[1]赵建鹏,杨秀峰,李国洪,等.基于面向对象的设施蔬菜高分遥感影像提取[J].江苏农业学报,2019,(04):911-918.[doi:doi:10.3969/j.issn.1000-4440.2019.04.023]
　ZHAO Jian peng,YANG Xiu feng,LI Guo hong,et al.Object oriented extraction of high resolution remote sensing images of facility vegetables[J].,2019,(04):911-918.[doi:doi:10.3969/j.issn.1000-4440.2019.04.023]
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基于面向对象的设施蔬菜高分遥感影像提取()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年04期
页码:
911-918
栏目:
园艺
出版日期:
2019-08-31

文章信息/Info

Title:
Object oriented extraction of high resolution remote sensing images of facility vegetables
作者:
赵建鹏1; 杨秀峰1; 2; 3; 李国洪1; 2; 3; 李旭青1; 2; 3; 金永涛1; 2; 3; 刘世盟1
(1.北华航天工业学院，河北廊坊065000；2.河北省航天遥感信息处理与应用协同创新中心，河北廊坊065000；3.河北省航天遥感信息工程技术研究中心，河北廊坊065000）
Author(s):
ZHAO Jianpeng1; YANG Xiufeng1; 2; 3; LI Guohong1; 2; 3; LI Xuqing1; 2; 3; JIN Yongtao1; 2; 3;  LIU Shimeng1
(1.North China Institute of Aerospace Engineering， Langfang 065000, China;2.Hebei Province Space Remote Sensing Information Processing and Application Cooperative Innovation Center， Langfang 065000, China;3.Hebei Province Space Remote Sensing Information Engineering Research Center， Langfang 065000, China)
关键词:
设施蔬菜; 影像提取; GF2; 多特征融合; 面向对象
Keywords:
facility vegetable; image extraction; GF2; multifeature fusion; object oriented
分类号:
S127
DOI:
doi:10.3969/j.issn.1000-4440.2019.04.023
文献标志码:
A
摘要:
以河北省廊坊市香河县五百户镇为研究区，综合利用高分二号（GF2）遥感影像的光谱、纹理特征，并结合边缘检测、阈值分割、数学形态学算法，设计了面向对象的多特征融合设施蔬菜面积提取方法。首先对影像进行增强处理，结合影像中光谱和纹理特征剔除建筑物和道路干扰。然后采用阈值分割算法将边缘检测后的“噪声”进行删除，并使用数学形态学方法提高影像分割效率。最后对于一些难以去除的“噪声”采用面积（Ar）、周长（Per）、圆形度（Rd）、长宽比（Pwl）、矩形比（Pr）这5个形状特征参数进行剔除，实现利用高分遥感影像提取设施蔬菜面积。精度验证结果表明，该方法在试验区野外核查的精度为8602%，随机样本点的总体分类精度为845%，Kappa系数为831%。
Abstract:
Taking Wubaihu Town of Xianghe County, Langfang City, Hebei province as the research area, an object oriented multifeature fusion facility vegetables area extraction method was designed based on the spectral and texture features of GF2 remote sensing image, and combined with edge detection, threshold segmentation and mathematical morphology algorithms. Firstly, the image was enhanced, and the spectral and texture features were used to remove the buildings and roads. Then the “noise” after edge detection was deleted by threshold segmentation, and the efficiency of image segmentation was improved by using mathematical morphology. Finally, for some noise which was difficult to remove, five shape characteristic parameters, area (Ar), perimeter (Per), round degree (Rd), percentage of width and length (Pwl) and percentage of rectangle (Pr) were used to eliminate the noise, so that the area of facility vegetables could be extracted from high resolution remote sensing images. The verification result showed that the accuracy of field verification in the test area was 8602%, the overall classification accuracy of random sample points was 845%, and the Kappa coefficient was 831%.

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备注/Memo

备注/Memo:
收稿日期：2018-09-19 基金项目：高分共性应用技术规范和高分遥感数据云平台处理应用共性关键技术项目（67-Y20A07-9002-16/17）；高分辨率对地观测系统重大专项省（自治区）域产业化应用项目（67-Y40G09-9002-15/18） 作者简介：赵建鹏（1992-），男，河北邯郸人，硕士研究生，研究方向为遥感应用技术。 通讯作者：杨秀峰，(E-mail)Yangxf1987anyang@126.com
更新日期/Last Update: 2019-08-31