田间作物杂草识别的最优遥感测量尺度
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摘要
遥感分类识别精度受测量尺度的制约。为克服现有最优测量尺度选择方法存在的问题,该文提出一种基于光谱角匹配的最优测量尺度选择方法。该方法将每个像元的光谱看作其所属地物类别参考光谱叠加混合像元与光谱变异性的净效应的总和,计算不同空间分辨率下像元光谱与其所属地物类别参考光谱的光谱角,用以衡量混合像元与光谱变异性净效应的大小,当光谱角最小时说明混合像元与光谱变异性的净效应最小,此时的遥感测量尺度即为最优尺度,并在1幅实例数据中实现了该方法,利用基于光谱角匹配的尺度选择方法得到了最优遥感测量尺度,通过试验证明在该尺度下进行分类识别时精度优于比其更大或更小的尺度,验证了本研究提出的最优空间分辨率选择方法的可靠性。将该实例数据中的目标地理实体对象化,从理论上分析了目标对象的面积和形状指数与最优遥感测量尺度之间的关系。该研究为田间作物杂草遥感识别提供了一种有效的最优测量尺度选择方法,可为当前变量作业中田间数据获取工作提供参考,对于推动遥感测量尺度选择研究也具有积极意义。
In recent years, remote sensing images obtained by different types of optical sensors from a ground platform are applied to crop-weed discrimination and serve variable-rate technology in precision agriculture. Classification accuracy in remote sensing is influenced by spatial scale, so choosing the optimal spatial scale can be helpful for field data acquisition. Influences of spatial scale on classification accuracy in remote sensing are mainly originated from two factors: one factor is mixed-pixel and the other factor is spectral variability. Both aggravated mixed pixel caused by a larger spatial scale and aggravated spectral variability caused by a smaller spatial scale will result in classification accuracy reduction in remote sensing. For geographic entities in remote sensing images have inherent spatial attribute and spectral attribute, a spatial scale exists which can minimize the net effect of both mixed-pixel and spectral variability. Under this spatial scale, pixels can have optimal spectral identifiability. An approach for the selection of optimal spatial scale using a spectral angle mapper to measure the net effect of both mixed-pixel and spectral variability was proposed for crop-weed discrimination. The basic thinking of optimal spatial scale selection based on spectral angle mapper is as follows: using the average spectra calculated from a great amount of pure pixels belonging to one kind of ground object as the reference spectra for this kind of ground object, the spectra of each pixel could be regarded as the sum of its reference spectra and the net effect of mixed-pixel and spectral variability. Then, the spectral angle between the pixel spectra under different spatial resolutions and its reference spectra might be calculated to measure the net effect of mixed-pixel and spectral variability. The pixel will have optimal spectral identifiability when the net effect is least, and in this case, the spatial scale is the optimal scale. The proposed approach was realized in one field image. The geographic entities in the image were objectified. The optimal spatial scale was 0.48 cm by using the spatial scale selection method based on a spectral angle mapper. The relationship between the area and shape indexes of the target object and its optimal spatial scale was analyzed theoretically. For other field scenes, the finding can provide a reference for optimal spatial scale selection by calculating the area and shape indexes of plant objects.
In recent years, remote sensing images obtained by different types of optical sensors from a ground platform are applied to crop-weed discrimination and serve variable-rate technology in precision agriculture. Classification accuracy in remote sensing is influenced by spatial scale, so choosing the optimal spatial scale can be helpful for field data acquisition. Influences of spatial scale on classification accuracy in remote sensing are mainly originated from two factors: one factor is mixed-pixel and the other factor is spectral variability. Both aggravated mixed pixel caused by a larger spatial scale and aggravated spectral variability caused by a smaller spatial scale will result in classification accuracy reduction in remote sensing. For geographic entities in remote sensing images have inherent spatial attribute and spectral attribute, a spatial scale exists which can minimize the net effect of both mixed-pixel and spectral variability. Under this spatial scale, pixels can have optimal spectral identifiability. An approach for the selection of optimal spatial scale using a spectral angle mapper to measure the net effect of both mixed-pixel and spectral variability was proposed for crop-weed discrimination. The basic thinking of optimal spatial scale selection based on spectral angle mapper is as follows: using the average spectra calculated from a great amount of pure pixels belonging to one kind of ground object as the reference spectra for this kind of ground object, the spectra of each pixel could be regarded as the sum of its reference spectra and the net effect of mixed-pixel and spectral variability. Then, the spectral angle between the pixel spectra under different spatial resolutions and its reference spectra might be calculated to measure the net effect of mixed-pixel and spectral variability. The pixel will have optimal spectral identifiability when the net effect is least, and in this case, the spatial scale is the optimal scale. The proposed approach was realized in one field image. The geographic entities in the image were objectified. The optimal spatial scale was 0.48 cm by using the spatial scale selection method based on a spectral angle mapper. The relationship between the area and shape indexes of the target object and its optimal spatial scale was analyzed theoretically. For other field scenes, the finding can provide a reference for optimal spatial scale selection by calculating the area and shape indexes of plant objects.
引文
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[23]Duveiller G andDefourny P.A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing[J].Remote Sensing of Environment,2010,114(11):2637-2650.
[24]Pittiglio C,Skidmore A K,De Bie C A J M,et al.A common dominant scale emerges from images of diverse satellite platforms using the wavelet transform[J].International Journal of Remote Sensing,2011,32(13):3665-3687.
[25]Obata K,Miura T,Yoshioka H.Scaling effects in area-averaged values of two-band spectral vegetation indices represented in a general form[J].Journal of Applied Remote Sensing,2012,6(1):063585.
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[28]柏延臣,王劲峰.基于特征统计可分性的遥感数据专题分类尺度效应分析[J].遥感技术与应用,2004,19(6):443-449.Bo Yanchen,Wang Jinfeng.Exploring the scale effect in thematic classification of remotely sensed data:Thestatistical separability-based method[J].Remote Sensing Technology and Application,2004,19(6):443-449.(in Chinese with English abstract)
[29]韩鹏,龚健雅,李志林.基于信息熵的遥感分类最优空间尺度选择方法[J].武汉大学学报:信息科学版,2008,33(7):676-679.Han Peng,Gong Jianya and Li Zhilin.A new approach for choice of optimal spatial scale in image classification based on entropy[J].Geomatics and Information Science of Wuhan University,2008,33(7):676-679.(in Chinese with English abstract)
[30]韩鹏,龚健雅,李志林,等.遥感影像分类中的空间尺度选择方法研究[J].遥感学报,2010,14(3):507-518.Han Peng,Gong Jianya,Li Zhilin,et al.Selection of optimal scale in remotely sensed image classification[J].Journal of Remote Sensing,2010,14(3):507-518.(in Chinese with English abstract)
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[2]Slaughter D C,Giles D K,Fennimore S A,et al.Multispectral machine vision identification of lettuce and weed seedlings for automated weed control[J].Weed Technology,2008,22(2):378-384.
[3]Swain K C,N rremark M,J rgensen R N,et al.Weed identification using an automated active shape matching(AASM)technique[J].Biosystems Engineering,2011,110(4):450-457.
[4]刘波,方俊永,刘学,等.基于成像光谱技术的作物杂草识别研究[J].光谱与光谱学分析,2010,30(7):1830-1833.Liu Bo,Fang Junyong,Liu Xue,et al.Research on crop-weed discrimination using a field imaging spectrometer[J].Spectroscopy and Spectral Analysis,2010,30(7):1830-1833.(in Chinese with English abstract)
[5]阎庆,梁栋,张东彦.基于监督局部线性嵌入算法的玉米田间杂草识别[J].农业工程学报,2013,29(14):171-177.Yan Qing,Liang Dong,Zhang Dongyan.Recognition of weed in corn field based on supervised locally linear embedding algorithm[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(14):171-177.(in Chinese with English abstract)
[6]赵川源,何东健,乔永亮.基于多光谱图像和数据挖掘的多特征杂草识别方法[J].农业工程学报,2013,29(2):192-198.Zhao Chuanyuan,He Dongjian,Qiao Yongliang.Identification method of multi-feature weed based on multi-spectral images and data mining[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(2):192-198.(in Chinese with English abstract)
[7]李先锋,朱伟兴,纪滨,等.基于图像处理和蚁群优化的形状特征选择与杂草识别[J].农业工程学报,2010,26(10):178-182.Li Xianfeng,Zhu Weixing,Ji Bin,et al.Shape feature selection and weed recognition based on image processing and ant colony optimization[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2010,26(10):178-182.(in Chinese with English abstract)
[8]沈宝国,陈树人,尹建军,等.基于颜色特征的棉田绿色杂草图像识别方法[J].农业工程学报,2009,25(6):163-167.Shen Baoguo,Chen Shuren,Yin Jianjun,et al.Image recognition of green weeds in cotton fields based on colorfeature[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2009,25(6):163-167.(in Chinese with English abstract)
[9]毛文华,曹晶晶,姜红花,等.基于多特征的田间杂草识别方法[J].农业工程学报,2007,23(11):206-209.Mao Wenhua,Cao Jingjing,Jiang Honghua,et al.In-field weed detection method based on multi-features[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2007,23(11):206-209.(in Chinese with English abstract)
[10]毛罕平,胡波,张艳诚,等.杂草识别中颜色特征和阈值分割算法的优化[J].农业工程学报,2007,23(9):154-158.Mao Hanping,Hu Bo,Zhang Yancheng,et al.Optimization of color index and threshold segmentation in weed recognition[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2007,23(9):154-158.(in Chinese with English abstract)
[11]Lam N and Quattrochi D A.On the issues of scale,resolution,and fractral analysis in the mapping sciences[J].Prof Geogr,1992,44(1):88-98.
[12]苏理宏,李小文,黄裕霞.遥感尺度问题研究进展[J].地球科学进展,2001,16(4):544-548.Su Lihong,li Xiaowen,Huang Yuxia.An review on scale in remote sensing[J].Advance In Earth Sciences,2001,16(4):544-548.(in Chinese with English abstract)
[13]Goodchild M F and Quattrochi D A.Scale,multiscaling,remote sensing and GIS[A].In:Quattrochi D A,Goodchild M F,eds.Scale in Remote Sensing and GIS Raton[C]//Boca Raton:CRC Lewis Publishers,1997.
[14]靳华安,王锦地,柏延臣,等.基于作物生长模型和遥感数据同化的区域玉米产量估算[J].农业工程学报,2012,28(13):166-171.Jin Hua’an,Wang Jindi,Bo Yanchen,et al.Estimation on regional maize yield based on assimilation of remote sensing data and crop growth model[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(13):166-171.(in Chinese with English abstract)
[15]赵虎,裴志远,马尚杰,等.WOFOST模型同化时序HJCCD数据反演叶面积指数[J].农业工程学报,2012,28(11):158-163.Zhao Hu,Pei Zhiyuan,Ma Shangjie,et al.Retrieving LAI by assimilating time series HJ CCD data with WOFOST[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(11):158-163.(in Chinese with English abstract)
[16]刘宁,孙鹏森,刘世荣,等.WASSI-C生态水文模型响应单元空间尺度的确定:以杂古脑流域为例[J].植物生态学报,2013,37(2):132-141.Liu Ning,Sun Pengsen,Liu Shirong,et al.Determination of spatial scale of response unit for the WASSI-C eco-hydrological model:A case study on the upper Zagunao River watershed of China[J].Chinese Journal of Plant Ecology,2013,37(2):132-141.(in Chinese with English abstract)
[17]温兆飞,张树清,白静,等.农田景观空间异质性分析及遥感监测最优尺度选择:以三江平原为例[J].地理学报,2012,67(3):346-356.Wen Zhaofei,Zhang Shuqing,Bai Jing,et al.Agricultural landscape spatial heterogeneity analysis and optimal scale selection:An example applied to sanjiang plain[J].Acta Geographica Sinica,2012,67(3):346-356.(in Chinese with English abstract)
[18]陈杰,邓敏,肖鹏峰,等.纹理频谱分析的高分辨率遥感影像最佳尺度选择[J].遥感学报,2011,15(3):492-511.Chen Jie,Deng Min,Xiao Pengfeng,et al.Optimal spatial scale choosing for high resolution imagery based on texture frequency analysis[J].Journal of Remote Sensing,2011,15(3):492-511.(in Chinese with English abstract)
[19]明冬萍,王群,杨建宇.遥感影像空间尺度特性与最佳空间分辨率选择[J].遥感学报,2008,12(4):529-537.Ming Dongping,Wang Qun,and Yang Jianyu.Spatial scale of remote sensing image and selection of optimal spatialresolution[J].Journal of Remote Sensing,2008,12(4):529-537.(in Chinese with English abstract)
[20]Markham B L and Townshend J R G.Land cover classification accuracy as a function of sensor spatial resolution[A].The15th International Symposium of Remote Sensing of Environment[C]//1981.
[21]Aplin P.On scales and dynamics in observing the environment[J].International Journal of Remote Sensing,2006,27(11):2123-2140.
[22]Stellmes M,Udelhoven T,Roder A,et al.Dryland observation at local and regional scale-Comparison of Landsat TM/ETM+and NOAA AVHRR time series[J].Remote Sensing of Environment,2010,114(10):2111-2125.
[23]Duveiller G andDefourny P.A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing[J].Remote Sensing of Environment,2010,114(11):2637-2650.
[24]Pittiglio C,Skidmore A K,De Bie C A J M,et al.A common dominant scale emerges from images of diverse satellite platforms using the wavelet transform[J].International Journal of Remote Sensing,2011,32(13):3665-3687.
[25]Obata K,Miura T,Yoshioka H.Scaling effects in area-averaged values of two-band spectral vegetation indices represented in a general form[J].Journal of Applied Remote Sensing,2012,6(1):063585.
[26]Woodcock CE,and Strahler A H.The factor of scale in remote sensing[J].Remote Sensing of Environment,1987(21):311-332.
[27]Atkinson P M,Aplin P.Spatial variation in land cover and choice of spatial resolution for remote sensing[J].International Journal of Remote Sensing,2004,25(18):3687-3702.
[28]柏延臣,王劲峰.基于特征统计可分性的遥感数据专题分类尺度效应分析[J].遥感技术与应用,2004,19(6):443-449.Bo Yanchen,Wang Jinfeng.Exploring the scale effect in thematic classification of remotely sensed data:Thestatistical separability-based method[J].Remote Sensing Technology and Application,2004,19(6):443-449.(in Chinese with English abstract)
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