中国农业科学 ›› 2019, Vol. 52 ›› Issue (6): 997-1008.doi: 10.3864/j.issn.0578-1752.2019.06.004
姬旭升1,2,3,4,李旭1,5,万泽福1,2,3,4,姚霞1,2,3,4,朱艳1,2,3,程涛1,2,3,4()
收稿日期:
2018-09-19
接受日期:
2019-01-17
出版日期:
2019-03-16
发布日期:
2019-03-22
通讯作者:
程涛
作者简介:
姬旭升,E-mail: 2016101011@njau.edu.cn。
基金资助:
JI XuSheng1,2,3,4,LI Xu1,5,WAN ZeFu1,2,3,4,YAO Xia1,2,3,4,ZHU Yan1,2,3,CHENG Tao1,2,3,4()
Received:
2018-09-19
Accepted:
2019-01-17
Online:
2019-03-16
Published:
2019-03-22
Contact:
Tao CHENG
摘要:
【目的】 枣树和棉花是新疆地区的两大优势作物。利用高空间分辨率遥感影像对作物进行识别,更加快速、准确地获取枣树和棉花的种植面积及其分布区域,以利于相关部门政策的制定及农作物的精确管理。【方法】 本文以新疆阿拉尔市主要农作物为研究对象,运用基于像素与面向对象的遥感影像分类方法,通过比较光谱角制图(SAM)、支持向量机(SVM)、CART决策树(DTs)、随机森林(RF)这4种机器学习算法在高空间分辨率卫星影像分类中的作物识别精度,探究影像获取时期(2016-05-10、2016-09-07、2016-10-08)及面向对象的信息提取技术对作物分类精度的影响。【结果】 5月份影像(即棉花覆膜期影像)作物分类精度最高,10月份影像次之,9月份影像最差;与基于像素的作物分类方法相比,面向对象的作物分类方法可以使各时期的作物分类总体精度得到一定提高(除SAM之外),各时期分类精度分别提高了4.83%、7.77%、7.22%,最高分类精度分别为93.52%(2016-05-10)、85.36%(2016-09-07)、88.88%(2016-10-08),均实现了较好的作物分类效果。【结论】 5月份(棉花覆膜期)影像对棉花和枣树分类效果最好,该时期的棉花被地膜覆盖,且枣树表现出明显的植被光谱特性,两种作物生长早期呈现出差异化的光谱特征,因此棉花和枣树的遥感识别应在作物生长早期进行;面向对象的分类方法可以综合运用光谱、纹理及空间信息,特别是纹理信息的加入,可以取得比基于像素方法更高的分类精度,且提供一种高效提取田块边界的手段,对当地农田信息化管理具有重要应用价值。在棉花和枣树识别过程中,纹理特征的重要性高于光谱和空间特征,红光和绿光波段在所有波段中对棉花和枣树的识别贡献最大。
姬旭升,李旭,万泽福,姚霞,朱艳,程涛. 基于高空间分辨率卫星影像的新疆阿拉尔市棉花与枣树分类[J]. 中国农业科学, 2019, 52(6): 997-1008.
JI XuSheng,LI Xu,WAN ZeFu,YAO Xia,ZHU Yan,CHENG Tao. Pixel-Based and Object-Oriented Classification of Jujube and Cotton Based on High Resolution Satellite Imagery over Alear, Xinjiang[J]. Scientia Agricultura Sinica, 2019, 52(6): 997-1008.
表1
获取的卫星影像"
卫星 Satellite | 波段类型 Band type | 空间分辨率 Spatial resolution (m) | 波段 Band | 获取时间 Date of image acquisition |
---|---|---|---|---|
SPOT-6 | 全色Panchromatic | 1.5 | 全色Panchromatic | 2016-05-10 |
多光谱Multispectral | 6 | 蓝Blue | ||
绿Green | ||||
红Red | ||||
近红外Near-infrared | ||||
Pleiades-1 | 全色Panchromatic | 0.5 | 全色Panchromatic | 2016-09-17 |
多光谱Multispectral | 2 | 蓝Blue | ||
绿Green | ||||
红Red | ||||
近红外Near-infrared | ||||
Worldview-3 | 全色Panchromatic | 0.5 | 全色Panchromatic | 2016-10-08 |
多光谱Multispectral | 2 | 海岸Coastal | ||
蓝Blue | ||||
绿Green | ||||
黄色Yellow | ||||
红Red | ||||
红边Red edge | ||||
近红外Near-infrared | ||||
近红外2 Near-infrared 2 |
表2
不同时期作物分类总体精度"
获取时间 Date of image acquisition | 光谱角制图SAM | 支持向量机SVM | 决策树DTs | 随机森林RF | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
基于像素 Pixel-based | 基于对象 Object-based | 基于像素 Pixel-based | 基于对象 Object-based | 基于像素 Pixel-based | 基于对像 Object-based | 基于像素 Pixel-based | 基于对象 Object-based | |||||||||
OA | K | OA | K | OA | K | OA | K | OA | K | OA | K | OA | K | OA | K | |
2016-05-10 | 87.47 | 0.81 | 64.62 | 0.47 | 78.35 | 0.67 | 90.25 | 0.85 | 90.93 | 0.86 | 92.12 | 0.88 | 91.27 | 0.87 | 93.52 | 0.90 |
2016-09-07 | 68.56 | 0.52 | 67.36 | 0.50 | 74.23 | 0.62 | 84.04 | 0.76 | 76.90 | 0.66 | 85.36 | 0.78 | 79.37 | 0.69 | 84.40 | 0.77 |
2016-10-08 | 78.30 | 0.67 | 74.21 | 0.61 | 77.53 | 0.67 | 88.88 | 0.83 | 80.13 | 0.70 | 88.77 | 0.83 | 82.14 | 0.73 | 83.82 | 0.78 |
表3
像素与对象水平的枣树及棉花单时期(2016-09-07)分类精度比较"
空间尺度 Spatial scale | 光谱角制图SAM | 支持向量机SVM | 决策树DTs | 随机森林RF | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
枣树Jujube | 棉花Cotton | 枣树Jujube | 棉花Cotton | 枣树Jujube | 棉花Cotton | 枣树Jujube | 棉花Cotton | |||||||||
UA | PA | UA | PA | UA | PA | UA | PA | UA | PA | UA | PA | UA | PA | UA | PA | |
面向对象 Object-oriented | 69.17 | 50.44 | 63.81 | 54.40 | 96.17 | 60.61 | 64.47 | 96.09 | 97.65 | 67.75 | 67.12 | 92.49 | 98.29 | 62.19 | 65.76 | 92.29 |
基于像素 Pixel-based | 70.23 | 52.45 | 44.21 | 49.27 | 93.24 | 43.90 | 52.15 | 87.86 | 96.48 | 47.48 | 54.55 | 91.91 | 96.29 | 52.54 | 57.95 | 90.87 |
图7
基于增益比值的特征重要性 1—4:蓝、绿、红、近红外波段光谱均值 Spectral Blue, Green, Red, NIR Mean;5—8:蓝、绿、红、近红外波段光谱标准差 Spectral Blue, Green, Red, NIR Std;9—12:蓝、绿、红、近红外波段光谱最小值 Spectral Blue, Green, Red, NIR Min;13—16:蓝、绿、红、近红外波段光谱最大值 Spectral Blue, Green, Red, NIR Max;17—20:蓝、绿、红、近红外波段纹理范围 Texture Blue, Green, Red, NIR Range;21—24:蓝、绿、红、近红外波段纹理均值Texture Blue, Green, Red, NIR Mean;25—28:蓝、绿、红、近红外波段纹理方差 Texture Blue, Green, Red, NIR Variance;29—32:蓝、绿、红、近红外波段纹理信息熵 Texture Blue, Green, Red, NIR Entropy;33:对象面积 Area;34:对象外边框周长 Length;35:紧凑型 Compactness;36:凸出的状态 Convexity;37:坚固性 Solidity;38:圆特性 Roundness;39:形状要素 Form Factor;40:延伸性 Elongation;41:矩形形状的度量 Rectangular Fit;42:主方向 Main Direction;43—44:长、短轴长度 Major, Minor Length;45:洞的个数 Number of Holes;46:对象和外轮廓的面积比 Hole Area/Solid Area"
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