基于邻近相关图像和决策树分类的森林景观变化检测
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摘要
提出一种基于邻近相关图像和决策树分类的景观变化检测方法,并将其应用于地震干扰引起的森林景观变化检测。以5·12汶川地震中遭受严重破坏的龙溪-虹口国家级自然保护区作为研究区,利用地震前后的Landsat5TM影像创建不同邻近窗口大小的邻近相关图像,结合决策树技术生成变化检测分类图。结果表明:使用邻近相关图像的变化检测精度有所提高,其中以5×5窗口创建的邻近相关图像变化检测效果最佳,总体分类精度和Kappa系数分别达到82.33%和0.8085。
A change detection model based on neighborhood correlation images(NCIs)and decision tree classification using remote sensing data was proposed,and then applied to detect forest landscape change information induced by forest disturbance.Longxi-Hongkou nature reserve which was seriously damaged in 5.12 Wenchuan Earthquake was selected as study area to verify the model,and various neighborhood configuration of correlation images were explored using bi-temporal Landsat5 TM images.Change detection maps were generated by using a machine learning decision tree(C5.0).The results shows that the accuracy of change detection results using NCIs is higher than that of result without NCI.Result with 5×5 window size is of highest accuracy among the different NCIs,and general accuracy and Kappa coefficient is 82.33% and 0.808 5 respectively.
A change detection model based on neighborhood correlation images(NCIs)and decision tree classification using remote sensing data was proposed,and then applied to detect forest landscape change information induced by forest disturbance.Longxi-Hongkou nature reserve which was seriously damaged in 5.12 Wenchuan Earthquake was selected as study area to verify the model,and various neighborhood configuration of correlation images were explored using bi-temporal Landsat5 TM images.Change detection maps were generated by using a machine learning decision tree(C5.0).The results shows that the accuracy of change detection results using NCIs is higher than that of result without NCI.Result with 5×5 window size is of highest accuracy among the different NCIs,and general accuracy and Kappa coefficient is 82.33% and 0.808 5 respectively.
引文
覃先林,陈尔学,李增元,等.2006.基于MODIS数据的森林覆盖变化监测方法研究.遥感技术与应用,21(3):178-183.
杨存建,欧晓昆,党承林,等.2000.森林植被动态变化信息的遥感检测.地球信息科学,(4):71-74.
Im J,Jensen J R.2005.A change detection model based on neighborhood correlation image analysis and decision tree classification.Remote Sensing of Environment,99(3):326-340.
Im J.2006.Neighborhood correlation image analysis for change detection using different spatial resolution imagery.Korean Journal of Remote Sensing,22(5):337-350.
Jensen J R,Cowen D J,Narumalani S,et al.1993.An evaluation of CoastWatch change detection protocol in South Carolina.Photogrammetric Engineering and Remote Sensing,59(6):1039-1046.
Jensen J R.2005.Introductory digital image processing:a remote sensing perspective.3rd ed.Upper Saddle River,NY:Prentice Hall,525.
Jin S M,Steven A S.2005.Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances:Remote Sensing of Environment,94(3):364-372.
Joy S M,Reich R M,Reynolds R T.2003.A Non-parametric Supervised Classification of Vegetation Types on the Kaibab National Forest using decision trees.International Journal of Remote Sensing,24(9):1835-1852.
Lu D,Mausel P,Brondizio E,et al.2004.Change detection techniques.International Journal of Remote Sensing,25(12):2365-2407.
Lunetta R S,Elvidge C.2000.Remote sensing change detection:environmental monitoring methods and applications.New York:Taylor and Francis,340.
Okhandiar R R,Raju P L,Bijker W.2008.Neighborhood correlation image analysis technique for change detection in forest landscape.ISPRS Proceeding,Beijing,1031-1038.
Ridd M K,Liu J.1998.A comparison of four algorithms for change detection in an urban environment.Remote Sensing of Environment,63(2):95-100.
Sean P H,Warren B C,Yang Z Q,et al.2005.Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection.Remote Sensing of Environment,97(3):301-310.
Sunil N,Deepak R M,Robert G R.2004.Change detection and landscape metrics for inferring anthropogenic processes in the greater EFMO area.Remote Sensing of Environment,91(3/4):478-489.
杨存建,欧晓昆,党承林,等.2000.森林植被动态变化信息的遥感检测.地球信息科学,(4):71-74.
Im J,Jensen J R.2005.A change detection model based on neighborhood correlation image analysis and decision tree classification.Remote Sensing of Environment,99(3):326-340.
Im J.2006.Neighborhood correlation image analysis for change detection using different spatial resolution imagery.Korean Journal of Remote Sensing,22(5):337-350.
Jensen J R,Cowen D J,Narumalani S,et al.1993.An evaluation of CoastWatch change detection protocol in South Carolina.Photogrammetric Engineering and Remote Sensing,59(6):1039-1046.
Jensen J R.2005.Introductory digital image processing:a remote sensing perspective.3rd ed.Upper Saddle River,NY:Prentice Hall,525.
Jin S M,Steven A S.2005.Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances:Remote Sensing of Environment,94(3):364-372.
Joy S M,Reich R M,Reynolds R T.2003.A Non-parametric Supervised Classification of Vegetation Types on the Kaibab National Forest using decision trees.International Journal of Remote Sensing,24(9):1835-1852.
Lu D,Mausel P,Brondizio E,et al.2004.Change detection techniques.International Journal of Remote Sensing,25(12):2365-2407.
Lunetta R S,Elvidge C.2000.Remote sensing change detection:environmental monitoring methods and applications.New York:Taylor and Francis,340.
Okhandiar R R,Raju P L,Bijker W.2008.Neighborhood correlation image analysis technique for change detection in forest landscape.ISPRS Proceeding,Beijing,1031-1038.
Ridd M K,Liu J.1998.A comparison of four algorithms for change detection in an urban environment.Remote Sensing of Environment,63(2):95-100.
Sean P H,Warren B C,Yang Z Q,et al.2005.Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection.Remote Sensing of Environment,97(3):301-310.
Sunil N,Deepak R M,Robert G R.2004.Change detection and landscape metrics for inferring anthropogenic processes in the greater EFMO area.Remote Sensing of Environment,91(3/4):478-489.
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