J,Wang K N,Li C G,et al. Classification of forest types based on discrete wavelet transform and random forests from GF-1 images[J]. Journal of Northwest Forestry University,2016,31(6):264-267.
[27]张晓羽,李凤日,甄贞,等.基于随机森林模型的陆地卫星-8遥感影像森林植被分类[J].东北林业大学学报,2016,44(6):53-57.Zhang X Y,Li F R,Zhen Z,et al. Forest vegetation classification of Landsat-8 remote sensing image based on random forests model[J]. Journal of Northeast Forestry University,2016,44(6):53-57.
[28]米爱中,张盼.一种基于混淆矩阵的分类器选择方法[J].河南理工大学学报(自然科学版),2017,36(2):116-121.Mi A Z,Zhang P. A method of classifier selection based on confusion matrix[J]. Journal of Henan Polytechnic University(Natural Science),2017,36(2):116-121.
[29] Dalponte M,Bruzzone L,Gianelle D. Tree species classification in the Southern Alps with very high geometrical resolution multispectral and hyperspectral data[C]//2011 3rd Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing(WHISPERS),2011:1-4.
[30]陈玲,郝文乾,高德亮.光学影像纹理信息在林业领域的最新应用进展[J].北京林业大学学报,2015,37(3):1-12.Chen L,Hao W Q,Gao D L. The latest applications of optical image texture in forestry[J]. Journal of Beijing Forestry University,2015,37(3):1-12.
[2] Hall R J,Skakun R S,Arsenault E J,et al. Modeling forest stand structure attributes using Landsat ETM+data:Application to mapping of aboveground biomass and stand volume[J]. Forest Ecology and Management,2006,225(1-3):378-390.
[3] Castillo-Santiago M A,Ricker M,Jong B H J D. Estimation of tropical forest structure from SPOT-5 satellite images[J]. International Journal of Remote Sensing,2010,31(10):2767-2782.
[4] Wolter P T,Townsend P A,Sturtevant B R. Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data[J]. Remote Sensing of Environment,2009,113(9):2019-2036.
[5] Immitzer M,Atzberger C,Koukal T. Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data[J]. Remote Sensing,2012,4(9):2661-2693.
[6] Pu R,Landry S. A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species[J]. Remote Sensing of Environment,2012,124(9):516-533.
[7]刘怀鹏,安慧君,王冰,等.基于递归纹理特征消除的WorldView-2树种分类[J].北京林业大学学报,2015,37(8):53-59.Liu H P,An H J,Wang B,et al. Tree species classification using WorldView-2 images based on recursive texture feature elimination[J]. Journal of Beijing Forestry University,2015,37(8):53-59.
[8]王妮,彭世揆,李明诗.基于树种分类的高分辨率遥感数据纹理特征分析[J].浙江农林大学学报,2012,29(2):210-217.Wang N,Peng S K,Li M S. High-resolution remote sensing of textural images for tree species classification[J]. Journal of Zhejiang Agricultural and Forestry University,2012,29(2):210-217.
[9] Franklin S E,Wulder M A,Gerylo G R. Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia[J]. International Journal of Remote Sensing,2001,22(13):2627-2632.
[10] Kim S R,Lee W K,Kwak D A,et al. Forest cover classification by optimal segmentation of high resolution satellite imagery[J]. Sensors,2011,11(2):1943.
[11] Rodriguez-Galiano V F,Chica-Olmo M,Abarca-Hernandez F,et al. Random forest classification of mediterra-nean land cover using multi-seasonal imagery and multi-seasonal texture[J].Remote Sensing of Environment,2012,121(138):93-107.
[12] Carleer A,Wolff E. Exploitation of very high resolution satellite data for tree species identification[J]. Photogrammetric Engineering and Remote Sensing,2004,70(1):135-140.
[13]陈旭,徐佐荣,余世孝.基于对象的QuickBird遥感图像多层次森林分类[J].遥感技术与应用,2009,24(1):22-26.Chen X,Xu Z R,Yu S X. Multi-level forest classification of QuickBird remote sensing image based on objects[J]. Remote Sensing Technology and Application,2009,24(1):22-26.
[14]潘腾.高分二号卫星的技术特点[J].中国航天,2015(1):3-9.Pan T. The technical features of the GF-2 satellite[J]. Aerospace China,2015(1):3-9.
[15]张过,李扬,祝小勇,等.有理函数模型在光学卫星影像几何纠正中的应用[J].航天返回与遥感,2010,31(4):51-57.Zhang G,Li Y,Zhu X Y,et al. Application of RFM in geometric rectification of optical satellite image[J]. Spaceraft Recovery and Remote Sensing,2010,31(4):51-57.
[16]孙攀,董玉森,陈伟涛,等.高分二号卫星影像融合及质量评价[J].国土资源遥感,2016,28(4):108-113. doi:10. 6046/gtzyyg. 2016. 04. 17.Sun P,Dong Y S,Chen W T,et al. Research on fusion of GF-2imagery and quality evaluation[J]. Remote Sensing for Land and Resources,2016,28(4):108-113. doi:10. 6046/gtzyyg. 2016.04. 17.
[17]张莹,张晓丽,王书涵,等.福建将乐林场主要树种冠层光谱反射特征分析[J].西北农林科技大学学报(自然科学版),2016,44(2):83-89.Zhang Y,Zhang X L,Wang S H,et al. Spectral reflectance characteristics of canopies of main tree species in Jiangle forest farm in Fujian[J]. Journal of Northwest Agricultural and Forestry University(Natural Science Edition),2016,44(2):83-89.
[18]白金婷.结合高分辨率遥感影像多维特征的森林分类[D].北京:北京林业大学,2016.Bai J T. The Forest Classification Combining Multidimensional Features Based on High-resolution Remote Sensing Images[D]. Beijing:Beijing Forestry University,2016.
[19]李光,姜春雪,刘争战,等. Laws纹理能量结合灰度共生矩阵的遥感影像面状地物提取[J].测绘与空间地理信息,2017,40(7):179-181.Li G,Jiang C X,Liu Z Z,et al. Polygon feature extraction of remote sensing image based on Laws texture energy and gray level co-occurrence matrix[J]. Geomatics and Spatial Information Technology,2017,40(7):179-181.
[20] Shahshahani B M,Landgrebe D. The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon[J]. IEEE Transactions on Geoscience and Remote Sensing,1994,32(5):1087-1095.
[21]袁梅宇.数据挖掘与机器学习[M].北京:清华大学出版社,2014.Yuan M Y. Data Mining and Machine Learning[M]. Beijing:Tsinghua University Press,2014.
[22]方匡南,吴见彬,朱建平,等.随机森林方法研究综述[J].统计与信息论坛,2011,26(3):32-38.Fang K N,Wu J B,Zhu J P,et al. A review of technologies on random forests[J]. Statistics and Information Forum,2011,26(3):32-38.
[23]曹正凤.随机森林算法优化研究[D].北京:首都经济贸易大学,2014.Cao Z F. Study on Optimization of Random Forests Algorithm[D].Beijing:Capital University of Economics and Business,2014.
[24] Rodriguez-Galiano V F,Ghimire B,Rogan J,et al. An assessment of the effectiveness of a random forest classifier for land-cover classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2012,67(1):93-104.
[25] Gislason P O,Benediktsson J A,Sveinsson J R. Random forests for land cover classification[J]. Pattern Recognition,2006,27(4):294-300.
[26]吕杰,汪康宁,李崇贵,等.基于小波变换和随机森林的森林类型分类研究[J].西北林学院学报,2016,31(6):264-267.Lyu J,Wang K N,Li C G,et al. Classification of forest types based on discrete wavelet transform and random forests from GF-1 images[J]. Journal of Northwest Forestry University,2016,31(6):264-267.
[27]张晓羽,李凤日,甄贞,等.基于随机森林模型的陆地卫星-8遥感影像森林植被分类[J].东北林业大学学报,2016,44(6):53-57.Zhang X Y,Li F R,Zhen Z,et al. Forest vegetation classification of Landsat-8 remote sensing image based on random forests model[J]. Journal of Northeast Forestry University,2016,44(6):53-57.
[28]米爱中,张盼.一种基于混淆矩阵的分类器选择方法[J].河南理工大学学报(自然科学版),2017,36(2):116-121.Mi A Z,Zhang P. A method of classifier selection based on confusion matrix[J]. Journal of Henan Polytechnic University(Natural Science),2017,36(2):116-121.
[29] Dalponte M,Bruzzone L,Gianelle D. Tree species classification in the Southern Alps with very high geometrical resolution multispectral and hyperspectral data[C]//2011 3rd Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing(WHISPERS),2011:1-4.
[30]陈玲,郝文乾,高德亮.光学影像纹理信息在林业领域的最新应用进展[J].北京林业大学学报,2015,37(3):1-12.Chen L,Hao W Q,Gao D L. The latest applications of optical image texture in forestry[J]. Journal of Beijing Forestry University,2015,37(3):1-12.