首页|基于无人机遥感技术的玉米种植信息提取方法研究

基于无人机遥感技术的玉米种植信息提取方法研究

扫码查看
使用无人机遥感试验获取的可见光图像研究拔节期玉米种植信息提取方法.首先确定感兴趣区地物种类,包括:玉米、小麦、向日葵、树苗和裸地;然后分别统计计算5类地物的27项纹理特征,比较各类地物特征的种内变异系数和与玉米的相对差异系数,选出适宜提取玉米种植信息的特征.经过分析发现,仅用一个特征参数难以准确提取玉米种植信息,需要各特征组合分层分类提取玉米信息.最后确定绿色均值、蓝色协同性和纹理低通植被指数TLVI为玉米种植信息提取特征.经过对初步提取结果的分析,发现分类后的小麦地和树苗地中仍残留有与玉米区特征相同的斑块,玉米地中有与非玉米区特征相同的斑块,结合两种斑块各自形状面积分布的独特性,分别实现残留斑块去除和玉米地错分斑块保留,完成玉米种植信息提取.选取与感兴趣区影像同时期不同区域的两幅影像进行方法验证,结果表明:该方法对玉米种植信息提取有较好效果,面积提取误差在20%以内,对用无人机可见光遥感影像进行玉米种植信息提取具有一定的适用性.
Extraction Method of Maize Planting Information Based on UAV Remote Sensing Techonology
A method of information extraction for maize at jointing stage was described by using the highresolution visible images,which were obtained by the unmanned aerial vehicle (UAV) remote sensing system.The 27 texture features of five ground objects were calculated separately,including maize,wheat,sunflower,sapling and bare land in the region of interest obtained by using co-occurrence measures and convolutions low pass.Comparing the variation coefficient of five ground objects and the relative difference with maize,the mean of green,homogeneity of blue and texture low pass vegetation index (TLVI) were chosen as the feature to obtain planting information of maize.In order to distinguish the maize land and sapling land,the TLVI was built by using scatter diagram in which the X-axis was the low-pass red band and the Y-axis was the low-pass blue band of maize land and sapling land.In the preliminary result,it was found that there were patches which had the same feature with maize land in wheat land and sapling land and patches of other kinds in the maize land.By analyzing the uniqueness of shape and area of two kinds of patches,the other patches were removed and the patches of maize land were retained.In order to verify the applicability and the reliability of the method,two different images which were in the same period with the region of interest were chosen to process by using the same method.The results indicated that the method could extract planting information of maize through using the high-resolution visible images obtained by the UAV remote sensing system and the area extraction error was less than 20%.

remote sensingunmanned aerial vehiclevisible imagetexture low pass vegetation indexplanting information extractionmaize

韩文霆、李广、苑梦婵、张立元、师志强

展开 >

西北农林科技大学机械与电子工程学院,陕西杨凌712100

西北农林科技大学水土保持研究所,陕西杨凌712100

遥感 无人机 可见光图像 纹理低通植被指数 种植信息提取 玉米

国家国际科技合作项目杨凌示范区工业项目

2014DFG721502015GY-03

2017

农业机械学报
中国农业机械学会 中国农业机械化科学研究院

农业机械学报

CSTPCDCSCD北大核心EI
影响因子:1.904
ISSN:1000-1298
年,卷(期):2017.48(1)
  • 54
  • 20