基于HJ-1星和GF-1号影像融合特征提取冬小麦种植面积

doi:10.13304/j.nykjdb.2022.0703

中国农业科技导报 ›› 2024, Vol. 26 ›› Issue (2): 109-119.DOI: 10.13304/j.nykjdb.2022.0703

• 智慧农业农机装备 • 上一篇

基于HJ-1星和GF-1号影像融合特征提取冬小麦种植面积

张宏¹^,²(), 李卫国¹^,²(), 张晓东², 卢必慧¹, 张琤琤¹, 李伟³, 马廷淮⁴

^1.江苏省农业科学院农业信息研究所，南京 210014
^2.江苏大学农业工程学院，江苏镇江 212013
^3.江苏大学流体机械工程技术研究中心，江苏镇江 212013
^4.南京信息工程大学国际教育学院，南京 210044

收稿日期:2022-08-23 接受日期:2022-10-24 出版日期:2024-02-15 发布日期:2024-02-04
通讯作者: 李卫国
作者简介:张宏 E-mail：823631981@qq.com；
基金资助:
国家重点研发计划项目(2021YFE0104400);江苏省农业科技自主创新资金项目（CX〔20〕2037）

Extraction of Winter Wheat Planting Area Based on Fusion Features of HJ-1 and GF-1 Image

Hong ZHANG¹^,²(), Weiguo LI¹^,²(), Xiaodong ZHANG², Bihui LU¹, Chengcheng ZHANG¹, Wei LI³, Tinghuai MA⁴

^1.Institute of Agricultural Information，Jiangsu Academy of Agricultural Sciences，Jiangsu Nanjing 210014，China
^2.College of Agricultural Engineering，Jiangsu University，Jiangsu Zhenjiang 212013，China
^3.Fluid Machinery Engineering Technology Research Center，Jiangsu University，Jiangsu Zhenjiang 212013，China
^4.Institute of International Education，Nanjing University of Information of Science and Technology，Jiangsu Nanjing 210044，China

Received:2022-08-23 Accepted:2022-10-24 Online:2024-02-15 Published:2024-02-04
Contact: Weiguo LI

摘要/Abstract

摘要：

为提高基于国产环境与灾害监测预报卫星（HJ-1/CCD）影像大范围提取冬小麦种植面积的精度，以江苏省宿迁市沭阳县为研究区域，对冬小麦拔节期30 m×30 m的HJ-1/CCD多光谱影像和2 m×2 m的高分1号卫星全色影像（GF-1/PMS）进行融合与面向对象分类研究。将GF-1/PMS全色影像进行8、16和24 m重采样，得到4种空间分辨率（含2 m）的全色影像，分别与HJ-1/CCD多光谱影像利用光谱锐化法（Gram-Schmidt，GS）进行融合。通过对融合影像进行质量评价，选择适合研究区冬小麦种植田块格局的适宜尺度影像。将HJ-1/CCD多光谱影像重采样，得到与适宜尺度融合影像相同尺度的影像，在两景影像中分别选取包含光谱、纹理信息的训练融合影像样本（samples of fused image，SFI）和重采样影像样本（samples of resampling image，SRI），采用面向对象分类方法对适宜尺度融合影像（fused image，FI）和重采样影像（resampling image，RI）进行冬小麦种植面积提取。结果表明，16 m×16 m融合影像的效果优于2 m×2 m、8 m×8 m和24 m×24 m 融合影像，其均值、标准差、平均梯度和相关系数分别为161.15、83.01、4.55和0.97。面向对象分类后，SFI对重采样影像RI_16m分类的总体精度为92.22%，Kappa系数为0.90。SFI对融合影像FI_16m分类的总体精度为94.44%，Kappa系数为0.93。SRI对重采样影像RI_16m分类的总体精度为84.44%，Kappa系数为0.80。SFI对融合影像FI_16m分类效果最好，说明基于融合影像和融合影像提取样本（SFI）结合的面向对象分类方法能准确提取冬小麦种植面积。另外，重采样影像和融合影像提取样本（SFI）相结合的面向对象分类方法也可较好提取冬小麦种植面积。为利用国产中空间分辨率HJ-1/CCD卫星和高分1号卫星融合影像有效提取大区域冬小麦种植面积信息提供了参考。

关键词: HJ-1/CCD卫星影像, GF-1/PMS卫星影像, 冬小麦种植面积, 特征提取, 影像融合, 面向对象分类

Abstract:

In order to improve the accuracy of extracting large-scale winter wheat planting area from the data of domestic environment and disaster monitoring and forecasting satellite （HJ-1/CCD）. This study took Shuyang County， Suqian City， Jiangsu Province as the research area. The fusion and object-oriented classification of the 30 m×30 m HJ-1/CCD multispectral image and the 2 m×2 m GF-1 panchromatic image （GF-1/PMS） at the jointing stage of winter wheat were carried out. The GF-1/PMS panchromatic images were resampled at 8， 16 and 24 m， and panchromatic images with four spatial resolutions （including 2 m） were obtained， which were fused with HJ-1/CCD multispectral images by Gram-Schmidt （GS）， respectively. Through the quality evaluation of the fused image， the appropriate scale image suitable for the pattern of winter wheat planting fields in the study area was selected. The HJ-1/CCD multispectral image was resampled to obtain an image with the same scale as the appropriate scale fused image. In the 2 scene images， the training samples SFI （samples of fused image） and SRI （samples of resampling image） containing spectral and texture information were selected respectively， the object-oriented classification method was used to extract the planting area of winter wheat from fused image （FI） and resampling image （RI）. The results showed that the fusion effect of 16 m×16 m fused images was better than 2 m×2 m， 8 m×8 m and 24 m×24 m fused images， and the mean， standard deviation， average gradient and correlation coefficient were 161.15， 83.01， 4.55 and 0.97. After object-oriented classification， the overall accuracy of SFI for the classification of resampled image RI_16m was 92.22%， and the Kappa coefficient was 0.90. The overall accuracy of SFI for the classification of fused image FI_16m was 94.44%， and the Kappa coefficient was 0.93. The overall accuracy of SRI for the classification of resampled image RI_16m was 84.44%， and the Kappa coefficient was 0.80. The classification effect of SFI for the fused image FI_16m was the best， indicating that the object-oriented classification method combined with the fused image and the extraction samples of fused image （SFI） could accurately extract the winter wheat planting area. In addition， the object-oriented classification method combining resampling image and the extraction samples of fused image （SFI） could also better extract the winter wheat planting area. This method provided a reference for the effective extraction of large-scale winter wheat planting area information combined with domestic medium-spatial resolution HJ-1/CCD images and GF-1 satellite images.

Key words: HJ-1/CCD satellite image, GF-1/PMS satellite image, winter wheat planting area, features extraction, image fusion, object-oriented classification

中图分类号:

S127

张宏, 李卫国, 张晓东, 卢必慧, 张琤琤, 李伟, 马廷淮. 基于HJ-1星和GF-1号影像融合特征提取冬小麦种植面积[J]. 中国农业科技导报, 2024, 26(2): 109-119.

Hong ZHANG, Weiguo LI, Xiaodong ZHANG, Bihui LU, Chengcheng ZHANG, Wei LI, Tinghuai MA. Extraction of Winter Wheat Planting Area Based on Fusion Features of HJ-1 and GF-1 Image[J]. Journal of Agricultural Science and Technology, 2024, 26(2): 109-119.

图/表 8

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融合尺度 Scale of fusion	均值 Mean value	标准差 Standard deviation	平均梯度 Average gradient	相关系数 Correlation coefficient
2 m×2 m	160.98±8.21 a	78.60±4.09 a	1.81±0.06 c	0.95±0.06 a
8 m×8 m	161.01±5.31 a	82.93±3.73 a	2.97±0.12 c	0.96±0.05 a
16 m×16 m	161.15±6.45 a	83.01±4.48 a	4.55±0.23 c	0.97±0.06 a
24 m×24 m	165.03±9.08 a	83.59±5.02 a	6.13±0.36 c	0.85±0.03 b

融合尺度 Scale of fusion	均值 Mean value	标准差 Standard deviation	平均梯度 Average gradient	相关系数 Correlation coefficient
2 m×2 m	160.98±8.21 a	78.60±4.09 a	1.81±0.06 c	0.95±0.06 a
8 m×8 m	161.01±5.31 a	82.93±3.73 a	2.97±0.12 c	0.96±0.05 a
16 m×16 m	161.15±6.45 a	83.01±4.48 a	4.55±0.23 c	0.97±0.06 a
24 m×24 m	165.03±9.08 a	83.59±5.02 a	6.13±0.36 c	0.85±0.03 b

植被类型 Vegetation type	同质性 Homogeneity	熵 Entropy	角二阶距 Angular second moment	对比度 Contrast
冬小麦 Winter wheat	0.80	1.39	0.31	0.66
油菜 Rape	0.72	1.12	0.32	0.70
其他植被 Other vegetation	0.60	1.27	0.21	1.33

植被类型 Vegetation type	同质性 Homogeneity	熵 Entropy	角二阶距 Angular second moment	对比度 Contrast
冬小麦 Winter wheat	0.80	1.39	0.31	0.66
油菜 Rape	0.72	1.12	0.32	0.70
其他植被 Other vegetation	0.60	1.27	0.21	1.33

分类组合 Classification combinations	训练样本 Training sample	影像 Image	精度 Accuracy	水体 Water/%	建筑和道路 Buildings and roads/%	冬小麦 Winter wheat/ %	油菜 Rape/ %	其他植被 Other vegetation/ %	总体精度 Overall Accuracy/ %	Kappa系数 Kappa Coefficient
组合一 Combination 1	SFI	RI_16m	生产者精度 Producer accuracy	90.00	92.00	97.78	85.00	91.43	92.22	0.90
组合一 Combination 1	SFI	RI_16m	用户精度 User accuracy	96.43	93.88	91.67	89.47	88.89	92.22	0.90
组合二 Combination 2	SFI	FI_16m	生产者精度 Producer accuracy	93.33	96.00	97.78	90.00	91.43	94.44	0.93
组合二 Combination 2	SFI	FI_16m	用户精度 User accuracy	100	90.57	100	85.71	94.12	94.44	0.93
组合三 Combination 3	SRI	RI_16m	生产者精度 Producer accuracy	83.33	86.00	93.33	70.00	80.00	84.44	0.80
组合三 Combination 3	SRI	RI_16m	用户精度 User accuracy	83.33	82.70	91.30	73.68	84.85	84.44	0.80

基于HJ-1星和GF-1号影像融合特征提取冬小麦种植面积

Extraction of Winter Wheat Planting Area Based on Fusion Features of HJ-1 and GF-1 Image

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[1]	石瑞瑶,田有文*,赖兴涛,古文君. 基于机器视觉的苹果品质在线分级检测[J]. 中国农业科技导报, 2018, 20(3): 80-86.
[2]	蒋艳娜1,肖伯祥2,郭新宇1,2,杨宝祝1,2*. 基于视频的植物动画合成方法[J]. , 2015, 17(1): 115-121.