[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]
点击复制

基于面向对象的设施蔬菜高分遥感影像提取()
分享到：

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%.

参考文献/References:

[1]王殿友.我国塑料大棚的类型及特点[J].农业科技与装备,2012(8):49-50.
[2]耿林,张蓉鑫,张舒婷,等.基于高空间分辨率遥感数据建立典型地物分类数据库[J]. 林业科技情报,2014,46(3):1-4.
［3]黄启厅, 曾志康, 谢国雪, 等. 基于高时空分辨率遥感数据协同的作物种植结构调查［J]. 南方农业学报,2017,48(3):552-560.
[4]王晓梅,张玉钧,刘文清,等. 基于光谱特征的植被遥感探测及应用研究[C]. 贵阳:第十五届全国遥感技术学术交流会, 2005.
[5]马群. 大棚菜区土地利用信息遥感提取及其不同方式的土壤质量效应[D].泰安:山东农业大学, 2011.
[6]李黔湘.基于高分遥感数据的农业大棚面积提取及分析——以北京市大兴区为例[J].北京水务,2016(6):14-17.
[7]黄振国,陈仲新,刘芳清,等.基于HJ1影像的大棚菜地遥感监测技术研究——以山东寿光市为例[J].中国农业资源与区划,2013,34(5):102-106.
[8]李静,赵庚星,李涛,等.TM影像中大棚菜地信息提取技术研究[J]. 水土保持学报, 2004(1):126-129.
[9]杨良闯,程先富.基于高分数据的村级地块面向对象分类方法研究[J].资源开发与市场,2014,30(5):515-518.
[10]马倩倩.基于RapidEye遥感影像的农作物种植面积提取研究[D].济南:山东师范大学, 2012.
[11]NOVELLI A, TARANTINO E. Combining ad hoc spectral indices based on LANDSAT8 OLI/TIRS sensor data for the detection of plastic cover vineyard [J]. Remote Sensing Letters, 2015, 6 (12): 933-941.
[12]潘腾.高分二号卫星的技术特点[J].中国航天, 2015(1): 3-9.
[13]田琼花.遥感影像纹理特征提取及其在影像分类中的应用[D].武汉:华中科技大学, 2007.
[14]ROBERT M H. Statistical and structural approaches to texture [J]. Proceedings of the IEEE, 1979, 67 (5): 786-804.
[15]丛思安,王星星.Kmeans算法研究综述[J].电子技术与软件工程,2018(17): 155-156.
[16]黄昕.高分辨率遥感影像多尺度纹理、形状特征提取与面向对象分类研究[D].武汉:武汉大学, 2009.

相似文献/References:

[1]邢鲲,曹俊宇,王媛媛,等.设施蔬菜昆虫群落结构与时序动态[J].江苏农业学报,2019,(03):564.[doi:doi:10.3969/j.issn.1000-4440.2019.03.009]
　XING Kun,CAO Jun-yu,WANG Yuan-yuan,et al.The structure and time dynamics of insect community in the protected vegetable[J].,2019,(04):564.[doi:doi:10.3969/j.issn.1000-4440.2019.03.009]
[2]王秋君,马艳,郭德杰,等.设施蔬菜土壤养分状况分析及综合评价[J].江苏农业学报,2019,(03):624.[doi:doi:10.3969/j.issn.1000-4440.2019.03.017]
　WANG Qiu-jun,MA Yan,GUO De-jie,et al.Analysis and comprehensive evaluation of soil nutrient status for greenhouse vegetable[J].,2019,(04):624.[doi:doi:10.3969/j.issn.1000-4440.2019.03.017]
[3]徐彬,徐健,祁建杭,等.江苏省设施蔬菜连作障碍土壤理化及生物特征[J].江苏农业学报,2019,(05):1124.[doi:doi:10.3969/j.issn.1000-4440.2019.05.018]
　XU Bin,XU Jian,QI Jian-hang,et al.Soil physical, chemical and biological characteristic analysis of long-term vegetable monoculture in greenhouse in Jiangsu province[J].,2019,(04):1124.[doi:doi:10.3969/j.issn.1000-4440.2019.05.018]
[4]朱楠,仇美华,范新会,等.有机肥部分替代化肥对设施菜地蔬菜产量及土壤质量的影响[J].江苏农业学报,2024,(06):1004.[doi:doi:10.3969/j.issn.1000-4440.2024.06.006]
　ZHU Nan,QIU Meihua,FAN Xinhui,et al.Effects of partial substitution of chemical fertilizers with organic fertilizers on vegetable yield and soil quality in greenhouse vegetable fields[J].,2024,(04):1004.[doi:doi:10.3969/j.issn.1000-4440.2024.06.006]

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