多时相Landsat遥感影像相对辐射归一化方法的性能比较
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摘要
多时相遥感影像的辐射归一化操作是进行土地覆盖变化检测和图像拼接之前不可缺少的步骤,本研究基于2013年7月10日和2016年3月28日覆盖南京的Landsat 8 OLI数据,以2016年影像作为参考影像,采用基于分布的直方图匹配法和顺序转换法,与基于像元的多元变化检测法和随机森林法对影像实施相对辐射归一化操作。采用信息熵、边缘强度、空间频率、峰值信噪比、交互信息量5个客观评价指标对不同相对辐射归一化方法的性能进行了评价。结果表明:4种归一化方法处理后通过目视能看出影像空间信息保留很完整,没有破坏地物的光谱特征,再结合5个评价分析比较得出顺序转化法的归一化效果最优。研究结论可为多时相遥感影像的协同利用提供参考。
The radiation normalization operation of multi-temporal remote sensing images is an indispensable step before land cover change detection and image stitching. This study was based on the Landsat 8 OLI data covering Nanjing on July 10, 2013 and March 28, 2016, with 2016 images as reference images. The distributionbased histogram matching method and sequential conversion method were used to perform relative radiation normalization operation on images based on pixel-based multivariate change detection method and random forest method. The performance of different relative radiation normalization methods was evaluated by using 5 objective evaluation indexes: information entropy, edge intensity, spatial frequency, peak signal-to-noise ratio and interactive information. The results show that after 4 kinds of normalization methods, it can be seen through visual observation that the image spatial information remains intact and there is no spectral feature of the damaged features.Combined with 5 evaluations and comparisons, it is concluded that the normalization effect of the sequential transformation method is optimal. The research conclusions can provide reference for the collaborative use of multi-temporal remote sensing images.
The radiation normalization operation of multi-temporal remote sensing images is an indispensable step before land cover change detection and image stitching. This study was based on the Landsat 8 OLI data covering Nanjing on July 10, 2013 and March 28, 2016, with 2016 images as reference images. The distributionbased histogram matching method and sequential conversion method were used to perform relative radiation normalization operation on images based on pixel-based multivariate change detection method and random forest method. The performance of different relative radiation normalization methods was evaluated by using 5 objective evaluation indexes: information entropy, edge intensity, spatial frequency, peak signal-to-noise ratio and interactive information. The results show that after 4 kinds of normalization methods, it can be seen through visual observation that the image spatial information remains intact and there is no spectral feature of the damaged features.Combined with 5 evaluations and comparisons, it is concluded that the normalization effect of the sequential transformation method is optimal. The research conclusions can provide reference for the collaborative use of multi-temporal remote sensing images.
引文
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[22]王敏华.遥感图像融合方法的研究[D].桂林:广西师范大学,2017.
[23]肖祥元,景文博,赵海丽.基于峰值信噪比改进的图像增强算法[J].长春理工大学学报(自然科学版),2017,40(4):83-86,92.
[24]强赞霞.遥感图像的融合及应用[D].武汉:华中科技大学,2005.
[25]邵艳坡,洪友堂.遥感影像相对辐射校正的PIF方法[J].国土资源遥感,2017,29(1):7-13.
[26]黄启厅,覃泽林,曾志康.多源多时相遥感影像相对辐射归一化方法研究[J].地球信息科学学报,2016,18(5):606-614.
[27]Hong G,Zhang Y.A comparative study on radiometric normalization using high resolution satellite images[J].International Journal of Remote Sensing,2008,29(2):425-438.
[2]Chen X X,Vierling L,Deering D.A simple and effective radiometric correction method to improve landscape change detection across sensors and across time[J].Remote Sensing of Environment,2005,98(1):63-79.
[3]余晓敏,邹勤.多时相遥感影像辐射归一化方法综述[J].测绘与空间地理信息,2012,35(6):8-12.
[4]胡昌苗,张微,冯峥,等.Landsat TM/ETM+与HJ-1A/B CCD数据自动相对辐射处理及精度验证[J].遥感学报,2014,18(2):267-286.
[5]Xu Q,Hou Z Y,Tokola T.Relative radiometric correction of multi-temporal ALOS AVNIR-2 data for the estimation of forest attributes[J].ISPRS Journal of Photogrammetry and Remote Sensing,2012,68:69-78.
[6]李明诗,梅昭容.不同相对辐射归一化方法在土地覆盖变化检测中的评价[J].南京林业大学学报(自然科学版),2017,41(4):1-7.
[7]徐凯健,曾宏达,朱小波,等.基于五种大气校正的多时相森林碳储量遥感反演研究[J].光谱学与光谱分析,2017,37(11):3493-3498.
[8]陈崇成,黄方红,黄绚.自动散点控制回归技术在遥感数据辐射归一化中的应用[J].地球信息科学,2000,2(2):52-55.
[9]Canty M J,Nielsen A A,Schmidt M.Automatic radiometric normalization of multitemporal satellite im-agery[J].Remote Sensing of Environment,2004,91 (3/4):441-451.
[10]Zhang L,Yang L,Lin H,et al.Automatic relative radiometric normalization using iteratively weighted least square regression[J].International Journal of Remote Sensing,2008,29(2):459-470.
[11]Seo D K,Kim Y H,Eo Y D,et al.Generation of radiometric,phenological normalized image based on random forest regression for change detection[J].Remote Sensing,2017,9(11):1163.
[12]Yu X M,Zhan F B,Hu J X,et al.Radiometric normalization for multitemporal and multispectral high resolution satellite images using ordinal conversion[C]//18th International Conference on Geoinformatics,Beijing,China,June 18-20,2010.
[13]申佩佩.基于数据挖掘技术的航摄影像土地利用变化检测研究[D].南京:东南大学,2017.
[14]Cao B,Du Y M,Xu D Q,et al.An improved histogram matching algorithm for the removal of striping noise in optical remote sensing imagery[J].Optik,2015,126(23):4723-4730.
[15]Nelson T,Wilson H G,Boots B,et al.Use of ordinal conversion for radiometric normalization and change detection[J].International Journal of Remote Sensing,2005,26(3):535-541.
[16]Bai Y,Tang P,Hu C M.Kernel mad algorithm for relative radiometric normalization[J].ISPRS Annals of Photogrammetry,Remote Sensing and Spatial Information Sciences,2016,III-1:49-53.
[17]Zhang Y J,Yu L,Sun M W,et al.A mixed radiometric normalization method for mosaicking of high-resolution satellite imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(5):2972-2984.
[18]Luo J B,Etz S P,Gray R T,et al.Normalized Kemeny and Snell distance:a novel metric for quantitative evaluation of rank-order similarity of images[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2002,24(8):1147-1151.
[19]佴兆骏,段洪涛,朱利,等.基于环境卫星CCD数据的太湖蓝藻水华监测算法研究[J].湖泊科学,2016,28(3):624-634.
[20]曹林,徐婷,申鑫,等.集成Landsat OLI和机载Li DAR条带数据的亚热带森林生物量制图[J].遥感学报,2016,20(4):665-678.
[21]刘锟,付晶莹,李飞.高分一号卫星4种融合方法评价[J].遥感技术与应用,2015,30(5):980-986.
[22]王敏华.遥感图像融合方法的研究[D].桂林:广西师范大学,2017.
[23]肖祥元,景文博,赵海丽.基于峰值信噪比改进的图像增强算法[J].长春理工大学学报(自然科学版),2017,40(4):83-86,92.
[24]强赞霞.遥感图像的融合及应用[D].武汉:华中科技大学,2005.
[25]邵艳坡,洪友堂.遥感影像相对辐射校正的PIF方法[J].国土资源遥感,2017,29(1):7-13.
[26]黄启厅,覃泽林,曾志康.多源多时相遥感影像相对辐射归一化方法研究[J].地球信息科学学报,2016,18(5):606-614.
[27]Hong G,Zhang Y.A comparative study on radiometric normalization using high resolution satellite images[J].International Journal of Remote Sensing,2008,29(2):425-438.