基于无人机遥感影像的覆膜农田面积及分布提取方法
|
摘要
针对基于无人机遥感的覆膜农田识别研究甚少的现状,该文以云南省昭通市鲁甸县为研究区,获取了研究区中地表类型复杂程度不同的2幅航空影像(复杂区影像和简单区影像)作为试验数据,利用灰度共生矩阵对原始航片影像进行纹理特征提取并选择纹理特征最佳提取参数;然后基于随机森林算法进行纹理特征重要性评价,优选纹理特征,结合原始数据进行最大似然初步分类;运用众数分析进行分类后处理;最后结合图像形态学算法与面积阈值分割法提取出了最终的覆膜农田面积及分布。通过试验结果发现,依据该文提出的方法,复杂区和简单区覆膜农田识别的总体精度、Kappa系数、产品精度、用户精度和面积误差分别达到了94.84%、0.89、92.48%、93.39%、0.38%和96.74%、0.93、97.39%、94.63%、1.95%。该文提出的融合监督分类和图像形态学算法的覆膜农田提取方法可以简单、快速的将地膜连成块,形成覆膜农田对象,进而通过面积阈值分割法获取高精度的覆膜农田分布信息。该方法可以为精准覆膜农田识别算法的发展提供参考。
Identification of plastic-mulched farmland using UAV image is quite few. This paper proposes a method of combining with texture features, image morphology algorithm and threshold segmentation algorithm to extract plastic-mulched farmland using UAV Red-Green-Blue(RGB) images. In order to test the performance of this method, this paper took Ludian County of Zhaotong City, Yunnan Province as the research area, and obtained 2 images in the research area as experimental data. The complexity of land cover type in the 2 images was different. In complex area, the main land cover types included vegetation, impervious layer(building and road), plastic-mulched farmland(mainly black plastic mulch with a small amount of white plastic mulch), and bare soil(containing the plastic residues of a previous year). In simple area, the land cover types were similar with those in complex area; however, all plastic-mulched farmland was covered by black plastic mulch and there were no plastic residues in bare soil. Firstly, we calculated the gray level co-occurrence matrix of 2 images in different window sizes(3×3, 5×5, 7×7, 9×9, 11×11, 13×13, 15×15), directions(0, 45°, 90° and 135°) and steps(1, 2 and 3) and extracted 8 texture features from each band of RGB images including mean, variance, synergy, contrast, dissimilarity, information entropy, second moment and correlation. Secondly, we combined the original RGB image with different texture features to make maximum likelihood classification and determined the best extraction parameters of the texture features by comparing the overall pixel accuracy, user accuracy and product accuracy of the plastic mulch in complex area. The best extraction parameters of texture features were the window size of 15×15, the direction of 0, and the step of 2, which were also used to extract texture features of the image in simple area. Thirdly, we selected the optimal texture combination based on importance evaluation of texture features using Random Forest Algorithm and combined them with original UAV RGB image to make maximum likelihood and get preliminary classification maps in both complex area and simple area. Fourthly, we recoded the preliminary classification maps into binary maps(1 refers to plastic mulch and 0 refers to the other land cover types) and made majority filtering to remove noises(such as the plastic residues of a previous year). Then, we used image morphology algorithms to convert the strip plastic mulch into the plastic-mulched farmland and set area threshold to extract plastic-mulched farmland distribution. The area thresholds were 35 m~2 in complex area and 500 m~2 in simple area. Finally, taking the digitized mulched farmland as references(ground truth data), the accuracy of the recognition results of mulched farmland was assessed by error matrix and area error. The results showed that the texture features extracted by the optimal parameters could greatly improve the classification accuracy. The image morphology algorithm and the threshold segmentation method could effectively extract the block-shaped plastic-mulched farmland. The overall accuracy, Kappa coefficient, product accuracy, user accuracy and area error were 94.84%, 0.89, 92.48%, 93.39%, 0.38% in complex area, and 96.74%, 0.93, 97.39%, 94.63%, 1.95% in simple area, respectively. Compared with step and direction, the size of window had greater influence on plastic mulch classification accuracy. Among 8 texture features, mean contributed most to extracting plastic mulch. The method of extracting plastic-mulched farmland based on the fusion of supervised classification and image morphology algorithm proposed in this paper can provide reference for the development of identification algorithm about plastic-mulched farmland.
Identification of plastic-mulched farmland using UAV image is quite few. This paper proposes a method of combining with texture features, image morphology algorithm and threshold segmentation algorithm to extract plastic-mulched farmland using UAV Red-Green-Blue(RGB) images. In order to test the performance of this method, this paper took Ludian County of Zhaotong City, Yunnan Province as the research area, and obtained 2 images in the research area as experimental data. The complexity of land cover type in the 2 images was different. In complex area, the main land cover types included vegetation, impervious layer(building and road), plastic-mulched farmland(mainly black plastic mulch with a small amount of white plastic mulch), and bare soil(containing the plastic residues of a previous year). In simple area, the land cover types were similar with those in complex area; however, all plastic-mulched farmland was covered by black plastic mulch and there were no plastic residues in bare soil. Firstly, we calculated the gray level co-occurrence matrix of 2 images in different window sizes(3×3, 5×5, 7×7, 9×9, 11×11, 13×13, 15×15), directions(0, 45°, 90° and 135°) and steps(1, 2 and 3) and extracted 8 texture features from each band of RGB images including mean, variance, synergy, contrast, dissimilarity, information entropy, second moment and correlation. Secondly, we combined the original RGB image with different texture features to make maximum likelihood classification and determined the best extraction parameters of the texture features by comparing the overall pixel accuracy, user accuracy and product accuracy of the plastic mulch in complex area. The best extraction parameters of texture features were the window size of 15×15, the direction of 0, and the step of 2, which were also used to extract texture features of the image in simple area. Thirdly, we selected the optimal texture combination based on importance evaluation of texture features using Random Forest Algorithm and combined them with original UAV RGB image to make maximum likelihood and get preliminary classification maps in both complex area and simple area. Fourthly, we recoded the preliminary classification maps into binary maps(1 refers to plastic mulch and 0 refers to the other land cover types) and made majority filtering to remove noises(such as the plastic residues of a previous year). Then, we used image morphology algorithms to convert the strip plastic mulch into the plastic-mulched farmland and set area threshold to extract plastic-mulched farmland distribution. The area thresholds were 35 m~2 in complex area and 500 m~2 in simple area. Finally, taking the digitized mulched farmland as references(ground truth data), the accuracy of the recognition results of mulched farmland was assessed by error matrix and area error. The results showed that the texture features extracted by the optimal parameters could greatly improve the classification accuracy. The image morphology algorithm and the threshold segmentation method could effectively extract the block-shaped plastic-mulched farmland. The overall accuracy, Kappa coefficient, product accuracy, user accuracy and area error were 94.84%, 0.89, 92.48%, 93.39%, 0.38% in complex area, and 96.74%, 0.93, 97.39%, 94.63%, 1.95% in simple area, respectively. Compared with step and direction, the size of window had greater influence on plastic mulch classification accuracy. Among 8 texture features, mean contributed most to extracting plastic mulch. The method of extracting plastic-mulched farmland based on the fusion of supervised classification and image morphology algorithm proposed in this paper can provide reference for the development of identification algorithm about plastic-mulched farmland.
引文
[1]哈斯图亚.基于多源数据的地膜覆盖农田遥感识别研究[D].北京:中国农业科学院,2017.Hasituya.Mapping Plastic-mulched Farmland with Multi-Source Remote Sensing Data[D].Beijing:Chinese Academy of Agricultural Sciences,2017.(in Chinese with English abstract)
[2]陈仲新,任建强,唐华俊,等.农业遥感研究应用进展与展望[J].遥感学报,2016,20(5):748-767.Chen Zhongxin,Ren Jianqiang,Tang Huajun,et al.Progress and perspectives on agricultural remote sensing research and applications in China[J].Journal of Remote Sensing,2016,20(5):748-767.(in Chinese with English abstract)
[3]Picuno P,Tortora A,Capobianco R L.Analysis of plasticulture landscapes in Southern Italy through remote sensing and solid modelling techniques[J].Landscape and Urban Planning,2011,100(1/2):1-56.
[4]Lanorte A,Santis F D,Gabriele Nolè,et al.Agricultural plastic waste spatial estimation by Landsat 8 satellite images[J].Computers&Electronics in Agriculture,2017,141:35-45.
[5]Hasituya,Chen Z,Wang L,et al.Monitoring plastic-mulched farmland by landsat-8 OLI imagery using spectral and textural features[J].Remote Sensing,2016,8(4):353.
[6]Hasituya,Chen Z.Mapping plastic-mulched farmland with multi-temporal Landsat-8 data[J].Remote Sensing,20179(6):557.
[7]沙先丽.地膜农田遥感信息提取及覆膜地表温度反演[D].杭州:浙江大学,2012.Sha Xianli.Extraction of Plastic-Mulched Landcover Information and on Retrieval of Mulched Land Surface Temperature[D].Hangzhou:Zhejiang University,2012.(in Chinese with English abstract)
[8]李佳雨,王华斌,王光辉,等.多源卫星数据的农用地膜信息提取[J].测绘通报,2018(7):78-82.Li Jiayu,Wang Huabin,Wang Guanghui,et al.Plastic-mulched farmland extraction with multi-source satellite data[J].Bulletin of Surveying and Mapping,2018(7):78-82.(in Chinese with English abstract)
[9]高梦婕,姜群鸥,赵一阳,等.基于GF-2遥感影像的塑料大棚提取方法对比[J].中国农业大学学报,2018,23(8):125-134.Gao Mengjie,Jiang Qunou,Zhao Yiyang,et al.Comparison of plastic greenhouse extraction method based on GF-2remote-sensing imagery[J].Journal of China Agricultural University,2018,23(8):125-134.(in Chinese with English abstract)
[10]杭丹维.南疆覆膜农田亚像元制图及热环境效应定量测算[D].杭州:浙江大学,2016.Hang Danwei.Mapping Subpixel of Plastic Mulched Landcover and Measuring the Heat Effect Mulching:A Case of Study Southern Xinjiang[D].Hangzhou:Zhejiang University,2016.(in Chinese with English abstract)
[11]Lu L,Hang D,Di L.Threshold model for detecting transparent plastic-mulched landcover using moderateresolution imaging spectroradiometer time series data:A case study in southern Xinjiang,China[J].Journal of Applied Remote Sensing,2015,9(1):097094.
[12]Levin N,Lugassi R,Ramon U,et al.Remote sensing as a tool for monitoring plasticulture in agricultural landscapes[J].International Journal of Remote Sensing,2007,28(1):183-202.
[13]张航.基于高光谱成像技术的皮棉中地膜识别方法研究[D].北京:中国农业大学,2016.Zhang Hang.Identification of Plastic Films in Ginned Cotton based on Hyperspectral Imaging Technique[D].Beijing:China Agricultural University,2016.(in Chinese with English abstract)
[14]王海慧.农膜的偏振反射特征研究[D].长春:东北师范大学,2007.Wang Haihui.Study on the Polarized Reflectance Characteristics of Agricultural Thin Membrane[D].Changchun:Northeast Normal University,2007.(in Chinese with English abstract)
[15]Hasituya,Zhongxin C,Fei L,et al.Mapping plastic-mulched farmland with C-Band full polarization SAR remote sensing data[J].Remote Sensing,2017,9(12):1264.
[16]Xiang H,Tian L.Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle(UAV)[J].Biosystems Engineering,2011,108(2):174-190.
[17]汪小钦,王苗苗,王绍强,等.基于可见光波段无人机遥感的植被信息提取[J].农业工程学报,2015,31(5):152-159.Wang Xiaoqin,Wang Miaomiao,Wang Shaoqiang,et al.Extraction of vegetation information from visible unmanned aerial vehicle images[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(5):152-159.(in Chinese with English abstract)
[18]李明,黄愉淇,李绪孟,等.基于无人机遥感影像的水稻种植信息提取[J].农业工程学报,2018,34(4):108-114.Li Ming,Huang Yuqi,Li Xumeng,et al.Extraction of rice planting information based on remote sensing image from UAV[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(4):108-114.(in Chinese with English abstract)
[19]杨文攀,李长春,杨浩,等.基于无人机热红外与数码影像的玉米冠层温度监测[J].农业工程学报,2018,34(17):68-75.Yang Wenpan,Li Changchun,Yang Hao,et al.Monitoring of canopy temperature of maize based on UAV thermal infrared imagery and digital imagery[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(17):68-75.
[20]戴建国,张国顺,郭鹏,等.基于无人机遥感可见光影像的北疆主要农作物分类方法[J].农业工程学报,2018,34(18):122-129.Dai Jianguo,Zhang Guoshun,Guo Peng,et al.Classification method of main crops in northern Xinjiang based on UAVvisible waveband images[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(18):122-129.(in Chinese with English abstract)
[21]于东海,冯仲科.基于无人机倾斜航空影像的树冠体积测算方法[J].农业工程学报,2019,35(1):90-97.Yu Donghai,Feng Zhongke.Tree crown volume measurement method based on oblique aerial images of UAV[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2019,35(1):90-97.(in Chinese with English abstract)
[22]孙钰,韩京冶,陈志泊,等.基于深度学习的大棚及地膜农田无人机航拍监测方法[J].农业机械学报,2018,49(2):133-140.Sun Jue,Han Jingye,Chen Zhibo,et al.Monitoring method for UAV image of greenhouse and plastic-mulched landcover based on deep learning[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(2):133-140.(in Chinese with English abstract)
[23]Haralick R M.Statistical and structural approaches to texture[J].Proceeding of the IEEE,1979,67(5):786-804.
[24]朱秀芳,潘耀忠,张锦水,等.训练样本对TM尺度小麦种植面积测量精度影响研究(Ⅰ):训练样本与分类方法间分类精度响应关系研究[J].遥感学报,2007,11(6):826-837.Zhu Xiufang,Pan Yaozhong,Zhang Jinshui,et al.The effects of training samples on the wheat planting area measure accuracy in TM scale(I):The accuracy Response of different classifiers to training samples[J].Journal of Remote Sensing,2007,11(6):826-837.(in Chinese with English abstract)
[25]朱秀芳,潘耀忠,王双,等.训练样本对TM尺度小麦种植面积测量精度影响研究(Ⅱ):样本质量对小麦测量精度的影响分析[J].测绘科学,2009,34(5):132-135.Zhu Xiufang,Pan Yaozhong,Wang Shuang,et al.The effects of training samples on the wheat planting area surveying accuracy in TM scale(II):The accuracy response of different classifiers to samples quality[J].Science of Surveying and Mapping,2009,34(5):132-135.(in Chinese with English abstract)
[26]朱秀芳,贾斌,潘耀忠,等.不同特征信息对TM尺度冬小麦面积测量精度影响研究[J].农业工程学报,2007,23(9):122-129.Zhu Xiufang,Jia Bin,Pan Yaozhong,et al.Effects of various feature information on the accuracy of winter wheat planting area measurement[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2007,23(9):122-129.(in Chinese with English abstract)
[2]陈仲新,任建强,唐华俊,等.农业遥感研究应用进展与展望[J].遥感学报,2016,20(5):748-767.Chen Zhongxin,Ren Jianqiang,Tang Huajun,et al.Progress and perspectives on agricultural remote sensing research and applications in China[J].Journal of Remote Sensing,2016,20(5):748-767.(in Chinese with English abstract)
[3]Picuno P,Tortora A,Capobianco R L.Analysis of plasticulture landscapes in Southern Italy through remote sensing and solid modelling techniques[J].Landscape and Urban Planning,2011,100(1/2):1-56.
[4]Lanorte A,Santis F D,Gabriele Nolè,et al.Agricultural plastic waste spatial estimation by Landsat 8 satellite images[J].Computers&Electronics in Agriculture,2017,141:35-45.
[5]Hasituya,Chen Z,Wang L,et al.Monitoring plastic-mulched farmland by landsat-8 OLI imagery using spectral and textural features[J].Remote Sensing,2016,8(4):353.
[6]Hasituya,Chen Z.Mapping plastic-mulched farmland with multi-temporal Landsat-8 data[J].Remote Sensing,20179(6):557.
[7]沙先丽.地膜农田遥感信息提取及覆膜地表温度反演[D].杭州:浙江大学,2012.Sha Xianli.Extraction of Plastic-Mulched Landcover Information and on Retrieval of Mulched Land Surface Temperature[D].Hangzhou:Zhejiang University,2012.(in Chinese with English abstract)
[8]李佳雨,王华斌,王光辉,等.多源卫星数据的农用地膜信息提取[J].测绘通报,2018(7):78-82.Li Jiayu,Wang Huabin,Wang Guanghui,et al.Plastic-mulched farmland extraction with multi-source satellite data[J].Bulletin of Surveying and Mapping,2018(7):78-82.(in Chinese with English abstract)
[9]高梦婕,姜群鸥,赵一阳,等.基于GF-2遥感影像的塑料大棚提取方法对比[J].中国农业大学学报,2018,23(8):125-134.Gao Mengjie,Jiang Qunou,Zhao Yiyang,et al.Comparison of plastic greenhouse extraction method based on GF-2remote-sensing imagery[J].Journal of China Agricultural University,2018,23(8):125-134.(in Chinese with English abstract)
[10]杭丹维.南疆覆膜农田亚像元制图及热环境效应定量测算[D].杭州:浙江大学,2016.Hang Danwei.Mapping Subpixel of Plastic Mulched Landcover and Measuring the Heat Effect Mulching:A Case of Study Southern Xinjiang[D].Hangzhou:Zhejiang University,2016.(in Chinese with English abstract)
[11]Lu L,Hang D,Di L.Threshold model for detecting transparent plastic-mulched landcover using moderateresolution imaging spectroradiometer time series data:A case study in southern Xinjiang,China[J].Journal of Applied Remote Sensing,2015,9(1):097094.
[12]Levin N,Lugassi R,Ramon U,et al.Remote sensing as a tool for monitoring plasticulture in agricultural landscapes[J].International Journal of Remote Sensing,2007,28(1):183-202.
[13]张航.基于高光谱成像技术的皮棉中地膜识别方法研究[D].北京:中国农业大学,2016.Zhang Hang.Identification of Plastic Films in Ginned Cotton based on Hyperspectral Imaging Technique[D].Beijing:China Agricultural University,2016.(in Chinese with English abstract)
[14]王海慧.农膜的偏振反射特征研究[D].长春:东北师范大学,2007.Wang Haihui.Study on the Polarized Reflectance Characteristics of Agricultural Thin Membrane[D].Changchun:Northeast Normal University,2007.(in Chinese with English abstract)
[15]Hasituya,Zhongxin C,Fei L,et al.Mapping plastic-mulched farmland with C-Band full polarization SAR remote sensing data[J].Remote Sensing,2017,9(12):1264.
[16]Xiang H,Tian L.Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle(UAV)[J].Biosystems Engineering,2011,108(2):174-190.
[17]汪小钦,王苗苗,王绍强,等.基于可见光波段无人机遥感的植被信息提取[J].农业工程学报,2015,31(5):152-159.Wang Xiaoqin,Wang Miaomiao,Wang Shaoqiang,et al.Extraction of vegetation information from visible unmanned aerial vehicle images[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(5):152-159.(in Chinese with English abstract)
[18]李明,黄愉淇,李绪孟,等.基于无人机遥感影像的水稻种植信息提取[J].农业工程学报,2018,34(4):108-114.Li Ming,Huang Yuqi,Li Xumeng,et al.Extraction of rice planting information based on remote sensing image from UAV[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(4):108-114.(in Chinese with English abstract)
[19]杨文攀,李长春,杨浩,等.基于无人机热红外与数码影像的玉米冠层温度监测[J].农业工程学报,2018,34(17):68-75.Yang Wenpan,Li Changchun,Yang Hao,et al.Monitoring of canopy temperature of maize based on UAV thermal infrared imagery and digital imagery[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(17):68-75.
[20]戴建国,张国顺,郭鹏,等.基于无人机遥感可见光影像的北疆主要农作物分类方法[J].农业工程学报,2018,34(18):122-129.Dai Jianguo,Zhang Guoshun,Guo Peng,et al.Classification method of main crops in northern Xinjiang based on UAVvisible waveband images[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(18):122-129.(in Chinese with English abstract)
[21]于东海,冯仲科.基于无人机倾斜航空影像的树冠体积测算方法[J].农业工程学报,2019,35(1):90-97.Yu Donghai,Feng Zhongke.Tree crown volume measurement method based on oblique aerial images of UAV[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2019,35(1):90-97.(in Chinese with English abstract)
[22]孙钰,韩京冶,陈志泊,等.基于深度学习的大棚及地膜农田无人机航拍监测方法[J].农业机械学报,2018,49(2):133-140.Sun Jue,Han Jingye,Chen Zhibo,et al.Monitoring method for UAV image of greenhouse and plastic-mulched landcover based on deep learning[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(2):133-140.(in Chinese with English abstract)
[23]Haralick R M.Statistical and structural approaches to texture[J].Proceeding of the IEEE,1979,67(5):786-804.
[24]朱秀芳,潘耀忠,张锦水,等.训练样本对TM尺度小麦种植面积测量精度影响研究(Ⅰ):训练样本与分类方法间分类精度响应关系研究[J].遥感学报,2007,11(6):826-837.Zhu Xiufang,Pan Yaozhong,Zhang Jinshui,et al.The effects of training samples on the wheat planting area measure accuracy in TM scale(I):The accuracy Response of different classifiers to training samples[J].Journal of Remote Sensing,2007,11(6):826-837.(in Chinese with English abstract)
[25]朱秀芳,潘耀忠,王双,等.训练样本对TM尺度小麦种植面积测量精度影响研究(Ⅱ):样本质量对小麦测量精度的影响分析[J].测绘科学,2009,34(5):132-135.Zhu Xiufang,Pan Yaozhong,Wang Shuang,et al.The effects of training samples on the wheat planting area surveying accuracy in TM scale(II):The accuracy response of different classifiers to samples quality[J].Science of Surveying and Mapping,2009,34(5):132-135.(in Chinese with English abstract)
[26]朱秀芳,贾斌,潘耀忠,等.不同特征信息对TM尺度冬小麦面积测量精度影响研究[J].农业工程学报,2007,23(9):122-129.Zhu Xiufang,Jia Bin,Pan Yaozhong,et al.Effects of various feature information on the accuracy of winter wheat planting area measurement[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2007,23(9):122-129.(in Chinese with English abstract)