Abstract
Recent advances in airborne and space-based remote sensing technologies and a rapid increase in the use of machine learning (ML) techniques in digital image processing applications have led to a renewed interest in the classification of satellite imagery. Decision-tree based ensemble learning (EL) algorithms, one of the popular ML techniques, have received considerable attention from researchers due to their simplicity, computational effectiveness and interpretability compared to black-box algorithms. The main goal of this study is to evaluate the supervised classification performance of the advanced decision-tree based EL algorithms, including rotation forest (RotFor), random ferns (RFerns), canonical correlation forest (CCF), extreme gradient boosting (XGBoost), light gradient boosting machines (LightGBM) and categorical boosting (CatBoost) using satellite imagery with different spatial and spectral resolutions. Two well-known EL algorithms, namely, adaptive boosting (AdaBoost) and random forest (RF), were also considered to compare their classification performances. In order to achieve the desired goal, three satellite imagery, namely WorldView-2, Sentinel-2 and hyperspectral ROSIS, were utilized as the fundamental datasets. Results of the study showed that the highest overall accuracy values (i.e. 92.65% for WorldView-2, 92.80% for Sentinel-2 and 95.70% for Pavia datasets) were estimated using CCF, LightGBM and RotFor algorithms, respectively. According to McNemar’s test result, the difference between the predictions of RotFor and CCF algorithms on test samples of the hyperspectral image was found to be statistically insignificant. On the other hand, the lowest classification accuracy values were obtained by the RFerns algorithm in all cases. In addition, while the CCF showed superior classification performance for high spatial resolution WorldView-2 and Pavia images, the highest classification performance was acquired with the LightGBM algorithm for medium spatial resolution Sentinel-2 image. As a result, the performances of advanced EL algorithms were found to be more robust than the well-known RF and AdaBoost ensemble classifiers.
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References
Abdollahi A, Pradhan B, Shukla N, Chakraborty S, Alamri A (2020a) Deep learning approaches applied to remote sensing datasets for road extraction: a state-of-the-art review. Remote Sens 12(9):1444. https://doi.org/10.3390/rs12091444
Abdollahi A, Pradhan B, Gite S, Alamri A (2020b) Building footprint extraction from high resolution aerial images using generative adversarial network (GAN) architecture. IEEE Access 8:209517–209527. https://doi.org/10.1109/access.2020.3038225
Abdollahi A, Pradhan B (2021) Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images. Expert Syst Appl 176:114908. https://doi.org/10.1016/j.eswa.2021.114908
Akar O (2017) Mapping land use with using rotation forest algorithm from UAV images. Eur J Remote Sens 50(1):269–279. https://doi.org/10.1080/22797254.2017.1319252
Akar O, Gungor O (2012) Classification of multispectral images using random forest algorithm. J Geod Geoinf 1(2):105–112. https://doi.org/10.9733/jgg.241212.1
Al Daoud E (2019) Comparison between xgboost, lightgbm and catboost using a home credit dataset. Int J Comput Inf Eng 13(1):6–10. https://doi.org/10.5281/zenodo.3607805
Ali J, Khan R, Ahmad N, Maqsood I (2012) Random forests and decision trees. IJCSI Int J Comput Sci 9(5):272–278
Al-Najjar HAH, Kalantar B, Pradhan B, Saeidi V, Halin AA, Ueda N, Mansor S (2019) Land cover classification from fused DSM and UAV images using convolutional neural networks. Remote Sens 11(12):1461. https://doi.org/10.3390/rs11121461
Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8:53. https://doi.org/10.1186/s40537-021-00444-8
Belgiu M, Drăguţ L (2016) Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Bosch A., Zisserman A, Muñoz X (2007) Image classification using random forests and ferns. Paper presented at the IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, 14–21 October. https://doi.org/10.1007/s001090000086
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Chan JCW, Paelinckx D (2008) Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens Environ 112(6):2999–3011. https://doi.org/10.1016/j.rse.2008.02.011
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. Paper presented at the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 785–794. https://doi.org/10.1145/2939672.2939785
Chen W, Li X, He H, Wang L (2017) A review of fine-scale land use and land cover classification in open-pit mining areas by remote sensing techniques. Remote Sens 10(1):15. https://doi.org/10.1145/2939672.2939785
Colkesen I, Ertekin OH (2020) Performance analysis of advanced decision forest algorithms in hyperspectral image classification. Photogramm Eng Remote Sens 86(9):571–580. https://doi.org/10.14358/pers.86.9.571
Colkesen I, Kavzoglu T (2016) Performance evaluation of rotation forest for SVM-based recursive feature elimination using hyperspectral imagery. Paper presented at the 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp 1–5. https://doi.org/10.1109/whispers.2016.8071792
Colkesen I, Kavzoglu T (2017a) Ensemble-based canonical correlation forest (CCF) for land use and land cover classification using Sentinel-2 and Landsat OLI imagery. Remote Sens Lett 8(11):1082–1091. https://doi.org/10.1080/2150704X.2017.1354262
Colkesen I, Kavzoglu T (2017b) The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery. Geocarto Int 32(1):71–86. https://doi.org/10.1080/10106049.2015.1128486
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46. https://doi.org/10.1016/0034-4257(91)90048-B
Congalton RG, Green K (2019) Assessing the accuracy of remotely sensed data: Principles and practices, Third Edition. CRC Press. https://doi.org/10.1201/9780429052729
Conţiu Ş, Groza A (2016) Improving remote sensing crop classification by argumentation-based conflict resolution in ensemble learning. Expert Syst Appl 64:269–286. https://doi.org/10.1016/j.eswa.2016.07.037
Costa H, Foody GM, Boyd DS (2018) Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sens Environ 205:338–351. https://doi.org/10.1016/j.rse.2017.11.024
Díez-Pastor JF, Rodríguez JJ, García-Osorio CI, Kuncheva LI (2015) Diversity techniques improve the performance of the best imbalance learning ensembles. Inf Sci 325:98–117. https://doi.org/10.1016/j.ins.2015.07.025
Duro DC, Franklin SE, Dubé MG (2012) A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens Environ 118:259–272. https://doi.org/10.1016/j.rse.2011.11.020
Dou P, Shen H, Li Z, Guan X, Huang W (2021) Remote sensing image classification using deep–shallow learning. IEEE J Sel Top Appl Earth Obs Remote Sens 14:3070–3083. https://doi.org/10.1109/JSTARS.2021.3062635
Feng W, Quan Y, Dauphin G, Li Q, Gao L, Huang W, Xia J, Zhu W, Xing M (2021) Semi-supervised rotation forest based on ensemble margin theory for the classification of hyperspectral image with limited training data. Inf Sci 575:611–638. https://doi.org/10.1016/j.ins.2021.06.059
Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201. https://doi.org/10.1016/S0034-4257(01)00295-4
Foody GM (2004) Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogramm Eng Remote Sens 70(5):627–633. https://doi.org/10.14358/PERS.70.5.627
Freund Y, Scapire R (1996) Experiments with a new boosting algorithm. Paper presented at the Proceedings of the 13th International Conference on Machine Learning, Francisco, Italy, 3–6 July
Fu B, Wang Y, Campbell A, Li Y, Zhang B, Yin S, Xing Z, Jin X (2017) Comparison of object-based and pixel-based random forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data. Ecol Indic 73:105–117. https://doi.org/10.1016/j.ecolind.2016.09.029
Ganaie MA, Hu M, Tanveer M, Suganthan PN (2022) Ensemble deep learning: A review. Available online: https://arxiv.org/abs/2104.02395v2 (accessed on 12 April 2022).
Gašparović M, Dobrinić D (2020) Comparative assessment of machine learning methods for urban vegetation mapping using multitemporal Sentinel-1 imagery. Remote Sens 12:1952. https://doi.org/10.3390/rs12121952
Georganos S, Grippa T, Vanhuysse S, Lennert M, Shimoni M, Wolff E (2018) Very high resolution object-based land use–land cover urban classification using extreme gradient boosting. IEEE Geosci Remote Sens Lett 15:607–611. https://doi.org/10.1109/LGRS.2018.2803259
Goldblatt R, Stuhlmacher MF, Tellman B, Clinton N, Hanson G, Georgescu M, Wang C, Serrano-Candela F, Khandelwal AK, Cheng WH, Balling RC (2018) Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sens Environ 205:253–275. https://doi.org/10.1016/j.rse.2017.11.026
Ha NT, Manley-Harris M, Pham TD, Hawes I (2020) A comparative assessment of ensemble-based machine learning and maximum likelihood methods for mapping seagrass using Sentinel-2 imagery in Tauranga Harbor New Zealand. Remote Sens 12(3):355. https://doi.org/10.3390/rs12030355
Habib N, Rahman MM (2021) Diagnosis of corona diseases from associated genes and x-ray images using machine learning algorithms and deep CNN. Informatics Med Unlocked 24:100621. https://doi.org/10.1016/j.imu.2021.100621
Holloway J, Mengersen K (2018) Statistical machine learning methods and remote sensing for sustainable development goals: a review. Remote Sens 10(9):1365. https://doi.org/10.3390/rs10091365
Huth J, Kuenzer C, Wehrmann T, Gebhardt S, Tuan VQ, Dech S (2012) Land cover and land use classification with twopac: towards automated processing for pixel- and object-based image classification. Remote Sens 4(9):2530–2553. https://doi.org/10.3390/rs4092530
Immitzer M, Atzberger C, Koukal T (2012) Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sens 4(9):2661–2693. https://doi.org/10.3390/rs4092661
Islam MD, Islam KS, Mia M (2020) An xgboost based approach for urban land use and land cover change modelling. Authorea Prepr, pp 1–13. https://doi.org/10.22541/au.159646139.97656606
Joshi GP, Alenezi F, Thirumoorthy G, Dutta AK, You J (2021) Ensemble of deep learning-based multimodal remote sensing image classification model on unmanned aerial vehicle networks. Math 9(22):2984. https://doi.org/10.3390/math9222984
Jozdani SE, Johnson BA, Chen D (2019) Comparing deep neural networks, ensemble classifiers, and support vector machine algorithms for object-based urban land use/land cover classification. Remote Sens 11(14):1713. https://doi.org/10.3390/rs11141713
Kavzoglu T, Colkesen I (2013) An assessment of the effectiveness of a rotation forest ensemble for land-use and land-cover mapping. Int J Remote Sens 34(12):4224–4241. https://doi.org/10.1080/01431161.2013.774099
Kavzoglu T, Colkesen I, Yomralioglu T (2015) Object-based classification with rotation forest ensemble learning algorithm using very-high-resolution WorldView-2 image. Remote Sens Lett 6:834–843. https://doi.org/10.1080/2150704X.2015.1084550
Kavzoglu T, Tonbul H, Yildiz Erdemir M, Colkesen I (2018) Dimensionality reduction and classification of hyperspectral images using object-based image analysis. J Indian Soc Remote Sens 46(8):1297–1306. https://doi.org/10.1007/s12524-018-0803-1
Kim S, Jeong M, Lee D, Ko BC (2019) Deep coupling of random ferns. Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 15–20 June.
Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51:181–207. https://doi.org/10.1023/A:1022859003006
Kursa MB (2014) rFerns: an implementation of the random ferns method for general-purpose machine learning. J Stat Softw 61(10):1–13. https://doi.org/10.18637/jss.v061.i10
Li M, Zang S, Zhang B, Li S, Wu C (2014) A review of remote sensing image classification techniques: the role of spatio-contextual information. Eur J Remote Sens 47(1):389–411. https://doi.org/10.5721/EuJRS20144723
Liang W, Luo S, Zhao G, Wu H (2020) Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms. Math 8(5):765. https://doi.org/10.3390/math8050765
Łoś H, Mendes GS, Cordeiro D, et al (2021) Evaluation of Xgboost and Lgbm performance in tree species classification with Sentinel-2 data. Paper presented at the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July, pp 5803–5806. https://doi.org/10.1109/igarss47720.2021.9553031
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823–870. https://doi.org/10.1080/01431160600746456
Ma L, Liu Y, Zhang X, Ye Y, Yin G, Johnson BA (2019) Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J Photogramm Remote Sens 152:166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015
Maxwell AE, Warner TA, Fang F (2018) Implementation of machine-learning classification in remote sensing: An applied review. Int J Remote Sens 39(9):2784–2817. https://doi.org/10.1080/01431161.2018.1433343
McCarty DA, Kim HW, Lee HK (2020) Evaluation of light gradient boosted machine learning technique in large scale land use and land cover classification. Environments 7(10):84. https://doi.org/10.3390/environments7100084
Mohajane M, Essahlaoui A, Oudija F, Hafyani MEL, Hmaidi AEL, Ouali AEL, Randazzo G, Teodoro AC (2018) Land use/land cover (LULC) using Landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments 5:131. https://doi.org/10.3390/environments5120131
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001
Nalepa J, Myller M, Cwiek M, Zak L, Lakota T, Tulczyjew L, Kawulok M (2021) Towards on-board hyperspectral satellite image segmentation: understanding robustness of deep learning through simulating acquisition conditions. Remote Sens 13(8):1532. https://doi.org/10.3390/rs13081532
Nikolakopoulos K, Oikonomidis D (2015) Quality assessment of ten fusion techniques applied on Worldview-2. Eur J Remote Sens 48:141–167. https://doi.org/10.5721/eujrs20154809
Oberweger M (2014) Embeddings for random ferns classification. M. S. diss., Institute for Computer Graphics and Vision Graz University of Technology, Austria
Ok AO, Akar O, Gungor O (2012) Evaluation of random forest method for agricultural crop classification. Eur J Remote Sens 45(1):421–432. https://doi.org/10.5721/EuJRS20124535
Osco LP, Marcato Junior J, Marques Ramos AP et al (2021) A review on deep learning in UAV remote sensing. Int J Appl Earth Obs Geoinf 102:102456. https://doi.org/10.1016/j.jag.2021.102456
Ozturk MY, Colkesen I (2020) Mapping of poplar tree growing fields with machine learning algorithms using multi-temporal Sentinel-2A imagery. Paper presented at the 41th Asian Conference on Remote Sensing (ACRS), Deqing, China, 9–11 November
Ozuysal M, Fua P, Lepetit V (2007) Fast keypoint recognition in ten lines of code. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1–8. https://doi.org/10.1109/cvpr.2007.383123
Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26(1):217–222. https://doi.org/10.1080/01431160412331269698
Pal M (2007) Ensemble learning with decision tree for remote sensing classification. Proc World Acad Sci Eng Technol 26:735–737
Pham TD, Yokoya N, Nguyen TTT, Le NN, Ha NT, Xia J, Takeuchi W, Pham TD (2020) Improvement of mangrove soil carbon stocks estimation in north Vietnam using Sentinel-2 data and machine learning approach. Giscience Remote Sens 58(1):1–20. https://doi.org/10.1080/15481603.2020.1857623
Pourshamsi M, Xia J, Yokoya N, Garcia M, Lavalle M, Pottier E, Balzter H (2021) Tropical forest canopy height estimation from combined polarimetric SAR and lidar using machine-learning. ISPRS J Photogramm Remote Sens 172:79–94. https://doi.org/10.1016/J.ISPRSJPRS.2020.11.008
Pradhan B, Al-Najjar HAH, Sameen MI, Tsang I, Alamri AM (2020) Unseen land cover classification from high-resolution orthophotos using integration of zero-shot learning and convolutional neural networks. Remote Sens 12(10):1676. https://doi.org/10.3390/rs12101676
Ramroach S, John M, Joshi A (2019) The efficacy of various machine learning models for multi-class classification of rna-seq expression data. Paper presented at the Intelligent Computing - Proceedings of the Computing Conference 997: 918–928 https://doi.org/10.1007/978-3-030-22871-2_65
Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619–1630. https://doi.org/10.1109/TPAMI.2006.211
Rumora L, Miler M, Medak D (2020) Impact of various atmospheric corrections on Sentinel-2 land cover classification accuracy using machine learning classifiers. ISPRS Int J Geo-Information 9(4):277. https://doi.org/10.3390/ijgi9040277
Sagi O, Rokach L (2018) Ensemble learning: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 8(4):1–18. https://doi.org/10.1002/widm.1249
Sahin EK (2020) Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping. Geocarto Int 1–25. https://doi.org/10.1080/10106049.2020.1831623
Sahin EK, Colkesen I (2019) Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping. Geocarto International 1–23 https://doi.org/10.1080/10106049.2019.1641560
Saini R, Ghosh SK (2021) Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date Sentinel-2A imagery. Geocarto Int 36:2141–2159. https://doi.org/10.1080/10106049.2019.1700556
Samat A, Li E, Wang W, Liu S, Lin C, Abuduwaili J (2020) Meta-XGBoost for hyperspectral image classification using extended mser-guided morphological profiles. Remote Sens 12(12):1973. https://doi.org/10.3390/rs12121973
Sekertekin A, Marangoz AM, Akcin H (2017) Pixel-based classification analysis of land use land cover using Sentinel-2 and Landsat-8 data. ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci XLII-4/W6:91–93. 10.5194/isprs-archives-XLII-4-W6-91-2017
Selva M, Aiazzi B, Butera F, Chiarantini L, Baronti S (2015) Hyper-sharpening: a first approach on sim-ga data. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):3008–3024. https://doi.org/10.1109/JSTARS.2015.2440092
Sencaki DB, Prayogi H, Arfah S, Arif Pianto T (2020) Machine learning approach for peatland delineation using multi-sensor remote sensing data in Ogan Komering Ilir Regency. IOP Conf Ser: Earth Environ Sci 500:012005. https://doi.org/10.1088/1755-1315/500/1/012005
Shi J, Shao T, Liu X, Zhang X, Zhang Z, Lei Y (2021) Evolutionary multitask ensemble learning model for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 14:936–950. https://doi.org/10.1109/JSTARS.2020.3037353
Sonobe R, Yamaya Y, Tani H, Wang X, Kobayashi N, Mochizuki K (2018) Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. J Appl Remote Sens 12(2):026019. https://doi.org/10.1117/1.JRS.12.026019
Sun Y, Todorovic S, Li J (2006) Reducing the overfitting of Adaboost by controlling its data distribution skewness. Int J Pattern Recognit Artif Intell 20:1093–1116. https://doi.org/10.1142/S0218001406005137
Talukdar S, Singha P, Mahato S, Shahfahad P, Pal S, Liou YA, Rahman A (2020) Land-use land-cover classification by machine learning classifiers for satellite observations-a review. Remote Sens 12(7):11–35. https://doi.org/10.3390/rs12071135
Tehrany MS, Pradhan B, Jebuv MN (2014) A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using Spot 5 imagery. Geocarto Int 29(4):351–369. https://doi.org/10.1080/10106049.2013.768300
Tian Y, Zhang Q, Huang H, Huang Y, Tao J, Zhou G, Zhang Y, Yang Y, Lin J (2022) Aboveground biomass of typical invasive mangroves and its distribution patterns using UAV-LiDAR data in a subtropical estuary: Maoling River estuary, Guangxi. China Ecol Indic 136:108694. https://doi.org/10.1016/j.ecolind.2022.108694
Tonbul H, Colkesen I, Kavzoglu T (2020) Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery. J Geod Sci 10(1):14–22. https://doi.org/10.1515/jogs-2020-0003
Ustuner M, Abdikan S, Bilgin G, Balik Sanli F (2020) crop classification using light gradient boosting machines. Turkish J Remote Sens GIS 1(2):97–105 ([In Turkish])
Ustuner M, Balik Sanli F (2019) Polarimetric target decompositions and light gradient boosting machine for crop classification: a comparative evaluation. ISPRS Int J Geo-Information 8(2):97. https://doi.org/10.3390/ijgi8020097
Ustuner M, Balik Sanli F, Abdikan S, Bilgin G, Goksel C (2019) A booster analysis of extreme gradient boosting for crop classification using PolSAR imagery. Paper presented at the 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Istanbul, Turkey, 16–19 July. https://doi.org/10.1109/Agro-Geoinformatics.2019.8820698
Wang X, Gao X, Zhang Y, Fei X, Chen X, Wang J, Zhang Y, Lu X, Zhao H (2019) Land-cover classification of coastal wetlands using the RF algorithm for Worldview-2 and Landsat 8 images. Remote Sens 11(16):1–22. https://doi.org/10.3390/rs11161927
Wu S, Nagahashi H (2014) A new method for solving overfitting problem of gentle AdaBoost. In: Wang Y, Jiang X, Yang M, et al. (eds) Fifth International Conference on Graphic and Image Processing (ICGIP 2013). SPIE
Xia J, Du P, He X, Chanussot J (2014) Hyperspectral remote sensing image classification based on rotation forest. IEEE Geoscience and Remote Sens Lett 11(1):239–243. https://doi.org/10.1109/LGRS.2013.2254108
Xia J, Ghamisi P, Yokoya N, Iwasaki A (2018) Random forest ensembles and extended multiextinction profiles for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56:202–216. https://doi.org/10.1109/TGRS.2017.2744662
Xia J, Yokoya N, Iwasaki A (2017) Hyperspectral image classification with canonical correlation forests. IEEE Geosci Remote Sens Lett 55(1):421–431. https://doi.org/10.1109/TGRS.2016.2607755
Zhang T, Su J, Xu Z, Luo Y, Li J (2021) Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier. Appl Sci 11(2):543. https://doi.org/10.3390/app11020543
Zhiwei Y, Juan Y, Xu Z, Zhengbing H (2016) Remote sensing textual image classification based on ensemble learning Int J Image Graph. Signal Process 8(12):21–29. https://doi.org/10.5815/ijigsp.2016.12.03
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Colkesen, I., Ozturk, M.Y. A comparative evaluation of state-of-the-art ensemble learning algorithms for land cover classification using WorldView-2, Sentinel-2 and ROSIS imagery. Arab J Geosci 15, 942 (2022). https://doi.org/10.1007/s12517-022-10243-x
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DOI: https://doi.org/10.1007/s12517-022-10243-x