张超,乔敏,刘哲,刘帝佑,金虹杉,朱德海.基于时序光谱和高分纹理分析的制种玉米田遥感识别[J].农业机械学报,2018,49(5):218-225.
ZHANG Chao,QIAO Min,LIU Zhe,LIU Diyou,JIN Hongshan,ZHU Dehai.Seed Maize Field Identification Based on Analysis of Remote Sensing Timing Spectrum and High Resolution Texture[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):218-225.
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基于时序光谱和高分纹理分析的制种玉米田遥感识别   [下载全文]
Seed Maize Field Identification Based on Analysis of Remote Sensing Timing Spectrum and High Resolution Texture   [Download Pdf][in English]
投稿时间:2017-11-10  
DOI:10.6041/j.issn.1000-1298.2018.05.025
中文关键词:  制种玉米田  多源遥感  植被指数  LBP-GLCM纹理  随机森林
基金项目:国家高技术研究发展计划(863计划)项目(2013AA10230103)
作者单位
张超 中国农业大学 
乔敏 中国农业大学 
刘哲 中国农业大学 
刘帝佑 中国农业大学 
金虹杉 中国农业大学 
朱德海 中国农业大学 
中文摘要:根据制种玉米与其他作物在中高分辨率遥感影像上的光谱和纹理差异,利用多源遥感数据,以提取制种玉米种植田为研究目标,提出了作物多时相光谱特征分析的植被指数体系,多维度反映了作物不同光谱差异;在纹理检测前加入图像旋转不变处理,解决了遥感影像中作物田纹理方向问题;最后构建了多时相光谱特征和高空间分辨率遥感影像LBP-GLCM纹理特征的制种玉米田识别方法体系。以新疆霍城县为研究区,利用上述方法体系结合随机森林分类器,通过实验得到分类总体精度为90.57%,Kappa系数为0.79,制种玉米田分类结果用户精度为99.20%,制图精度为86.68%,基本满足对制种玉米田的识别需求。
ZHANG Chao  QIAO Min  LIU Zhe  LIU Diyou  JIN Hongshan  ZHU Dehai
China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:seed maize fields  multi-source remote sensing  vegetation index  LBP-GLCM texture  random forest
Abstract:Using remote sensing technology to rapidly and accurately differentiate the seed maize fields and grain maize fields is the urgent need of seed production and market supervision, and also is an important aspect of the research on the classification and planting mode of crops by using remote sensing to monitor. Based on the spectral and texture differences of seed maize and other crops in the high resolution remote sensing image, the multi-source remote sensing data were used, including GF-1 WFV multi-spectral image, Landsat8 OLI image and GF-2 PMS full-color image to extract the seed maize fields as research target, the vegetation index system of crop multi-temporal spectral characteristics was proposed, which multidimensionally reflected different spectral differences between crops;and adding the image rotation invariant processing before the texture detection, to solve the problem of crop field texture direction in remote sensing image;finally, the identification method system of seed maize fields based on multi-temporal spectral feature and LBP-GLCM texture feature in high spatial resolution remote sensing image were established. Qitai County, Xinjiang Uygur Autonomous Region was taken as the study area to verify, based on the above method and the random forest classifier, the overall accuracy was 90.57%, the Kappa coefficient was 0.79. The accuracy of the classification results of seed maize field was 99.20%, and the mapping accuracy was 86.68%, which basically satisfied the needs of seed maize recognition requirements. The research result not only provided a method for the monitoring of hybrid maize seed production in China, but also provided a technical reference for monitoring and supervision of hybrid seed field with the same planting system.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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