面向对象的特色农作物种植遥感调查方法研究

doi:10.12082/dqxxkx.2018.180116

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (10): 1509-1519.doi: 10.12082/dqxxkx.2018.180116

面向对象的特色农作物种植遥感调查方法研究

单治彬¹(), 孔金玲^2,^*(), 张永庭³, 李欢³, 关红³, 胡永新², 李健锋¹, 张文博¹

1. 长安大学地球科学与资源学院,西安 710054
2. 长安大学地质工程与测绘学院,西安 710054
3. 宁夏回族自治区遥感测绘勘查院,银川 750021

收稿日期:2018-02-28 修回日期:2018-07-19 出版日期:2018-10-25 发布日期:2018-10-17
通讯作者: 孔金玲 E-mail:zbshan_chd@126.com;jlkong@163.com
作者简介:
作者简介：单治彬（1993-）,男,陕西安康人,硕士生,主要从事遥感反演研究。E-mail: zbshan_chd@126.com
基金资助:
宁夏回族自治区重点研发计划项目（2016KJHM130）

Remote Sensing Investigation Method of Object-oriented Crops with Special Characteristics

SHAN Zhibin¹(), KONG Jinling²(), ZHANG Yongting³, LI Huan³, GUAN Hong³, HU Yongxin², LI Jianfeng¹, ZHANG Wenbo¹

1. School of Earth Science and Resources, Chang'an University, Xi'an 710054, China
2. School of geological engineering and Geomatics, Chang'an University, Xi'an 710054, China
3. The remote sensing surveying and Mapping Institute of the Ningxia Hui Autonomous Region, yinchuan 750021, China

Received:2018-02-28 Revised:2018-07-19 Online:2018-10-25 Published:2018-10-17
Supported by:
Key Research and Development Project of the Ningxia Hui Autonomous Region, No.2016KJHM130.

摘要/Abstract

摘要：

宁夏自治区具有土地、光能、引黄灌溉等优势,为宁夏特色农作物（硒砂瓜、枸杞、大枣）的生长提供了先天条件。快速准确地获取特色农作物的种植信息不仅是宁夏特色农作物监测、估产和灾害评估的重要依据,同时也是分析特色农作物结构分布变化和评价区域特色农业生产影响的重要凭证。近年来,随着航天技术和卫星传感器的不断发展,越来越多的学者将遥感技术运用到农作物种植信息的提取研究中。但是传统的遥感调查模型都是基于中低分辨遥感数据建立的,对于新的高分数据没有完备的信息提取模型。此外,基于GF-1遥感影像对类似宁夏特色农作物（硒砂瓜、枸杞、大枣）的信息提取研究相对较少,决策条件和分类模型的选择也难以满足高分农业的需求。基于此,本文利用国产GF-1 PMS遥感影像,在分析3类特色农作物光谱特征和纹理特征的基础上,建立了面向对象的支持向量机（SVM）分类模型,总体分类精度达到94.94%,Kappa系数为0.9174。同时将分类结果与传统的SVM分类结果相比较,研究发现面向对象的SVM模型的精度更高,效果最好,纹理信息的引入使光谱特征差异较小的枸杞和大枣更容易区分,有效降低了模型错分和漏分误差,改善了模型分类结果。研究结果为实现宁夏特色农作物的快速自动化提取提供了有效途径,也为开展农作物承保和受灾定损评估体系建设提供技术支撑。

关键词: 特色农作物, 支持向量机, 面向对象模型, 特征提取, GF-1影像

Abstract:

The advantages of Ningxia Hui Autonomous Region is land, light energy, and irrigation of Yellow River, which provides an inherent condition for the growth of characteristic crops of Ningxia (e.g. watermelon, Chinese wolfberry and jujube). Accessing to planting structure information of characteristic crops quicklf and accurately is not only an important basis for regional crop monitoring, yield estimation and disaster assessment, but also an important evidence for analyzing the spatial pattern changes of characteristic crop and assessing the impact of regional characteristics on agricultural production. In recent years, with the continuous development of space technology and remote sensing satellites, more and more scholars have applied remote sensing technology to the extraction of crop planting structure information. However, the traditional remote sensing survey model is only applicable to low resolution and medium resolution remote sensing data, and domestic and abroad scholars have relatively few studies on information extraction of similar Ningxia characteristic crops (e.g. watermelon, Chinese wolfberry and jujube), and the selection of classification models and strategies is difficult to meet the demand of rapid monitoring, accurate acquisition and real-time decision-making. Based on this, this paper calculate and analyze the spectral characteristics and texture features of the three types of specialty crops under the support of GF-2 remote sensing data, and establish an SVM of object-oriented classification model, the overall classification accuracy is 94.94% and the Kappa coefficient is 0.9174. And compare the classification results with the traditional SVM classification results, The study found that the SVM of object-oriented model established in this paper has the highest accuracy and best results, the texture information makes it easier to distinguish the Chinese wolfberry and jujube,whice has theless difference in spectral characteristics, and the texture information effectively reducing the model error and missing error, and improving the model classification results.

Key words: characteristic crops, support vector machine, object-oriented, feature extraction, GF-1 images

单治彬, 孔金玲, 张永庭, 李欢, 关红, 胡永新, 李健锋, 张文博. 面向对象的特色农作物种植遥感调查方法研究[J]. 地球信息科学学报, 2018, 20(10): 1509-1519.

SHAN Zhibin,KONG Jinling,ZHANG Yongting,LI Huan,GUAN Hong,HU Yongxin,LI Jianfeng,ZHANG Wenbo. Remote Sensing Investigation Method of Object-oriented Crops with Special Characteristics[J]. Journal of Geo-information Science, 2018, 20(10): 1509-1519.

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作物类型	训练数据			检验数据
作物类型	样方数	样方总面积/km²	面积比/%	样方数	样方总面积/km²	面积比/%
硒砂瓜	100	83.56	39.43	70	53.31	39.47
枸杞	100	69.38	32.74	70	46.95	34.76
大枣	60	58.99	27.83	40	34.82	25.77

地物类型	硒砂瓜	枸杞	大枣	合计
未分类	0	0	0	0
硒砂瓜	39 557	361	0	39 918
枸杞	439	13 190	116	13 745
大枣	0	2986	20 492	23 478
合计	39 996	16 537	20 608	77 141

地物类型	硒砂瓜	枸杞	大枣
制图精度/%	98.90	79.76	99.44
用户精度/%	99.10	95.96	87.28
错分误差/%	0.90	4.04	12.72
漏分精度/%	1.10	20.24	0.56
总体精度/%				94.94
Kappa系数				0.917

类别	光谱单数据源SVM法		光谱结合纹理SVM法		面向对象的SVM法
类别	制图精度/%	用户精度/%	制图精度/%	用户精度/%	制图精度/%	用户精度/%
硒砂瓜	90.02	95.84	92.3	99.58	98.90	99.10
枸杞	76.30	66.72	78.52	72.84	79.76	95.96
大枣	80.78	76.94	91.24	80.74	99.44	87.28
总体精度/%	84.82		89.17		94.94
Kappa系数	0.769		0.815		0.917

面向对象的特色农作物种植遥感调查方法研究

Remote Sensing Investigation Method of Object-oriented Crops with Special Characteristics

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