作物 | 训练样本 | 测试样本 | |||
地块 | 像素 | 地块 | 像素 | ||
玉米 | 49 | 59539 | 45 | 55786 | |
花生 | 79 | 212440 | 86 | 241625 | |
水稻 | 45 | 100386 | 43 | 94936 | |
大豆 | 30 | 27994 | 29 | 26862 |
Citation: | Guo Qile, Li Junling, Guo Peng. Extraction of peanut planting area based on dual-temporal remote sensing features of crops. J Appl Meteor Sci, 2022, 33(2): 218-230. DOI: 10.11898/1001-7313.20220208. |
Table 1 Experimental measurements for each crop type
作物 | 训练样本 | 测试样本 | |||
地块 | 像素 | 地块 | 像素 | ||
玉米 | 49 | 59539 | 45 | 55786 | |
花生 | 79 | 212440 | 86 | 241625 | |
水稻 | 45 | 100386 | 43 | 94936 | |
大豆 | 30 | 27994 | 29 | 26862 |
Table 2 Information of selected features
编码 | 特征参量 | 类型 |
S1~S4 | 2020-08-01时相的红、绿、蓝和近红外波段 | 光谱特征 |
S5~S8 | 2020-08-15时相的红、绿、蓝和近红外波段 | |
V1 | 2020-08-01时相的归一化植被指数 | 植被指数特征 |
V2 | 2020-08-01时相的绿度总和指数 | |
V3 | 2020-08-01时相的比值植被指数 | |
V4 | 2020-08-01和2020-08-15两时相归一化植被指数差 | |
T1 | 2020-08-01时相纹理平均值 | 纹理特征 |
T2 | 2020-08-15时相纹理平均值 | |
T3 | 2020-08-15和2020-08-01两时相纹理平均值差 |
Table 3 The J-M distances between peanut and the following crop in different feature spaces
作物 | 原始影像 | 构造的特征空间 | |||||
2020-08-01 | 2020-08-15 | A | B | C | D | ||
大豆 | 1.9199 | 1.4455 | 1.9527 | 1.9564 | 1.9967 | 1.9970 | |
水稻 | 1.9669 | 1.4921 | 1.9998 | 1.9999 | 2.0000 | 2.0000 | |
玉米 | 1.7635 | 1.9935 | 1.8510 | 1.8616 | 1.9916 | 1.9922 |
[1] |
Tang H J, Zhou Q B, Liu J, et al. Wheat Mapping Using High Resolution Remote Sensing Data. Beijing: Science Press, 2016.
|
[2] |
Luo J N, Liu L W. Research and implementation of remote sensing big data distributed technology. J Appl Meteor Sci, 2017, 28(5): 621-631. doi: 10.11898/1001-7313.20170510
|
[3] |
Ye Q L, Xu D P, Zhang D. Remote sensing image classification based on deep learning features and support vector machine. Journal of Forestry Engineering, 2019, 4(2): 125-131. https://www.cnki.com.cn/Article/CJFDTOTAL-LKKF201902020.htm
|
[4] |
Kuang Q M, Yu T Z. Review of leveraging AI for exploitation of satellite observations. Advances in Meteorological Science and Technology, 2020, 10(3): 21-29. doi: 10.3969/j.issn.2095-1973.2020.03.004
|
[5] |
Sun J, Cao Z, Li H, et al. Application of artificial intelligence technology to numerical weather prediction. J Appl Meteor Sci, 2021, 32(1): 1-11. doi: 10.11898/1001-7313.20210101
|
[6] |
Zhang P, Hu S G. Fine crop classification by remote sensing in complex planting area based on field parcel. Transactions of the CSAE, 2019, 35(20): 125-134. doi: 10.11975/j.issn.1002-6819.2019.20.016
|
[7] |
Jin Z Q, Wang X M, Bao Y S, et al. Squall line identification method based on convolution neural network. J Appl Meteor Sci, 2021, 32(5): 580-591. doi: 10.11898/1001-7313.20210506
|
[8] |
Li Y, Chen H L. Review of machine learning approaches for modern agrometeorology. J Appl Meteor Sci, 2020, 31(3): 257-266. doi: 10.11898/1001-7313.20200301
|
[9] |
Deng J Z, Liu Q D, Wang C W, et al. Crop classification based on multitemporal Sentinel-2 satellite imagery. Guangdong Agricultural Sciences, 2020, 47(4): 129-138. https://www.cnki.com.cn/Article/CJFDTOTAL-GDNY202004019.htm
|
[10] |
Liu J, Wang L M, Teng F, et al. Impact of red-edge waveband of RapidEye satellite on estimation accuracy of crop planting area. Transactions of the CSAE, 2016, 32(13): 140-148. doi: 10.11975/j.issn.1002-6819.2016.13.020
|
[11] |
Wei P F, Xu X G, Yang G J, et al. Remote sensing classification of crops based on the change characteristics of multi-phase vegetation index. Journal of Agricultural Science and Technology, 2019, 21(2): 54-61. https://www.cnki.com.cn/Article/CJFDTOTAL-NKDB201902008.htm
|
[12] |
Zhang Y, Yang W, Sun Y, et al. Fusion of multispectral aerial imagery and vegetation indices for machine learning-based ground classification. Remote Sensing, 2021, 13(8): 1411. doi: 10.3390/rs13081411
|
[13] |
Liu E H, Zhou G S, Zhou L, et al. Remote sensing inversion of leaf and canopy water content in different growth stages of summer maize. J Appl Meteor Sci, 2020, 31(1): 52-62. doi: 10.11898/1001-7313.20200105
|
[14] |
Yang L, Han L J, Song J L, et al. Monitoring and evaluation of high temperature and heat damage of summer maize based on remote sensing data. J Appl Meteor Sci, 2020, 31(6): 749-758. doi: 10.11898/1001-7313.20200610
|
[15] |
Zhang Y G, Tao Y X, Luo X B, et al. Hyperspectral image classification based on feature importance. Infrared Technology, 2020, 42(12): 1185-1191. https://www.cnki.com.cn/Article/CJFDTOTAL-HWJS202012010.htm
|
[16] |
Yang S T, Gu L J, Li X F, et al. Crop classification method based on optimal feature selection and hybrid CNN-RF networks for multi-temporal remote sensing imagery. Remote Sensing, 2020, 12(19): 3119. doi: 10.3390/rs12193119
|
[17] |
Orynbaikyzy A, Gessner U, Mack B, et al. Crop type classification using fusion of Sentinel-1 and Sentinel-2 data: Assessing the impact of feature selection, optical data availability, and parcel sizes on the accuracies. Remote Sensing, 2020, 12(17): 2779. doi: 10.3390/rs12172779
|
[18] |
Deng L Y, Shen Z F, Ke Y M, et al. Winter wheat planting area extraction using multi-temporal remote sensing images based on field parcel. Transactions of the CSAE, 2018, 34(21): 157-164. doi: 10.11975/j.issn.1002-6819.2018.21.019
|
[19] |
Ma Q R, Zuo X, Hu C D, et al. Effects of waterlogging on photosynthetic characteristics and yield of summer peanut. J Appl Meteor Sci, 2021, 32(4): 479-490. doi: 10.11898/1001-7313.20210409
|
[20] |
Zhou S D, Jing L Y, Meng H K, et al. Analysis on spatial distribution evolution of main peanut production areas in China and its influencing factors. Journal of Agrotechnical Economics, 2018(3): 100-109. https://www.cnki.com.cn/Article/CJFDTOTAL-NYJS201803009.htm
|
[21] |
Wei S C, Li K W, Zhang J Q, et al. Hazard assessment of peanut drought and flood disasters in Huang-Huai-Hai Region. J Appl Meteor Sci, 2021, 32(5): 629-640. doi: 10.11898/1001-7313.20210510
|
[22] |
Cheng Z S, Li Y R, Xu G Z, et al. Present situation of peanut production, scientific research and industrialization development countermeasures in Hebei Province. Journal of Peanut Science, 2003, 32(Suppl I): 60-63. https://www.cnki.com.cn/Article/CJFDTOTAL-PEAN2003S1011.htm
|
[23] |
Zhou S, Han B, Li S, et al. Current situation and development countermeasure of peanut in Henan Province. Tianjin Agricultural Sciences, 2021, 27(8): 56-59. doi: 10.3969/j.issn.1006-6500.2021.08.011
|
[24] |
Zhu B C, Li Z G, Wang Y T, et al. Consideration on development of peanut industry in Henan-Investigation on the development of peanut industry in Zhengyang County, Zhumadian City. China Grain Economy, 2019(10): 52-55. doi: 10.3969/j.issn.1007-4821.2019.10.015
|
[25] |
Liu N, Xiong A Y, Zhang Q, et al. Development of basic dataset of severe convective weather for artificial intelligence training. J Appl Meteor Sci, 2021, 32(5): 530-541. doi: 10.11898/1001-7313.20210502
|
[26] |
Wang N, Li Q Z, Du X, et al. Identification of main crops based on the univariate feature selection in Subei. Journal of Remote Sensing, 2017, 21(4): 519-530. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB201704004.htm
|
[27] |
Chen Y W, Huang X M, Li Y, et al. Ensemble learning for bias correction of station temperature forecast based on ECMWF products. J Appl Meteor Sci, 2020, 31(4): 494-503. doi: 10.11898/1001-7313.20200411
|
[28] |
Han F, Yang L, Zhou C X, et al. An experimental study of the short-time heavy rainfall event forecast based on ensemble learning and sounding data. J Appl Meteor Sci, 2021, 32(2): 188-199. doi: 10.11898/1001-7313.20210205
|
[29] |
Kononenko I. Estimating Attributes: Analysis and Extensions of RELIEF//European Conference on Machine Learning, 1994: 171-182.
|
[30] |
Liu Y, Meng Q Y, Wang Y J, et al. A method for estimating impervious surface percentage based on feature optimization and SVM. Geography and Geo-Information Science, 2018, 34(1): 24-31. doi: 10.3969/j.issn.1672-0504.2018.01.005
|
[31] |
Tong Q X, Zhang B, Zheng L F. Hyperspectral Remote Sensing: Principles, Techniques, and Applications. Beijing: Higher Education Press, 2006.
|
[32] |
Geng X R. Target Detection and Classification for Hyperspectral Imagery. Beijing: University of Chinese Academy of Sciences(Institute of Remote Sensing Applications), 2005.
|
[33] |
Deng N Y, Tian Y J. A New Method in Data Mining: Support Vector Machine. Beijing: Science Press, 2004.
|
[34] |
Breiman L. Random forests. Machine Learning, 2001, 45(1): 5-32. doi: 10.1023/A:1010933404324
|
[35] |
Congalton R G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 1991, 37(1): 35-46. doi: 10.1016/0034-4257(91)90048-B
|
[36] |
Zhang Y S, Ouyang F, Yuan Z M. Change characteristics of landscape pattern in farmland ecosystems in North China. Ecological Science, 2018, 37(4): 114-122. https://www.cnki.com.cn/Article/CJFDTOTAL-STKX201804014.htm
|
[37] |
Luo Z J, Zhao Y, Zhao J, et al. Defining of permanent basic farmland based on landscape pattern and spatial autocorrelation. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(10): 195-204. doi: 10.6041/j.issn.1000-1298.2018.10.022
|