دراسة مقارنة التعلم الآلي في التصنيف الفعال للبيانات متعددة الأطياف في الزراعة

Shelestov A, Lavreniuk M, Kussul N, Novikov A, Skakun S. Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping. Front Earth Sci. 2017: 17. https://doi.org/10.3389/feart.2017.00017

Weiss M, Jacob F, Duveiller G. Remote sensing for agricultural applications: A meta-review. Remote Sens. Environ. 2020; 236: 111402. https://doi.org/10.1016/j.rse.2019.111402

Basso B, Liu L. Seasonal crop yield forecast: Methods, applications, and accuracies. Adv Agron. 2019 Jan 1; 154: 201-55. https://doi.org/10.1016/bs.agron.2018.11.002

Gallego FJ, Kussul N, Skakun S, Kravchenko O, Shelestov A, Kussul O. Efficiency assessment of using satellite data for crop area estimation in Ukraine. Int J Appl Earth Obs. Geoinf. 2014; 29: 22-30. https://doi.org/10.1016/j.jag.2013.12.013

Lobell DB, Thau D, Seifert C, Engle E, Little B. A scalable satellite-based crop yield mapper. Remote Sens Environ. 2015; 164: 324-33. https://doi.org/10.1016/j.rse.2015.04.021

Skakun S, Vermote E, Franch B, Roger JC, Kussul N, Ju J, et al. Winter wheat yield assessment from Landsat 8 and Sentinel-2 data: Incorporating surface reflectance, through phenological fitting, into regression yield models. Remote Sens. 2019; 11(15):1768. https://doi.org/10.3390/rs11151768

Mishra VN, Prasad R, Kumar P, Srivastava PK, Rai PK. Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information. J Appl Remote Sens. 2017; 11(4): 046003-. https://doi.org/10.1117/1.JRS.11.046003

Ji S, Zhang C, Xu A, Shi Y, Duan Y. 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens. 2018; 10(1): 75. https://doi.org/10.3390/rs10010075

Sicre CM, Fieuzal R, Baup F. Contribution of multispectral (optical and radar) satellite images to the classification of agricultural surfaces. Int J Appl Earth Obs Geoinf. 2020 ;84: 101972. https://doi.org/10.1016/j.jag.2019.101972

Orieschnig CA, Belaud G, Venot JP, Massuel S, Ogilvie A. Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta. Eur J Remote Sens. 2021; 54(1): 398-416. https://doi.org/10.1080/22797254.2021.1948356

Cavallaro G, Riedel M, Richerzhagen M, Benediktsson JA, Plaza A. On understanding big data impacts in remotely sensed image classification using support vector machine methods. IEEE J Sel Top Appl Earth Obs Remote Sens. 2015; 8(10): 4634-46. https://doi.org/10.1109/JSTARS.2015.2458855

Carretero J, Blas JG. Introduction to cloud computing: platforms and solutions. Cluster Comput. 2014; 17: 1225-9: https://doi.org/10.1007/s10586-014-0352-5

Kumar L, Mutanga O. Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sens. 2018; 10(10): 1509.https://doi.org/10.3390/rs10101509

Kumar L, Mutanga O. Remote sensing of above-ground biomass. Remote Sens. 2017; 9(9): 935. https://doi.org/10.3390/rs9090935

Tamiminia H, Salehi B, Mahdianpari M, Quackenbush L, Adeli S, Brisco B. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. J Photogramm. Remote Sens. 2020; 164: 152-70. https://doi.org/10.1016/j.isprsjprs.2020.04.001

Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens. 2007; 28(5): 823-70. https://doi.org/10.1080/01431160600746456

Sishodia RP, Ray RL, Singh SK. Applications of remote sensing in precision agriculture: A review. Remote Sens. 2020; 12(19): 3136.https://doi.org/10.3390/rs12193136

Amani M, Ghorbanian A, Ahmadi SA, Kakooei M, Moghimi A, Mirmazloumi SM, et al. Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE J Sel Top Appl Earth Obs Remote Sens. 2020; 13: 5326-50. https://doi.org/10.1109/JSTARS.2020.3021052

Pelletier C, Webb GI, Petitjean F. Temporal convolutional neural network for the classification of satellite image time series. Remote Sens. 2019; 11(5): 523.https://doi.org/10.3390/rs11050523

Leslie CR, Serbina LO, Miller HM. Landsat and agriculture—Case studies on the uses and benefits of Landsat imagery in agricultural monitoring and production. U S Geol Surv. 2017. https://doi.org/10.3133/ofr20171034

Debats SR, Luo D, Estes LD, Fuchs TJ, Caylor KK. A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes. Remote Sens Environ. 2016; 179: 210-21. https://doi.org/10.1016/j.rse.2016.03.010

Skakun S, Franch B, Vermote E, Roger JC, Becker-Reshef I, Justice C, et al. Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model. Remote Sens. Environ. 2017; 195: 244-58. https://doi.org/10.1016/j.rse.2017.04.026

Benami E, Jin Z, Carter MR, Ghosh A, Hijmans RJ, Hobbs A, et al. Uniting remote sensing, crop modelling and economics for agricultural risk management. Nat Rev Earth Environ. 2021 ; 2(2): 140-59. https://doi.org/10.1038/s43017-020-00122-y

Skakun S, Franch B, Vermote E, Roger JC, Justice C, Masek J, et al. Winter wheat yield assessment using Landsat 8 and Sentinel-2 data. InIGARSS 2018-2018. Int Geosci Remote Sens Symp. 2018 (pp. 5964-5967). IEEE. https://doi.org/10.1109/IGARSS.2018.8519134

Kussul N, Lemoine G, Gallego FJ, Skakun SV, Lavreniuk M, Shelestov AY. Parcel-based crop classification in Ukraine using Landsat-8 data and Sentinel-1A data. IEEE J Sel Top Appl Earth Obs Remote Sens. 2016; 9(6): 2500-8. https://doi.org/10.1109/JSTARS.2016.2560141

Lavreniuk M, Kussul N, Meretsky M, Lukin V, Abramov S, Rubel O. Impact of SAR data filtering on crop classification accuracy. IEEE First Ukraine. Conf Electr Comput Eng. UKRCON 2017 (pp. 912-917). IEEE.https://doi.org/10.1109/UKRCON.2017.8100381

Claverie M, Ju J, Masek JG, Dungan JL, Vermote EF, Roger JC, et al. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sens Environ. 2018; 219: 145-61. https://doi.org/10.1016/j.rse.2018.09.002

Shelestov A, Lavreniuk M, Kussul N, Novikov A, Skakun S. Large scale crop classification using Google earth engine platform. IEEE Int Geosci Remote Sens Symp .2017 (pp. 3696-3699).. https://doi.org/10.1109/IGARSS.2017.8127801

Kussul N, Mykola L, Shelestov A, Skakun S. Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery. Eur. J. Remote Sens. 2018;51(1):627-36. https://doi.org/10.1080/22797254.2018.1454265

Chen J, Cao X, Peng S, Ren H. Analysis and applications of GlobeLand30: a review. Int J Geo-Inf Remot sens. 2017; 6(8): 230. https://doi.org/10.3390/ijgi6080230

Yang N, Liu D, Feng Q, Xiong Q, Zhang L, Ren T, et al. Large-scale crop mapping based on machine learning and parallel computation with grids. Remote Sens. 2019; 11(12): 1500. https://doi.org/10.3390/rs11121500

Neetu, Ray SS. Exploring machine learning classification algorithms for crop classification using Sentinel 2 data. Int Arch Photogramm Remote Sens Spat Inf Sci. 2019; 42: 573-8. https://doi.org/10.5194/isprs-archives-XLII-3-W6-573-2019, 2019

Asroni A, Ku-Mahamud KR, Damarjati C, Slamat HB. Arabic speech classification method based on padding and deep learning neural network. Baghdad Sci. J. 2021;18(2 (Suppl.)):0925-. http://dx.doi.org/10.21123/bsj.2021.18.2(Suppl.).0925

Jasim OZ. Using of machines learning in extraction of urban roads from DEM of LIDAR data: Case study at Baghdad expressways, Iraq. Period. Eng. Nat. Sci. 2019; 7(4):1710-21. http://dx.doi.org/10.21533/pen.v7i4.914

Khanday AM, Khan QR, Rabani ST. Detecting textual propaganda using machine learning techniques. Baghdad Sci. J. 2021; 18(1):0199-. https://doi.org/10.21123/bsj.2021.18.1.0199

Mahmood RA, Abdi A, Hussin M. Performance evaluation of intrusion detection system using selected features and machine learning classifiers. Baghdad Sci. J. 2021;18(2 (Suppl.)):0884-. http://dx.doi.org/10.21123/bsj.2021.18.2(Suppl.).0884

Ashraf S, Alfandi O, Ahmad A, Khattak AM, Hayat B, Kim KH, et al . Bodacious-instance coverage mechanism for wireless sensor network. Wirel Commun Mob Comput. 2020; 2020:1-1. https://doi.org/10.1155/2020/8833767

Campos-Taberner M, García-Haro FJ, Martínez B, Sánchez-Ruíz S, Gilabert MA. A copernicus sentinel-1 and sentinel-2 classification framework for the 2020+ European common agricultural policy: A case study in València (Spain). Agronomy. 2019; 9(9):556. https://doi.org/10.3390/agronomy9090556

Immitzer M, Vuolo F, Atzberger C. First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens. 2016; 8(3): 166. https://doi.org/10.3390/rs8030166

Nasrallah A, Baghdadi N, Mhawej M, Faour G, Darwish T, Belhouchette H, et al. A novel approach for mapping wheat areas using high resolution Sentinel-2 images. Sensors. 2018; 18(7): 2089. https://doi.org/10.3390/s18072089

Piedelobo L, Hernández-López D, Ballesteros R, Chakhar A, Del Pozo S, González-Aguilera D, Moreno MA. Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin. Agric Syst. 2019; 171: 36-50. https://doi.org/10.1016/j.agsy.2019.01.005

Vapnik V, Vapnik V. Realism and Instrumentalism: Classical Statistics and VC Theory (1960–1980). Estimation of Dependences Based on Empirical Data. 2006:411-24. https://doi.org/10.1007/0-387-34239-7_11

Mountrakis G, Im J, Ogole C. Support vector machines in remote sensing: A review. J Photogramm. Remote Sens. 2011; 66(3): 247-59. https://doi.org/10.1016/j.isprsjprs.2010.11.001

Maulik U, Chakraborty D. Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques. IEEE Trans Geosci Remote Sens. 2017; 5(1): 33-52. https://doi.org/10.1109/MGRS.2016.2641240

Sivasankar T, Kumar D, ShankerSrivastava H, Patel P. Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data. Geocarto Int. 2020; 35(8):9 05-15. https://doi.org/10.1080/10106049.2019.1566404

Breiman L. Classification and regression trees. Routledge; 2017 Oct 19. https://doi.org/10.1201/9781315139470

Bittencourt HR, Clarke RT. Use of classification and regression trees (CART) to classify remotely-sensed digital images. Int Geosci Remote Sens Symp. 2003; 7: 3751-3753 (IEEE Cat. No. 03CH37477). https://doi.org/10.1109/IGARSS.2003.1295258

Zhang N, Wu Y, Zhang Q. Detection of sea ice in sediment laden water using MODIS in the Bohai Sea: A CART decision tree method. Int J Remote Sens. 2015; 36(6):1661-74. https://doi.org/10.1080/01431161.2015.1015658

Han J, Mao K, Xu T, Guo J, Zuo Z, Gao C. A soil moisture estimation framework based on the CART algorithm and its application in China. J Hydrol. 2018; 563: 65-75. https://doi.org/10.1016/j.jhydrol.2018.05.051

Tamy S, Belhadaoui H, Rabbah MA, Rabbah N, Rifi M. An evaluation of machine learning algorithms to detect attacks in SCADA network. In2019 7th Mediterranean CMT 2019 (pp. 1-5). IEEE. https://doi.org/10.1109/CMT.2019.8931327

Belgiu M, Drăguţ L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 2016; 114: 24-31. https://doi.org/10.1016/j.isprsjprs.2016.01.011

Guan H, Li J, Chapman M, Deng F, Ji Z, Yang X. Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests. Int J Remote Sens. 2013; 34(14): 5166-86. https://doi.org/10.1080/01431161.2013.788261

Zhang H, Li Q, Liu J, Du X, Dong T, McNairn H. Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier. Geocarto Int. 2018; 33(10): 1017-35. https://doi.org/10.1080/10106049.2017.1333533

Shukla G, Garg RD, Srivastava HS, Garg PK. Performance analysis of different predictive models for crop classification across an aridic to ustic area of Indian states. Geocarto Int. 2018; 33(3): 240-59. https://doi.org/10.1080/10106049.2016.1240721

Man CD, Nguyen TT, Bui HQ, Lasko K, Nguyen TN. Improvement of land-cover classification over frequently cloud-covered areas using Landsat 8 time-series composites and an ensemble of supervised classifiers. Int J Remote Sens. 2018; 39(4): 1243-55. https://doi.org/10.1080/01431161.2017.1399477

Bui QT, Chou TY, Hoang TV, Fang YM, Mu CY, Huang PH, et al. Gradient boosting machine and object-based CNN for land cover classification. Remote Sens. 2021; 13(14): 2709. https://doi.org/10.3390/rs13142709

Sharifi A. Yield prediction with machine learning algorithms and satellite images. J Sci Food Agric. 2021; 101(3): 891-6. https://doi.org/10.1002/jsfa.10696

Grandini M, Bagli E, Visani G. Metrics for multi-class classification: an overview. arXiv preprint arXiv: 2008.05756. 2020. https://doi.org/10.48550/arXiv. 2008.05756