[1]王文泉,陈永富*,李肇晨,等.基于面向对象的热带林分类方法研究[J].南京林业大学学报(自然科学版),2017,41(03):117-123.[doi:10.3969/j.issn.1000-2006.2017.03.018]
 WANG Wenquan,CHEN Yongfu*,LI Zhaochen,et al.Object-oriented classification of tropical forest[J].Journal of Nanjing Forestry University(Natural Science Edition),2017,41(03):117-123.[doi:10.3969/j.issn.1000-2006.2017.03.018]
点击复制

基于面向对象的热带林分类方法研究/HTML
分享到:

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

卷:
41
期数:
2017年03期
页码:
117-123
栏目:
研究论文
出版日期:
2017-05-31

文章信息/Info

Title:
Object-oriented classification of tropical forest
文章编号:
1000-2006(2017)03-0117-07
作者:
王文泉1陈永富1*李肇晨1洪小江2李小成2韩文涛2
1. 中国林业科学研究院资源信息研究所,北京 100091;
2.海南霸王岭国家级自然保护区管理局,海南 昌江 572722
Author(s):
WANG Wenquan1CHEN Yongfu1*LI Zhaochen1HONG Xiaojiang2LI Xiaocheng2HAN Wentao2
1.Research Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing 100091,China;
2.Hainan Bawangling National National Reserve,Changjiang 572722, China
关键词:
面向对象 热带林 信息提取 多尺度分割
Keywords:
object-oriented tropical forests information extraction multi-scale segmentation
分类号:
S757
DOI:
10.3969/j.issn.1000-2006.2017.03.018
摘要:
【目的】为了加强热带林资源的保护,采用遥感技术对热带林植被进行分类研究。【方法】基于SPOT6高分辨率遥感影像,采用ESP多尺度分割评价模型与专家知识结合的方法确定最优分割尺度参数,在分割的基础上充分挖掘目标地物的光谱、形状及纹理信息,合理选择分类特征组合,建立分类规则,构建了一套基于面向对象的热带林多尺度分类方法。【结果】与单一尺度的分类方法相比,该方法分类精度有明显提高,分类总体精度达到84.46%,并且缩短了传统目视确定最优分割参数的时间,提高了分割效率和精度。【结论】基于面向对象的多尺度分类方法能够实现高精度的热带林植被信息提取,可为遥感分类技术在热带林的应用提供参考。
Abstract:
【Objective】This study was conducted to improve tropical forests protection by assessing remote sensing-based classification technology based on remote sensing. 【Methods】Using an object-oriented classification method, tropical forests was extracted based on SPOT-6 high-resolution remote sensing images. Specifically, the estimation of scale parameter(ESP)multi-scale segmentation model, coupled with experts’ knowledge, was used to determine the optimal segmentation scale parameters. Through the analysis of the spectral, shape and texture features of the image objects, a reasonable set of these features was established. As a consequence, an object-oriented multi-scale classification method was created to map the distribution of tropical forests using classification rules. 【Results】The results showed that the proposed object-oriented multi-scale classification method had the ability to extract information about the distribution of tropical forests. This method had an overall classification accuracy of 84.46%, which was an improvement over that of single-scale classification. Moreover, less time was needed and accuracy was improved with this method, compared with the traditional segmentation method. 【Conclusion】Our object-oriented multi-scale classification method provides a solid technical reference for mapping tropical forests mapping, which is fundamental for monitoring and protection of tropical forests resources.

参考文献/References:

[1] 程燕,岳彩荣.面向对象遥感图像森林分类研究进展[J].林业调查规划,2012,37(4):19-23. DOI:10.3969/j.issn.1671-3168.2012.04.005. CHENG Y, YUE C R. Study progress on forest classification of object-oriented remote sensing images [J]. Forest Inventory and Planning, 2012,37(4):19-23.
[2] 张连华,庞勇,岳彩荣,等.TM影像决策树分类中的影响因素研究[J].林业科学研究,2014,27(1):1-5. DOI:10.13275/j.cnki.lykxyj.2014.01.001. ZHAGN L H, PANG Y, YUE C R, et al. Factorsaffecting decision tree classification method over TM image [J]. Forest Research, 2014, 27(1):1-5.
[3] 沈明霞,何瑞银,丛静华.基于ETM+遥感影像的森林植被信息提取方法研究[J].南京林业大学学报(自然科学版),2007,31(6):113-116.DOI:10.3969/j/issn.1000-2006.2007.06.027. SHEN M X, HE R Y, CONG J H. Methodological study of information extraction of forest using ETM+ and remote sensing image [J].Journal of Nanjing Forestry University(Natural Sciences Edition), 2007, 31(6):113-116.
[4] 黎良财,张晓丽, 郭航.基于SVM方法的SPOT-5影像植被分类[J].东北林业大学学报,2014,42(1):51-56. DOI:10.13759/j.cnki.dlxb.2014.01.012. LI L C, ZHANG X L,GUO H. Vegetation extraction in SPOT5 image with SVM method[J].Journal of Northeast Forestry University, 2014,42(1):51-56.
[5] 马友平.基于变差函数纹理和BP人工神经网络的QuickBird影像分类研究[J].遥感技术与应用,2010,25(4):540-546. MA Y P. The Classification research of quickBird image based on variogram texture and BP artificial neural networks [J].Remote Sensing Technology and Application, 2010, 25(4):540-546.
[6] 何诚,董志海,张思玉,等.基于决策树系统的遥感植被分类技术[J].测绘科学, 2014, 39(1):83-86. HE C, DONG Z H,ZHANG S Y, et al. Vegetation classification technology of hyperspectral remote sensing based on decision tree tool[J].Science of Surveying and Mapping, 2014, 39(1):83-86.
[7] 李春干,邵国凡.面向对象的SPOT5图像森林分类[J].林业科学,2010,46(8):130-139. DOI: 10.11707/j.1001-7488.201008020. LI C G, SHAO G F. Object-oriented classification of forest cover using SPOT5 imagery [J].Scientia Silvae Sinicae, 2010,46(8):130-139.
[8] 崔一娇,朱琳,赵力娟.基于面向对象及光谱特征的植被信息提取与分析[J].生态学报,2013,33(3):867-875. DOI: 10.5846 /stxb201204110510. CUI Y J, ZHU L, ZHAO L J. Abstraction and analysis of vegetation information based on object-oriented and spectra features [J].Acta Ecologica Sinica, 2013, 33(3):867-875.
[9] BAATA M,SCHPE A. Object-oriented and multi-scale image analysis in semantic network[C]// Proc of the 2nd International Symposium on Operationalization of Remote Sensing. Enschede, Netherlands, 1999.
[10 ]MYINT S W, GOBER P, BRAZEL A, et al. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery [J]. Remote Sensing of Environment, 2011, 115(5):1145-1161. DOI:10.1016/j.rse.2010.12.017.
[11] 周小成,庄海东,等.面向小班对象的森林资源变化遥感监测方法——以福建省厦门市为例[J].资源科学,2013,35(8):17-18. ZHOU X C, ZHUANG H D, et al. Amethod to extract forest cover change by object-oriented classification [J]. Resources Science, 2013,35(8):17-1718.
[12] 曹宝,秦其明,马海建,等.面向对象方法在SPOT5遥感图像分类中的应用——以北京市海淀区为例[J].地理与地理信息科学,2006,22(2):46-49. CAO B, QIN Q M, MA H J, et al. Application of object-oriented approach to SPOT5 image classification: a case study in Haidian District, Beijing City [J]. Geography and Geo-Information Science, 2006, 22(2):46-49.
[13] 孙晓艳,杜华强,韩凝,等.面向对象多尺度分割的SPOT5影像毛竹林专题信息提取[J].林业科学,2013,49(10):80-87. DOI: 10.11707/j.1001-7488.20131013. SUN X Y, DU H Q,HAN N, et al. Multi-scale segmentation, object-based extraction of moso bamboo forest from SPOT5 imagery[J]. Scientia Silvae Sinicae, 2013, 49(10):80-87.
[14] 郭亚鸽,于信芳,江东,等.面向对象的森林植被图像识别分类方法[J].地球信息科学学报,2012,14(4):514-522. DOI:10.3724/SP.J.1047.2012.00514. GUO Y H, YU X F, JIAGN D, et al. Study on forest classification based on object oriented techniques[J]. Journal of Geo-Information Science, 2012, 14(4):514-522.
[15] MATHIEU R, ARYAL J, CHONG A K. Object-based classification of IKONOS imagery for mapping large-scale vegetation communities in urban areas [J]. Sensors, 2007, 7(11):2860-2880.
[16] 姚成,赵晋陵.基于时序HJ-CCD影像的区域尺度水稻提取方法研究[J].南京农业大学学报,2015,38(6):1023-1029.DOI:10.7685/j.issn.1000-2030.2015.06.023. YAO C, ZHAO J L. Identifying the spatial-temporal characteristics of paddy rice using time-series HJ-CCD imagery[J]. Journal of Nanjing Agricultural University, 2015, 38(6): 1023-1029.
[17] 王荣,江东,韩惠,等.高分辨率遥感影像天然林与人工林植被覆盖信息提取[J].资源科学,2013,35(4):868-874. WANG R,JIANG D,HAN H, et al. Extractingnatural and artificial forest information based on high resolution remote sensing data[J]. Resources Science, 2013, 35(4):868-874.
[18] 李肇晨,罗微,陈永富,等.海南霸王岭陆均松空间分布格局及其与微生境异质性的关系[J].生态学报, 2015, 35(8):2545-2554. DOI:10.5846/stxb201406061165. LI Z C, LUO W, CHEN Y F, et al. The relationships between microhabitat heterogeneity and the spatial distribution of Dacrydium pectinatum in Bawangling, Hainan Island[J]. Acta Ecologica Sinica, 2015, 35(8):2545-2554.
[19] 宋晓阳,姜小三,江东,等.基于面向对象的高分影像分类研究[J].遥感技术与应用, 2015,30(1):99-105. DOI:10.11873/j.issn.1004-0323.2015.1.0099. SONG X Y, JIANG X S, JIANG D, et al. Object-orientedclassification of high-resolution remote sensing image [J]. Remote Sensing Technology and Application, 2015,30(1):99-105.
[20] 沈占锋,骆剑承,胡晓东,等.高分辨率遥感影像多尺度均值漂移分割算法研究[J].武汉大学学报(信息科学版), 2010, 35(3):313-316. DOI:10.13203/j.whugis2010.03.009 SHEN Z F, LUO J C, HU X D, et al. Amean shift multi-scale segmentation for high-resolution remote sensing images [J]. Geomatics and Information Science of Wuhan University, 2010, 35(3):313-316.
[21] 刘兆祎,李鑫慧,沈润平,等.高分辨率遥感图像分割的最优尺度选择[J].计算机工程与应用, 2014,50(6):144-147. DOI:10.3778/j.issn.1002-8331.1206-0094. LIU Z W, LI X H, SHEN R P, et al. Selection of the best segmentation scale in high-resolution image segmentation [J]. Computer Engineering and Applications, 2014,50(6):144-147.
[22] Drgu 瘙 塅 L, CSILLIK O, EISANK C, et al. Automated parameterisation for multi-scale image segmentation on multiple layers[J]. ISPRS Journal of Photogrammetry & Remote Sensing Official Publication of the International Society for Photogrammetry & Remote Sensing, 2014, 88:119-127.
[23] 王东广,肖鹏峰,宋晓群,等.结合纹理信息的高分辨率遥感图像变化检测方法[J].国土资源遥感,2012(4):76-81. DOI:10.6046/gtzyyg.2012.04.13. WANG G D, XIAO P F, SONG X Q, et al. Changedetection method for high resolution remote sensing image in association with textural and spectral information[J]. Remote Sensing for Land & Resources, 2012(4):76-81.

相似文献/References:

[1]李春干,邵国凡.面向对象森林分类的多分类器结合方法研究[J].南京林业大学学报(自然科学版),2010,34(01):073.[doi:10.3969/j.jssn.1000-2006.2010.01.016]
 LI Chun gan,SHAO Guo fan.Combination multiclassifier for objectoriented classification of forest cover[J].Journal of Nanjing Forestry University(Natural Science Edition),2010,34(03):073.[doi:10.3969/j.jssn.1000-2006.2010.01.016]

备注/Memo

备注/Memo:
收稿日期:2016-04-01 修回日期:2017-01-03
基金项目:国家自然科学基金项目(31270678)
第一作者:王文泉(wangwenquanhaha@163.com)。*通信作者:陈永富(chenyf@ifrit.ac.com),研究员,博士。
引文格式:王文泉,陈永富,李肇晨,等. 基于面向对象的热带林分类方法研究[J]. 南京林业大学学报(自然科学版),2017,41(3):117-123.
更新日期/Last Update: 2017-05-20