基于多时相GF-1 WFV和高分纹理的制种玉米田识别
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摘要
为给监管部门提供更准确的数据,及时发现非法玉米制种区域,根据不同地物在多时相光谱、高空间纹理等特征上的差异,基于163个地面样本、多源时序优选植被指数集和高空间分辨率遥感影像纹理分析的方法,进行制种玉米田识别。通过相关性分析,从GF-1 WFV多光谱影像计算的8个植被指数(VI)中确定6种,多维度反映不同作物光谱差异,并利用随机森林(RF)分类方法实现玉米田块的识别;利用玉米抽雄期的1期0. 7 m Kompsat-3全色影像,构建灰度共生矩阵(GLCM)纹理特征体系,并进行局部二值模式(Uniform-LBP)旋转不变处理,解决了影像中作物种植纹理的方向性问题,同时为体现制种玉米父母本间隔种植的特点,提出了Subtract纹理特征,进一步识别制种玉米田。以新疆维吾尔自治区奇台县为研究区,对本文提出的方法进行实例验证,试验结果表明,制种玉米田识别的制图精度、用户精度分别为93. 34%、99. 19%。
Accurately mastering the planting area and distribution of the seed maize field can provide more accurate data for regulatory authorities,and timely detection of illegal seed production areas.According to the differences of high temporal phase spectrum,high spatial texture and shape of features,the identification of maize seed production field was carried out based on 163 ground samples,multisource sequential optimization of vegetation index set and texture analysis of high spatial resolution remote sensing images. Through correlation analysis,six vegetation indices( VIs) of the normalized difference vegetation index( NDVI), enhance vegetable index( EVI), normalized difference water index( NDWI),triangle vegetation index( TVI),ratio vegetable index( RVI) and difference vegetation index( DVI) were identified from eight VIs reflecting different growth conditions of vegetation. And the random forest( RF) classification algorithm was used to identify the seed maize field. The gray-level cooccurrence matrix( GLCM) texture feature system was constructed by using the 0. 7 m Kompsat-3 image of tasseling stage. It contained five texture features: mean,entropy,contrast,angular second moment( ASM) and homogeneity. At the same time,Subtract texture features were proposed in order to reflect the characteristics of the intercropping of corn parents. Before constructing the texture feature system,local binary patterns( LBP) processing on the image was performed to solve the directional problem of crop planting texture in the image. The random forest was used to identify the seed maize field from maize field classification results. Qitai County in Xinjiang Uyghur Autonomous Region was taken as a research area to verify the proposed method,the results showed that the user's accuracy and mapping accuracy of the seed maize field was 99. 19% and 93. 34%,respectively. The research result can provide further technical support for the monitoring and supervision of hybrid corn farming in China.
Accurately mastering the planting area and distribution of the seed maize field can provide more accurate data for regulatory authorities,and timely detection of illegal seed production areas.According to the differences of high temporal phase spectrum,high spatial texture and shape of features,the identification of maize seed production field was carried out based on 163 ground samples,multisource sequential optimization of vegetation index set and texture analysis of high spatial resolution remote sensing images. Through correlation analysis,six vegetation indices( VIs) of the normalized difference vegetation index( NDVI), enhance vegetable index( EVI), normalized difference water index( NDWI),triangle vegetation index( TVI),ratio vegetable index( RVI) and difference vegetation index( DVI) were identified from eight VIs reflecting different growth conditions of vegetation. And the random forest( RF) classification algorithm was used to identify the seed maize field. The gray-level cooccurrence matrix( GLCM) texture feature system was constructed by using the 0. 7 m Kompsat-3 image of tasseling stage. It contained five texture features: mean,entropy,contrast,angular second moment( ASM) and homogeneity. At the same time,Subtract texture features were proposed in order to reflect the characteristics of the intercropping of corn parents. Before constructing the texture feature system,local binary patterns( LBP) processing on the image was performed to solve the directional problem of crop planting texture in the image. The random forest was used to identify the seed maize field from maize field classification results. Qitai County in Xinjiang Uyghur Autonomous Region was taken as a research area to verify the proposed method,the results showed that the user's accuracy and mapping accuracy of the seed maize field was 99. 19% and 93. 34%,respectively. The research result can provide further technical support for the monitoring and supervision of hybrid corn farming in China.
引文
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[12]张超,乔敏,刘哲,等.基于时序光谱和高分纹理分析的制种玉米田遥感识别[J/OL].农业机械学报,2018,49(5):218-225.ZHANG Chao,QIAO Min,LIU Zhe,et al. Seed maize field identification based on analysis of remote sensing timing spectrum and high resolution texture[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):218-225.http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20180525&flag=1&journal_id=jcsam. DOI:10.6041/j. issn. 1000-1298. 2018. 05. 025.(in Chinese)
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[14]杨清跃,高飞,聂青.纹理图像识别中的旋转不变性分析[J].计算机工程与应用,2010,46(33):205-207.YANG Qingyue,GAO Fei,NIE Qing. Analysis of rotation invariance in texture image recognition[J]. Computer Engineering and Applications,2010,46(33):205-207.(in Chinese)
[15]杨龙飞.基于局部二值模式和灰度共生矩阵的纹理特征提取技术研究[D].兰州:兰州大学,2016.YANG Longfei. The study on texture feature extraction method based on LBP and GLCM[D]. Lanzhou:Lanzhou University,2016.(in Chinese)
[16]王国德,张培林,任国全,等.融合LBP和GLCM的纹理特征提取方法[J].计算机工程,2012,38(11):199-201.WANG Guode,ZHANG Peilin,REN Guoquan,et al. Texture feature extraction method fused with LBP and GLCM[J].Computer Engineering,2012,38(11):199-201.(in Chinese)
[17]薛翠红,于洋,张朝,等.融合LBP与GLCM的人群密度分类算法[J].电视技术,2015,39(24):7-10.XUE Cuihong,YU Yang,ZHANG Chao,et al. Fusing LBP and GLCM for crowd density classification algorithm[J]. Video Engineering,2015,39(24):7-10.(in Chinese)
[18] TATSUMI K,YAMASHIKI Y,TORRES M,et al. Crop classification of upland fields using random forest of time-series Landsat 7 ETM+data[J]. Computers and Electronics in Agriculture,2015,115:171-179.
[19]王利民,刘佳,杨玲波,等.随机森林方法在玉米-大豆精细识别中的应用[J].作物学报,2018,44(4):569-580.WANG Limin,LIU Jia,YANG Lingbo,et al. Application of random forest method in maize-soybean accurate identification[J]. Acta Agronomica Sinica,2018,44(4):569-580.(in Chinese)
[20]王行汉,刘超群,丛沛桐,等.基于增强温度植被指数的农业旱情遥感监测[J].干旱区资源与环境,2018,32(5):165-170.WANG Xinghan,LIU Chaoqun,CONG Peitong,et al. Agriculture drought monitoring using remote sensing based on enhanced temperature vegetation dryness index[J]. Journal of Arid Land Resources and Environment,2018,32(5):165-170.(in Chinese)
[21]张超,乔敏,刘哲,等.基于无人机和卫星遥感影像的制种玉米田识别纹理特征尺度优选[J].农业工程学报,2017,33(17):98-104.ZHANG Chao,QIAO Min,LIU Zhe,et al. Texture scale analysis and identification of seed maize fields based on UAV and satellite remote sensing images[J]. Transactions of the CSAE,2017,33(17):98-104.(in Chinese)
[2]宋茜.基于GF 1/WFV和面向对象的农作物种植结构提取方法研究[D].北京:中国农业科学院,2016.SONG Qian. Object-based image analysis with machine learning algorithms for cropping pattern mapping using GF 1/WFV imagery[D]. Beijing:Chinese Academy of Agricultural Sciences,2016.(in Chinese)
[3] LW F,KNFEL P,CONRAD C. Analysis of uncertainty in multi-temporal object-based classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2015,105:91-106.
[4] HAO Pengyu,ZHENG Niu,LI Wang. Crop classification using multi-temporal HJ satellite images:case study in Kashgar,Xinjiang[C]∥Proceedings of SPIE—The International Society for Optical Engineering,2014,9260:926005-926005-7.
[5] HAO Pengyu,WANG Li,NIU Zheng,et al. The potential of time series merged from Landsat 5 TM and HJ 1 CCD for crop classification:a case study for Bole and Manas counties in Xinjiang,China[J]. Remote Sensing,2014,6(8):7610-7631.
[6] HAO Pengyu,ZHAN Yulin,WANG Li,et al. Feature selection of time series MODIS data for early crop classification using random forest:a case study in Kansas,USA[J]. Remote Sensing,2015,7(5):5347-5369.
[7] ZHAN Yulin,SHAKIR M,HAO Pengyu,et al. The effect of EVI time series density on crop classification accuracy[J].OPTIK—International Journal for Light and Electron Optics,2018,157:1065-1072.
[8]苏岫,耿杰,马晓瑞,等.基于多种植被指数信息与联合稀疏表示的红树林种类识别[J].海洋环境科学,2017,36(1):114-120.SU Xiu,GENG Jie,MA Xiaorui,et al. Mangrove species classification based on multiple vegetation index extraction and joint sparse representation[J]. Marine Environmental Science,2017,36(1):114-120.(in Chinese)
[9] JUNIOR S C A,FRANK T,RODRIGUES T,et al. Discrimination of soybean areas through images EVI/MODIS and analysis based on geo-object[J]. Revista Brasileira de Engenharia Agricola e Ambiental,2014,18(1):44-53.
[10] ZHONG L,GONG P,BIGING G S. Efficient corn and soybean mapping with temporal extendability:a multi-year experiment using Landsat imagery[J]. Remote Sensing of Environment,2014,140(1):1-13.
[11]张超,金虹杉,刘哲,等.基于GF遥感数据纹理分析识别制种玉米[J].农业工程学报,2016,32(21):183-188.ZHANG Chao,JIN Hongshan,LIU Zhe,et al. Seed maize identification based on texture analysis of GF remote sensing data[J]. Transactions of the CSAE,2016,32(21):183-188.(in Chinese)
[12]张超,乔敏,刘哲,等.基于时序光谱和高分纹理分析的制种玉米田遥感识别[J/OL].农业机械学报,2018,49(5):218-225.ZHANG Chao,QIAO Min,LIU Zhe,et al. Seed maize field identification based on analysis of remote sensing timing spectrum and high resolution texture[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):218-225.http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no=20180525&flag=1&journal_id=jcsam. DOI:10.6041/j. issn. 1000-1298. 2018. 05. 025.(in Chinese)
[13]商立群,杜亚娟. Hu矩和Zernike矩在图象识别中的应用[J].西安科技学院学报,2000,20(1):53-56.SHANG Liqun,DU Yajuan. On application of Hu moments and Zernike moments in images identification[J]. Journal of Xi'an Institute of Science and Technology,2000,20(1):53-56.(in Chinese)
[14]杨清跃,高飞,聂青.纹理图像识别中的旋转不变性分析[J].计算机工程与应用,2010,46(33):205-207.YANG Qingyue,GAO Fei,NIE Qing. Analysis of rotation invariance in texture image recognition[J]. Computer Engineering and Applications,2010,46(33):205-207.(in Chinese)
[15]杨龙飞.基于局部二值模式和灰度共生矩阵的纹理特征提取技术研究[D].兰州:兰州大学,2016.YANG Longfei. The study on texture feature extraction method based on LBP and GLCM[D]. Lanzhou:Lanzhou University,2016.(in Chinese)
[16]王国德,张培林,任国全,等.融合LBP和GLCM的纹理特征提取方法[J].计算机工程,2012,38(11):199-201.WANG Guode,ZHANG Peilin,REN Guoquan,et al. Texture feature extraction method fused with LBP and GLCM[J].Computer Engineering,2012,38(11):199-201.(in Chinese)
[17]薛翠红,于洋,张朝,等.融合LBP与GLCM的人群密度分类算法[J].电视技术,2015,39(24):7-10.XUE Cuihong,YU Yang,ZHANG Chao,et al. Fusing LBP and GLCM for crowd density classification algorithm[J]. Video Engineering,2015,39(24):7-10.(in Chinese)
[18] TATSUMI K,YAMASHIKI Y,TORRES M,et al. Crop classification of upland fields using random forest of time-series Landsat 7 ETM+data[J]. Computers and Electronics in Agriculture,2015,115:171-179.
[19]王利民,刘佳,杨玲波,等.随机森林方法在玉米-大豆精细识别中的应用[J].作物学报,2018,44(4):569-580.WANG Limin,LIU Jia,YANG Lingbo,et al. Application of random forest method in maize-soybean accurate identification[J]. Acta Agronomica Sinica,2018,44(4):569-580.(in Chinese)
[20]王行汉,刘超群,丛沛桐,等.基于增强温度植被指数的农业旱情遥感监测[J].干旱区资源与环境,2018,32(5):165-170.WANG Xinghan,LIU Chaoqun,CONG Peitong,et al. Agriculture drought monitoring using remote sensing based on enhanced temperature vegetation dryness index[J]. Journal of Arid Land Resources and Environment,2018,32(5):165-170.(in Chinese)
[21]张超,乔敏,刘哲,等.基于无人机和卫星遥感影像的制种玉米田识别纹理特征尺度优选[J].农业工程学报,2017,33(17):98-104.ZHANG Chao,QIAO Min,LIU Zhe,et al. Texture scale analysis and identification of seed maize fields based on UAV and satellite remote sensing images[J]. Transactions of the CSAE,2017,33(17):98-104.(in Chinese)
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