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作物学报 ›› 2020, Vol. 46 ›› Issue (7): 1099-1111.doi: 10.3724/SP.J.1006.2020.94134

• 耕作栽培·生理生化 • 上一篇    下一篇

基于多时相双极化SAR数据的作物种植面积提取

古丽努尔·依沙克,买买提·沙吾提(),马春玥   

  1. 新疆大学资源与环境科学学院 / 新疆大学绿洲生态教育部重点实验室 / 新疆智慧城市与环境建模普通高校重点实验室, 新疆乌鲁木齐 830046
  • 收稿日期:2019-09-10 接受日期:2020-03-24 出版日期:2020-07-12 网络出版日期:2020-04-13
  • 通讯作者: 买买提·沙吾提
  • 作者简介:E-mail: Gulnur_E@163.com
  • 基金资助:
    国家自然科学基金项目(41361016);国家自然科学基金项目(41461051)

Extraction of crop acreage based on multi-temporal and dual-polarization SAR data

Gulnur ISAK,Mamat SAWUT(),MA Chun-Yue   

  1. College of Resources and Environmental Sciences, Xinjiang University / Ministry of Education Key Laboratory of Oasis Ecology, Xinjiang University / Key Laboratory for Wisdom City and Environmental Modeling, Xinjiang University, Urumqi 830046, Xinjiang, China
  • Received:2019-09-10 Accepted:2020-03-24 Published:2020-07-12 Published online:2020-04-13
  • Contact: SAWUT Mamat
  • Supported by:
    National Natural Science Foundation of China(41361016);National Natural Science Foundation of China(41461051)

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摘要：

及时准确地获取农作物的空间分布信息和种植面积, 在农业生产管理与农业政策的制定等方面具有非常重要的作用。本文以多时相Sentinel-1A影像(4月17日、5月5日、6月16日、7月22日、8月27日、9月2日)为主要数据源, 根据研究区作物的物候特征, 提取棉花、玉米和果树在不同生长期的后向散射系数(Sigma)和归一化后向散射系数(Gamma)。通过对作物不同极化、不同时相后向散射系数的统计, 建立散射特征时序变化曲线, 并分析其特征。利用人工神经网络(Artificial neural network)、支持向量机(Support vector machine)和随机森林(Random forest) 3种分类方法对研究区的主要农作物进行分类识别以及种植面积提取, 并对分类结果对比分析和验证。结果表明, 1)棉花的后向散射系数在6月现蕾期和7月开花期明显上升, 8月份达最高值, 变化特征最明显, 易与其他作物区分; 玉米和果树的后向散射系数在9月份与其他地物之间表现出显著差异。2)相较于神经网络和支持向量机, 随机森林的分类效果最好, 总体精度达88.97%。其中, 对棉花和果园的分类精度为90.88%和93.17%, 对玉米的分类效果最差, 仅有71.6%。综上所述, 多时相双极化SAR数据在不同类型作物的识别及面积提取方面具有一定的应用潜力。

关键词: 多时相, Sentinel-1A, 后向散射系数, 作物面积提取

Abstract:

It plays a vital role in agricultural production management and agricultural policy formulation to acquire spatial distribution information and planting area of crops timely and accurately. In this paper, according to the phenological characteristics of crops, the back scattering coefficients (Sigma) and normalized back scattering coefficients (Gamma) of cotton, corn and orchard in different growth periods were extracted by using multi-temporal Sentinel-1A images (April 17, May 5, June 16, July 22, August 27, and September 2). The time-series change curves of the scattering characteristics were established and the characteristics were analyzed by using backscattering coefficients of crops with different polarizations and different time phases. Artificial neural network (ANN), support vector machine (SVM), and random forest (RF) were used to classify and identify cotton, corn and orchard. On this basis, the classification results were compared and analyzed, and the accuracy was verified. The backscattering coefficient of cotton increased significantly in June and July with the highest value in August, of which the changes were most obvious and easily distinguished from other crops. The backscattering coefficients of corn and fruit trees were significantly different from those of other land objects in September. The optimal classification was obtained by using random forest (the overall accuracy was up to 88.97%) than by using neural network and support vector machine. The classification accuracy for cotton and orchard was 90.88% and 93.17%, and the classification effect of corn is the worst, only 71.6%. In conclusion, multi-temporal and dual-polarization SAR data has certain application potential in the identification and area extraction of different crops.

Key words: multi-temporal, Sentinel-1A, backscattering coefficient, crop area extraction

图1

研究区及样点分布示意图"

表1

Sentinel-1A SAR数据主要参数"

获取时间
Acquisition time		 入射角
Incidence angle		 极化方式
Polarization		 产品类型
Product type		 分辨率
Resolution (m)
2018/4/17 39.2° VV, VH GRD 5×10
2018/5/05 39.2° VV, VH GRD 5×10
2018/6/16 39.2° VV, VH GRD 5×10
2018/7/22 39.2° VV, VH GRD 5×10
2018/8/27 39.2° VV, VH GRD 5×10
2018/9/02 39.2° VV, VH GRD 5×10

表2

典型作物不同时相J-M距离"

类型
Type		 2018/04/17 2018/05/05 2018/06/16 2018/07/22 2018/08/27 2018/09/02
Sigma Gamma Sigma Gamma Sigma Gamma Sigma Gamma Sigma Gamma Sigma Gamma
棉花-玉米Cotton-corn 0.01 0.01 0.09 0.09 0.53 0.62 0.58 0.59 0.81 0.81 0.12 0.12
棉花-果园Cotton-orchard 0.19 0.18 0.06 0.06 0.29 0.26 0.06 0.06 0.07 0.07 0.40 0.40
棉花-水体Cotton-water 0.97 0.98 1.52 1.51 1.98 1.96 1.76 1.77 1.99 1.99 1.93 1.92
棉花-建筑Cotton-building 1.65 1.65 1.84 1.83 1.70 1.67 1.66 1.65 1.67 1.67 1.64 1.64
玉米-果园Corn-orchard 0.14 0.14 0.04 0.04 1.22 1.22 0.93 0.94 1.15 1.15 0.12 0.12
玉米-水体Corn-water 1.04 1.05 1.49 1.49 1.98 1.94 1.34 1.33 1.99 1.99 1.97 1.97
玉米-建筑Corn-building 1.65 1.65 1.80 1.80 1.89 1.87 1.86 1.86 1.91 1.91 1.50 1.50
果园-水体Orchard-water 1.40 1.40 1.61 1.61 1.99 1.99 1.88 1.88 1.99 1.99 1.99 1.99
果园-建筑Orchard-building 1.55 1.55 1.77 1.77 1.70 1.63 1.66 1.65 1.74 1.74 1.54 1.54
建筑-水体Building-water 1.90 1.90 1.99 1.99 1.99 1.99 1.96 1.96 1.99 1.99 1.98 1.99

表3

典型作物不同波段组合J-M距离"

类型
Type		 6/16 06/16+08/27 06/16+09/02 08/27+09/02 6/16+08/27+09/02
Sigma Gamma Sigma Gamma Sigma Gamma Sigma Gamma Sigma Gamma
棉花-玉米Cotton-corn 0.53 0.62 0.88 0.93 0.83 0.81 1.02 0.93 1.50 1.49
棉花-果园Cotton-orchard 0.29 0.26 0.55 0.54 1.10 1.09 0.85 0.86 1.47 1.45
棉花-水体Cotton-water 1.98 1.96 1.99 1.98 1.98 1.99 1.99 1.97 1.99 1.99
棉花-建筑Cotton-building 1.70 1.67 1.78 1.77 1.77 1.80 1.80 1.78 1.96 1.92
玉米-果园Corn-orchard 1.22 1.22 1.35 1.34 1.35 1.37 1.40 1.38 1.52 1.49
玉米-水体Corn-water 1.98 1.94 1.99 1.96 1.99 1.98 1.98 1.97 1.99 1.98
玉米-建筑Corn-building 1.89 1.87 1.84 1.83 1.78 1.74 1.73 1.70 1.81 1.81
果园-水体Orchard-water 1.99 1.99 1.99 1.98 1.99 1.99 1.98 1.99 1.99 1.99
果园-建筑Orchard-building 1.70 1.63 1.78 1.76 1.65 1.63 1.67 1.66 1.73 1.73
建筑-水体Building-water 1.99 1.99 1.99 1.99 1.99 1.98 1.99 1.99 1.99 1.99

图2

技术路线图"

图3

多时相SAR典型地物后向散射统计图 VV: 入射波为垂直偏振时的垂直偏振后向散射; VH: 入射波为垂直偏振时的水平偏振后向散射。"

图4

典型地物后向散射特征时间序列变化 VV: 入射波为垂直偏振时的垂直偏振后向散射; VH: 入射波为垂直偏振时的水平偏振后向散射。"

图5

多时相双极化SAR影像分类结果 ANN: 人工神经网络; SVM: 支持向量机; RF: 随机森林。"

表4

不同农作物种植面积"

类型
Type		 面积
Area (hm2)		 占全图比例
Proportion of total graph (%)
棉花Cotton 228842 30.23
玉米Corn 45183 9.47
果园Orchard 71665 5.97

表5

时间序列数据不同分类方法精度验证"

类型
Type		 人工神经网络 ANN 支持向量机 SVM 随机森林 RF
Sigma Gamma Sigma Gamma Sigma Gamma
Prod.
Acc		 User.
Acc		 Prod.
Acc		 User.
Acc		 Prod.
Acc		 User.
Acc		 Prod.
Acc		 User.
Acc		 Prod.
Acc		 User.
Acc		 Prod.
Acc		 User.
Acc
果园Orchard 90.92 73.86 90.78 81.01 89.03 84.55 92.23 84.67 93.17 90.93 91.50 85.42
玉米Corn 17.31 93.66 46.35 88.01 56.45 88.54 48.51 91.65 71.60 93.19 53.38 89.70
棉花Cotton 85.96 86.72 79.06 94.85 89.53 82.11 81.34 99.89 90.88 88.43 86.52 92.61
建筑Building 84.11 98.74 88.57 93.85 84.02 98.74 85.65 90.45 87.14 97.99 82.32 99.57
水体Water 82.07 99.32 44.60 83.42 86.57 97.88 73.52 97.39 92.68 98.01 75.49 99.38
其他Other 97.30 58.18 98.09 52.71 95.74 70.50 98.23 59.06 95.46 85.62 98.79 80.72
Overall. Acc 77.85% 76.42% 84.36% 81.3% 88.97% 82.57%
Kappa 0.73 0.71 0.81 0.77 0.87 0.79

表6

棉花种植面积精度验证"

编号
No.		 棉花样方面积
Sample area of
cotton (hm2)		 棉花分类面积
Classified area of
cotton (hm2)		 精度
Accuracy
(%)		 编号
No.		 非棉花样方面积
Sample area of
non-cotton (hm2)		 非棉花分类面积Classified area of non-cotton (hm2) 精度
Accuracy
(%)
1 43.37 43.58 99.53 21 1.40 1.39 98.76
2 102.52 104.98 97.60 22 1.74 1.77 98.35
3 37.12 35.69 96.14 23 2.19 2.26 96.97
4 34.69 33.20 95.69 24 1.24 0.88 95.40
5 31.38 32.88 95.23 25 2.08 1.98 95.14
6 8.44 8.87 94.90 26 32.20 33.82 94.98
7 54.79 51.59 94.15 27 31.13 33.02 93.95
8 8.24 7.51 91.20 28 24.27 21.51 88.62
9 106.06 116.13 90.51 29 16.46 14.44 87.75
10 117.49 129.85 89.48 30 1.71 1.43 83.84
11 131.91 114.47 86.78 31 36.74 29.42 80.08
12 28.15 24.25 86.13 32 72.66 52.31 72.00
13 36.63 31.53 86.08 33 1.18 0.85 71.94
14 105.81 90.80 85.81 34 4.28 5.60 69.09
15 94.85 80.10 84.44 35 21.44 13.70 63.89
16 26.81 21.82 81.40 36 47.88 30.51 63.72
17 18.27 11.80 64.60 37 47.44 26.79 56.47
18 38.16 17.76 46.54 38 3.69 5.42 53.42
19 18.51 8.59 46.40 39 30.36 7.85 25.87
20 28.68 12.81 44.67 40 6.67 1.19 17.86
棉花样方总面积Total sample area (hm2) 1147.85 非棉花样方总面积Total sample area (hm2) 434.81
棉花分类总面积Total classified area (hm2) 1058.03 非棉花分类总面积Total classified area (hm2) 334.89
总精度 Overall accuracy: 92.17% 总精度 Overall accuracy: 73.64%
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