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遥感技术与应用  2020, Vol. 35 Issue (3): 623-633    DOI: 10.11873/j.issn.1004-0323.2020.3.0623
数据与图像处理     
遥感影像多尺度分割中最优尺度的选取及评价
王芳1,2(),杨武年1(),王建3,谢兵4,任金铜1
1.成都理工大学 国土资源部地学空间信息技术重点实验室, 四川 成都 610059
2.内江师范学院 地理与资源科学学院,四川 内江 641100
3.内江职业技术学院 土木工程系,四川 内江 641000
4.四川建筑职业技术学院 测绘工程系,四川 德阳 618000
Selection and Evaluation of the Optimal Scale in Multi-scale Segmentation of Remote Sensing Images
Fang Wang1,2(),Wunian Yang1(),Jian Wang3,Bin Xie4,Jintong Ren1
1.Key Laboratory of Geo-spatial Information Technology of Ministry of Land and Resources of P. R. China, Chengdu University of Technology, Chengdu 610059, China
2.College of Geography and Resources Science, Neijiang Normal University, Neijiang 641100, China
3.Department of Civil Engineering, Neijiang Vocational&Technical Cllege, Neijiang 641000, China
4.Department of Surveying and Mapping Engineering, Sichuan College of Architectural Technology, Deyang 618000, China
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摘要:

多尺度分割是面向对象图像分析技术的前提和关键,多尺度分割的质量直接影响着面向对象分类的精度,但尺度选择仍然是多尺度分割中的一个难题。针对此问题,根据遥感影像的最优分割尺度与影像上目标复杂度密切相关的事实,提出了一种自上而下基于分割对象复杂度选取最优尺度的方法。该方法在分割过程中,提取每一对象的影像特征构建其复杂度函数,通过设置阈值,经迭代计算来确定每一对象的最优分割尺度,进而得到具有全局最优尺度的分割结果,并将其应用于ZY-3多光谱数据和GF-2融合影像,得到分割和分类结果。并将其与单一最优尺度和非监督评价法的分割及分类结果进行比较,结果表明:该方法能够获取与地面目标相匹配的分割尺度,改善了分割效果,提高了分类精度,具有一定实用价值。

关键词: Meanshift分割面向对象图像分析技术对象复杂度最优分割尺度尺度选取及评价    
Abstract:

Multi-scale segmentation is the premise and key step of Object-Based Image Analysis (OBIA). The quality of multi-scale segmentation directly affects the accuracy of object-oriented classification. However, scale selection and evaluation remains a challenge in multi-scale segmentation. According to the fact that the optimal segmentation scale of the remote sensing image is closely related to the complexity of the objects of the image, a top-down method to select the optimal scale based on the complexity of segmented objects is proposed. In the top-down segmentation process, image features of each segmented object are extracted to construct the complexity function, and the optimal scale of each object is determined by setting a threshold value and iterating calculation. Then, the segmentation results with the best scale are obtained and applied to the ZY-3 satellite multispectral image and the GF-2 fusion image to obtain segmentation and classification results. Qualitative visual evaluation method, unsupervised evaluation method and supervised classification evaluation method were used to compare them with results obtained by the optimal single-scale segmentation and the unsupervised evaluation method. The experimental results show that the method can accurately obtain the scale matching with the ground targets, and improve segmentation effect and the classification accuracy, it is of practical value.

Key words: Mean-shift segmentation    Object-Based Image Analysis (OBIA)    Object complexity    Optimal segmentation scale    Scale selection and evaluation
收稿日期: 2019-01-17 出版日期: 2020-07-10
ZTFLH:  TP751  
基金资助: 国家自然科学基金项目“川西高原植被生态水(层)及水分胁迫状况遥感动态监测方法”(41671432);四川省国土资源厅项目“国产卫星数据在土地利用监测与现状变更中的遥感产品验证与推广研究”(KJ-2016-12)
通讯作者: 杨武年     E-mail: 398321192@qq.com;ywn@cdut.edu.cn
作者简介: 王芳(1983—),女,河南周口人,博士研究生,讲师,主要从事3S技术与数字国土研究。E?mail: 398321192@qq.com
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引用本文:

王芳,杨武年,王建,谢兵,任金铜. 遥感影像多尺度分割中最优尺度的选取及评价[J]. 遥感技术与应用, 2020, 35(3): 623-633.

Fang Wang,Wunian Yang,Jian Wang,Bin Xie,Jintong Ren. Selection and Evaluation of the Optimal Scale in Multi-scale Segmentation of Remote Sensing Images. Remote Sensing Technology and Application, 2020, 35(3): 623-633.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.3.0623        http://www.rsta.ac.cn/CN/Y2020/V35/I3/623

图1  技术路线图
图2  研究区ZY-3多光谱影像、GF-2融合影像及参考对象的分布
影像获取时间空间分辨/m波段面积/km2地物类型地理位置
GF-22016.6.161.0蓝、绿、红、近红9.81建筑、水体、裸地、绿地、道路、耕地四川省隆昌县中东部
ZY-32017.8.275.8蓝、绿、红、近红34.11建筑、水体、裸地、绿地、道路四川省泸州市区
表1  研究区影像数据信息
图3  采用本研究方法基于ZY-3多光谱影像和GF-2影像的多尺度分割结果
图4  分割结果
图5  分类结果
图6  局部分割和分类结果
影像ZY-3GF-2
方法SE-OSU-ES-OSE-OSU-ES-O
GS0.5800.6220.6440.5890.6320.660
表2  三种方法分割结果的GS值
精度土地利用类型ZY-3多光谱影像GF-2影像
SE-OSU-EO-SSE-OSU-EO-S
制图精度/%建筑用地90.1487.3288.3892.0485.5083.19
耕地///90.9082.8375.76
林地///96.9084.5493.81
绿地99.9899.7599.2492.7792.7792.77
道路87.7467.9265.1091.7891.7883.56
裸地88.4675.0067.3085.7185.7185.71
水体85.7176.6275.3293.5593.5593.55
用户精度/%建筑用地92.0981.5882.3095.4195.2490.38
耕地///95.7483.6794.94
林地///92.1689.1385.05
绿地90.4186.8482.9186.5275.5076.24
道路86.9285.7181.1891.7890.4187.14
裸地85.1976.4779.5585.7199.9685.71
水体99.9899.9799.9799.9799.9799.97
总体精度/%91.1084.4983.5692.7588.0486.08
Kappa0.8770.7820.7680.9120.8550.832
表3  分割结果的GS值与面向对象分类结果精度评价
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