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基于无人机遥感影像的水稻种植信息提取 预览 被引量:3

Extraction of rice planting information based on remote sensing image from UAV
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摘要 水稻是中国南方最主要的粮食作物,种植面积波动对国家粮食稳定有很大影响。通过无人机遥感试验获取多幅有重叠区域的图像,使用Agisoft photoscan软件拼接重构试验区的完整图像,利用多尺度分割方法将试验区域分割成若干对象,并基于统计方法提取对象的光谱特征、几何特征和纹理特征;然后,建立识别水稻地块的二分类Logistic回归模型,特征指标为形状指数、红色均值、红色标准偏差、最大化差异度量、灰度共生矩阵同质性和灰度共生矩阵非相似性。结果表明:模型辨识训练样本集的正确率为100%,辨识检验样本的正确率为97%,模型应用于辨识验证区域水稻田块,总体正确率为98%。最后基于累计像素方法测算水稻田块的面积,并与目视解译测算的结果对比,面积误差小于3.5%,研究方法识别水稻田块效果好,面积测算准确率高。因此,该研究对利用无人机遥感影像普查水稻种植信息具有一定的适用性。 The rice is the main crop in China. Based on the advantages of flexibility, high accuracy and short working cycle of the unmanned aerial vehicle(UAV), in this paper, we aim to establish a method for the investigation of rice planting area by UAV remote sensing image. The six-rotor UAV's camera image sensor is CMOS with FOV94. The focus is on infinity. The maximum single pixel is 4 000×3 000 pixels. The experimental region and verification region mainly included rice, tree, grassland, bare land, water body and buildings and so on. At first, the multiple images with overlapped region were obtained by UAV. The complete images of the experimental region and the verification region were obtained by Agisoft photoscan software. The image spatial resolution of the experimental region was 0.04 m and the verification region was 0.02 m. The multiresolution segmentation algorithm of eC ognition Developer 9 software was used to segment the complete image of the experimental region and the verification region to obtain several objects and calculate the spectral, geometric, and texture features of each object. Using multiresolution segmentation algorithm to segment the image, the scale parameter of experimental region: scale=480, shape=0.1, compact=0.1, and the total number of objects after the segmentation were 880. The scale parameter of experimental region: scale=1 500, shape=0.1, compact=0.3, a total of 240 split object after segmentation. Subjects in the experimental region and the verification region were divided into training samples and verification samples. Training samples in the experimental region were used to extract characteristic indexes for identifying rice, binary logistic model training samples for identifying rice, and establishment and verification of characteristic indexes. The sample was used to test the race recognition model. The characteristics indexes of race identified in this study were shape index, red mean, red standard deviation Max.diff(maximum difference), GLCM contrast(gray-lev
作者 李明 黄愉淇 李绪孟 彭冬星 谢景鑫 Li Ming1,3, Huang Yuqi1, Li Xumeng2, Peng Dongxing1, Xie Jingxin1 (1. College of Engineering, Hunan Agricultural University, Changsha 410128, China; 2. College of Science, Hunan Agricultural University, Changsha 410128, China; 3. Hunan Soar Star Aviation Technology Co.Ltd, Changsha 410100, China)
出处 《农业工程学报》 CSCD 北大核心 2018年第4期108-114,共7页 Transactions of the Chinese Society of Agricultural Engineering
基金 湖南省创新平台与人才计划(2017RS3061) 长沙市高新技术产业发展专项重点项目(K1508073-11) 湖南省技术创新引导计划(2016GK4123) 基于无人机数据采集平台水稻水肥精准管理关键技术的研究与示范(2017NK2382)
关键词 无人机 遥感 农作物 可见光 水稻 二分类 unmanned aerial vehicle remote sensing crops visible rice multi-feature
作者简介 李明,博士后,教授,博士生导师,主要从事精准农业及机器人研究.Email:liming@hunau.net.中国农业工程学会会员:李明(E041200580S).
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