Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

Abstract

Rice is an important food crop for human beings. Accurately distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring, yield estimation, and phenological monitoring, which has significance for the development of modern agriculture. Compact polarimetric (CP) synthetic aperture radar (SAR) provides multichannel information and shows great potential for rice monitoring and mapping. Currently, the use of machine learning methods to build classification models is a controversial topic. In this paper, the advantages of CP SAR data, the powerful learning ability of machine learning, and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice paddies. First, CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data sets. Second, 20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data sets. In addition, we fully considered the change degree of CP SAR parameters on a time scale (ΔCP_DoY). Six machine learning methods were employed to carry out the fine classification of rice paddies. The results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters of ΔCP_DoY. The overall accuracy is greater than 95.05%, and the Kappa coefficient is greater than 0.937. Among them, the random forest (RF) and support vector machine (SVM) achieve the best results, with an overall accuracy reaching 97.32% and 97.37%, respectively, and Kappa coefficient values reaching 0.965 and 0.966, respectively. For the two types of rice paddies, the average accuracy of the transplant hybrid (T-H) rice paddy is greater than 90.64%, and the highest accuracy is 95.95%. The average accuracy of direct-sown japonica (D-J) rice paddy is greater than 92.57%, and the highest accuracy is 96.13%.

Graphical abstract

Keywords

compact polarimetric (CP) SAR / rice paddy / machine learning / fine classification / multitemporal

References

[1] Abubakar G A, Wang K, Shahtahamssebi A, Xue X, Belete M, Gudo A J A, Mohamed Shuka K A, Gan M (2020). Mapping maize fields by using multi-temporal Sentinel-1A and Sentinel-2A images in Makarfi, Northern Nigeria, Africa.Sustainability (Basel), 12(6): 2539

[2] BetbederJ, Rapinel S, CorpettiT, PottierE, CorgneS, Hubert-MoyL (2013). Multi-temporal classification of TerraSAR-X data for wetland vegetation mapping. In: Remote Sensing for Agriculture, Ecosystems, and Hydrology XV part of the 20th International Symposium on Remote Sensing, Dresden, Germany, 2013 Sep 23–26

[3] Bouvet A, Le Toan T, Lam-Dao N (2009). Monitoring of the rice cropping system in the Mekong Delta using ENVISAT/ASAR dual polarization data.IEEE Trans Geosci Remote Sens, 47(2): 517–526

[4] Breiman L (2001). Random forests.Mach Learn, 45(1): 5–32

[5] BressanM, VitriaJ (2002). Improving naive Bayes using class-conditional ICA. In: Garijo F J, Riquelme J C, Toro M, eds. Advances in Artificial Intelligence - Iberamia 2002

[6] Brisco B, Li K, Tedford B, Charbonneau F, Yun S, Murnaghan K (2013). Compact polarimetry assessment for rice and wetland mapping.Int J Remote Sens, 34(6): 1949–1964

[7] ChenH, LiH (2008). Rice recognition using multi-temporal and dual polarized synthetic aperture radar images. In: International Colloquium on Computing, Communication, Control and Management, Guangzhou, China, 2008 Aug 04–05

[8] Cloude S R, Goodenough D G, Chen H (2012). Compact decomposition theory.IEEE Geosci Remote Sens Lett, 9(1): 28–32

[9] de Castro Filho H C, de Carvalho O A Junior, Ferreira de Carvalho O L, de Bem P P, de Moura R S, de Albuquerque A O, Silva C R, Guimaraes Ferreira P H, Guimaraes R F, Trancoso Gomes R A (2020). Rice crop detection using LSTM, Bi-LSTM, and machine learning models from Sentinel-1 time series.Remote Sens (Basel), 12(16): 2655

[10] Dusseux P, Corpetti T, Hubert-Moy L, Corgne S (2014). Combined use of multi-temporal optical and radar satellite images for grassland monitoring.Remote Sens (Basel), 6(7): 6163–6182

[11] Gašparović M, Dobrinic D (2021). Green infrastructure mapping in urban areas using Sentinel-1 imagery.Croat J For Eng, 42(2): 337–356

[12] Guo X, Li K, Wang Z, Li H, Yang Z (2018). Fine classification of rice with multi-temporal compact polarimetric SAR based on SVM + SFS strategy.Remote Sens Land Resour, 30(4): 20

[13] Harzevili N S, Alizadeh S H (2021). Analysis and modeling conditional mutual dependency of metrics in software defect prediction using latent variables.Neurocomputing, 460: 309–330

[14] HeK, ZhangX, RenS, SunJ (2015). Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: IEEE International Conference on Computer Vision, Santiago, CHILE, 2015 Dec 11–18

[15] Ho T K (1998). The random subspace method for constructing decision forests.IEEE Trans Pattern Anal Mach Intell, 20(8): 832–844

[16] Huang G B, Zhou H, Ding X, Zhang R (2012). Extreme learning machine for regression and multiclass classification.IEEE Trans Syst Man Cybern B Cybern, 42(2): 513–529

[17] Inoue Y, Sakaiya E (2013). Relationship between X-band backscattering coefficients from high-resolution satellite SAR and biophysical variables in paddy rice.Remote Sens Lett, 4(3): 288–295

[18] KohaviR, JohnG H (1997). Wrappers for feature subset selection. Artif Intell, 97(1–2): 273–324

[19] Kucuk C, Taskin G, Erten E (2016). Paddy-rice phenology classification based on machine-learning methods using multitemporal Co-Polar X-Band SAR images.IEEE J Sel Top Appl Earth Obs Remote Sens, 9(6): 2509–2519

[20] Kurosu T, Fujita M, Chiba K (1995). Monitoring of rice crop growth from space using the ERS-1 C-Band SAR.IEEE Trans Geosci Remote Sens, 33(4): 1092–1096

[21] Lardeux C, Frison P L, Tison C, Souyris J C, Stoll B, Fruneau B, Rudant J P (2009). Support vector machine for multifrequency SAR polarimetric data classification.IEEE Trans Geosci Remote Sens, 47(12): 4143–4152

[22] Lardeux C, Frison P L, Tison C, Souyris J C, Stoll B, Fruneau B, Rudant J P (2011). Classification of tropical vegetation using multifrequency partial SAR polarimetry.IEEE Geosci Remote Sens Lett, 8(1): 133–137

[23] LeCun Y, Bengio Y, Hinton G (2015). Deep learning.Nature, 521(7553): 436–444

[24] Le Toan T, Laur H, Mougin E, Lopes A (1989). Multitemporal and dual-polarization observations of agricultural vegetation covers by X-Band SAR images.IEEE Trans Geosci Remote Sens, 27(6): 709–718

[25] Le Toan T, Ribbes F, Wang L F, Floury N, Ding K H, Kong J A, Fujita M, Kurosu T (1997). Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results.IEEE Trans Geosci Remote Sens, 35(1): 41–56

[26] Li K, Zhang F, Shao Y, Cai A, Yuan J, Touzi R (2011). Polarization signature analysis of paddy rice in southern China.Can J Rem Sens, 37(1): 122–135

[27] Li K, Brisco B, Yun S, Touzi R (2012a). Polarimetric decomposition with RADARSAT-2 for rice mapping and monitoring.Can J Rem Sens, 38(2): 169–179

[28] Li W, Prasad S, Fowler J E, Bruce L M (2012b). Locality-preserving dimensionality reduction and classification for hyperspectral image analysis.IEEE Trans Geosci Remote Sens, 50(4): 1185–1198

[29] Ndikumana E, Ho Tong Minh D, Baghdadi N, Courault D, Hossard L (2018). Deep recurrent neural network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France.Remote Sens (Basel), 10(8): 1217

[30] Onojeghuo A O, Blackburn G A, Wang Q, Atkinson P M, Kindred D, Miao Y (2018). Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data.Int J Remote Sens, 39(4): 1042–1067

[31] Park S, Im J, Park S, Yoo C, Han H, Rhee J (2018). Classification and mapping of paddy rice by combining Landsat and SAR time series data.Remote Sens (Basel), 10(3): 447

[32] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011). Scikit-learn: machine learning in python.J Mach Learn Res, 12: 2825

[33] Pérez A, Larranaga P, Inza I (2009). Bayesian classifiers based on kernel density estimation: flexible classifiers.Int J Approx Reason, 50(2): 341–362

[34] Raney R K (2006). Dual-polarized SAR and Stokes parameters.IEEE Geosci Remote Sens Lett, 3(3): 317–319

[35] Raney R K (2007). Hybrid-polarity SAR architecture.IEEE Trans Geosci Remote Sens, 45(11): 3397–3404

[36] Raney R K, Cahill J T S, Patterson G W, Bussey D B J (2012a). The m-chi decomposition of hybrid dual-polarimetric radar data with application to lunar craters.J Geophys Res, 117(E12): E00H21

[37] RaneyR K, CahillJ T S, PattersonG W, BusseyD B J (2012b). The m-chi decomposition of hybrid dual-polarimetric radar data. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, GERMANY, 2012 Jul 22–27

[38] Ranjan A K, Parida B R (2019). Paddy acreage mapping and yield prediction using sentinel-based optical and SAR data in Sahibganj District, Jharkhand (India).Spatial Inform Res, 27(4): 399–410

[39] Réfrégier P, Morio J (2006). Shannon entropy of partially polarized and partially coherent light with Gaussian fluctuations.J Opt Soc Am A Opt Image Sci Vis, 23(12): 3036–3044

[40] SchlechtriemenJ, WedelA, HillenbrandJ, BreuelG, Kuhnert K-D (2014). A lane change detection approach using feature ranking with maximized predictive power. In: IEEE Intelligent Vehicles Symposium (IV), Dearborn, MI, 2014 Jun 08–11

[41] Shao Y, Fan X T, Liu H, Xiao J H, Ross S, Brisco B, Brown R, Staples G (2001). Rice monitoring and production estimation using multitemporal RADARSAT.Remote Sens Environ, 76(3): 310–325

[42] Suykens J A K, Vandewalle J (1999). Least squares support vector machine classifiers.Neural Process Lett, 9(3): 293–300

[43] Thorp K R, Drajat D (2021). Deep machine learning with Sentinel satellite data to map paddy rice production stages across West Java, Indonesia.Remote Sens Environ, 265: 112679

[44] Touzi R, Boerner W M, Lee J S, Lueneburg E (2004). A review of polarimetry in the context of synthetic aperture radar: concepts and information extraction.Can J Rem Sens, 30(3): 380–407

[45] Truong-Loi M L, Freeman A, Dubois-Fernandez P C, Pottier E (2009). Estimation of soil moisture and faraday rotation from bare surfaces using compact polarimetry.IEEE Trans Geosci Remote Sens, 47(11): 3608–3615

[46] Uppala D, Kothapalli R V, Poloju S, Mullapudi S S V R, Dadhwal V K (2015). Rice crop discrimination using single date RISAT1 hybrid (RH, RV) polarimetric data.Photogramm Eng Remote Sensing, 81(7): 557–563

[47] Uppala D, Somepalli V, Venkata R K, Rama S M V (2021). Identification of optimal single date for rice crop discrimination and relationships between backscatter and biophysical parameters using RISAT-1 hybrid polarimetric SAR data.Geocarto Int, 36(17): 2010–2022

[48] van Beijma S, Comber A, Lamb A (2014). Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data.Remote Sens Environ, 149: 118–129

[49] Wang J, Li K, Shao Y, Zhang F, Wang Z, Guo X, Qin Y, Liu X (2020). Analysis of combining SAR and optical optimal parameters to classify typhoon-invasion lodged rice: a case study using the random forest method.Sensors (Basel), 20(24): 7346

[50] Wang S, Gao R, Wang L (2016). Bayesian network classifiers based on Gaussian kernel density.Expert Syst Appl, 51: 207–217

[51] Waske B, Braun M (2009). Classifier ensembles for land cover mapping using multitemporal SAR imagery.ISPRS J Photogramm Remote Sens, 64(5): 450–457

[52] Yang Z, Li K, Liu L, Shao Y, Brisco B, Li W (2014). Rice growth monitoring using simulated compact polarimetric C band SAR.Radio Sci, 49(12): 1300–1315

[53] YinJ, YangJ (2014). Ship detection by using the m-chi and m-delta decompositions. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, Canada, 2014 Jul 13–18

[54] Yu Y, Li M, Fu Y (2018). Forest type identification by random forest classification combined with SPOT and multitemporal SAR data.J For Res, 29(5): 1407–1414

[55] Zhang M Q (1997). Identification of protein coding regions in the human genome by quadratic discriminant analysis.Proc Natl Acad Sci USA, 94(2): 565–568

[56] Zhang X, Xu J, Chen Y, Xu K, Wang D (2021). Coastal wetland classification with GF-3 polarimetric SAR imagery by using object-oriented random forest algorithm.Sensors (Basel), 21(10): 3395

Acknowledgments

This work was funded in part by the National Natural Science Foundation of China (Grant No. 41871272).

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2023 Higher Education Press

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