The impacts of vegetation indices from UAV-based RGB imagery on land cover classification using ensemble learning

Yıl 2021, Cilt: 3 Sayı: 2, 41 - 47, 30.12.2021

Muhammed Yusuf Öztürk İsmail Çölkesen

https://doi.org/10.53093/mephoj.943347

Cited By: 4

Öz

The production of land use and land cover (LULC) maps using UAV images obtained by RGB cameras offering very high spatial resolution has recently increased. Vegetation indices (VIs) have been widely used as an important ancillary data to increase the limited spectral information of the UAV image in pixel-based classification. The main goal of this study is to analyze the effect of frequently used RGB-based VIs including green leaf index (GLI), red- green-blue vegetation index (RGBVI) and triangular greenness index (TGI) on the classification of UAV images. For this purpose, five different dataset combinations comprising of RGB bands and VIs were formed. In order to evaluate their effects on thematic map accuracy, four ensemble learning methods, namely RF, XGBoost, LightGBM and CatBoost were utilized in classification process. Classification results showed that the use of RGB UAV image with VIs increased the overall accuracy (OA) values in all cases. On the other hand, the highest OA values were calculated with the use of Dataset-5 (i.e. RGB bands and all VIs considered). Additionally, the classification result of Dataset-4 (i.e. RGB bands and TGI) showed superior performance compared to Dataset-2 (i.e. RGB bands and GLI) and Dataset-3 (i.e. RGB bands and RGBVI). All in all, the TGI was found to be useful for improving classification accuracy of UAV image having limited spectral information compared to GLI and RGBVI. The improvement in overall accuracy reached to 2% with the use of RGB bands and TGI index. Furthermore, within the ensemble algorithms, CatBoost produced the highest overall accuracy (92.24%) with the dataset consist of RBG bands and all VIs considered. 

Anahtar Kelimeler

Ensemble learning, UAV, LULC, LightGBM, XGBoost

Teşekkür

This article is presented in "2nd Intercontinental Geoinformation Days" 2021 and selected for publication in Mersin Photogrammetry Journal.

Kaynakça

• Abdi A M (2020). Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing, 57(1), 1–20. https://doi.org/10.1080/15481603.2019.1650447
• Al Daoud E (2019). Comparison between XGBoost, LightGBM and CatBoost Using a Home Credit Dataset. International Journal of Computer and Information Engineering, 13(1), 6–10.
• Breiman L (2001). Random Forests. In Machine Learning (pp. 5–32). Chapman and Hall/CRC. https://doi.org/10.1023/A:1010933404324
• Chen Tianqi & Guestrin C (2016). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu, 785–794. https://doi.org/10.1145/2939672.2939785
• Chen Tingting, Xu J, Ying H, Chen X, Feng R, Fang X, Gao H & Wu J (2019). Prediction of Extubation Failure for Intensive Care Unit Patients Using Light Gradient Boosting Machine. IEEE Access, 7, 150960–150968. https://doi.org/10.1109/ACCESS.2019.2946980
• Colkesen I & Ertekin O H (2020). Performance Analysis of Advanced Decision Forest Algorithms in Hyperspectral Image Classification. Photogrammetric Engineering & Remote Sensing, 86(9), 571–580. https://doi.org/10.14358/PERS.86.9.571
• Colkesen I & Kavzoglu T (2017). The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery. Geocarto International, 32(1), 71–86. https://doi.org/10.1080/10106049.2015.1128486
• 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. Ecological Indicators, 73, 105–117. https://doi.org/10.1016/j.ecolind.2016.09.029
• Fuentes-Peailillo F, Ortega-Farias S, Rivera M, Bardeen M & Moreno M (2018). Comparison of vegetation indices acquired from RGB and Multispectral sensors placed on UAV. 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control (ICA-ACCA), 1–6. https://doi.org/10.1109/ICA-ACCA.2018.8609861
• Goldblatt R, Stuhlmacher M F, Tellman B, Clinton N, Hanson G, Georgescu M, Wang C, Serrano-Candela F, Khandelwal A K, Cheng W H & Balling R C (2018). Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sensing of Environment, 205(December 2017), 253–275. https://doi.org/10.1016/j.rse.2017.11.026
• Ha N T, Manley-Harris M, Pham T D & Hawes I (2021). Detecting multi-decadal changes in seagrass cover in tauranga harbour, new zealand, using landsat imagery and boosting ensemble classification techniques. ISPRS International Journal of Geo-Information, 10(6). https://doi.org/10.3390/ijgi10060371
• Hamedianfar A, Gibril M B A, Hosseinpoor M & Pellikka P K E (2020). Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images. Geocarto International, 0(0), 1–19. https://doi.org/10.1080/10106049.2020.1737974
• Hindersah R, Handyman Z, Indriani F N, Suryatmana P & Nurlaeny N (2018). JOURNAL OF DEGRADED AND MINING LANDS MANAGEMENT Azotobacter population, soil nitrogen and groundnut growth in mercury-contaminated tailing inoculated with Azotobacter. J. Degrade. Min. Land Manage, 5(53), 2502–2458. https://doi.org/10.15243/jdmlm
• Hunt E R, Daughtry C S T, Eitel J U H & Long D S (2011). Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index. Agronomy Journal, 103(4), 1090–1099. https://doi.org/10.2134/agronj2010.0395
• Huth J, Kuenzer C, Wehrmann T, Gebhardt S, Tuan V Q & Dech S (2012). Land cover and land use classification with TWOPAC: Towards automated processing for pixel- and object-based image classification. Remote Sensing, 4(9), 2530–2553. https://doi.org/10.3390/rs4092530
• Jang G, Kim J, Yu J, Kim H, Kim Y, Kim D, Kim K, Lee C W & Chung Y S (2020). Remote sensing Review : Cost-E ff ective Unmanned Aerial Vehicle ( UAV ) Platform for Field Plant Breeding Application. Remote Sensing, 12(6), 998.
• Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q & Liu T Y (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 2017-Decem(Nips), 3147–3155.
• Kerkech M, Hafiane A & Canals R (2018). Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Computers and Electronics in Agriculture, 155(October), 237–243. https://doi.org/10.1016/j.compag.2018.10.006
• Lu J, Cheng D, Geng C, Zhang Z, Xiang Y & Hu T (2021). Combining plant height, canopy coverage and vegetation index from UAV-based RGB images to estimate leaf nitrogen concentration of summer maize. Biosystems Engineering, 202, 42–54. https://doi.org/10.1016/j.biosystemseng.2020.11.010
• Ma X, Sha J, Wang D, Yu Y, Yang Q & Niu X (2018). Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electronic Commerce Research and Applications, 31, 24–39. https://doi.org/10.1016/j.elerap.2018.08.002
• Nitze I, Barrett B & Cawkwell F (2015). Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series. International Journal of Applied Earth Observation and Geoinformation, 34(1), 136–146. https://doi.org/10.1016/j.jag.2014.08.001
• Pham T D, Yokoya N, Nguyen T T T, Le N N, Ha N T, Xia J, Takeuchi W & Pham T D (2020). Improvement of Mangrove Soil Carbon Stocks Estimation in North Vietnam Using Sentinel-2 Data and Machine Learning Approach. GIScience & Remote Sensing, 1–20. https://doi.org/10.1080/15481603.2020.1857623
• Pham T D, Yokoya N, Nguyen T T T, Le N N, Ha N T, Xia J, Takeuchi W & Pham T D (2021). Improvement of Mangrove Soil Carbon Stocks Estimation in North Vietnam Using Sentinel-2 Data and Machine Learning Approach. GIScience & Remote Sensing, 58(1), 68–87. https://doi.org/10.1080/15481603.2020.1857623
• Sagi O & Rokach L (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), 1–18. https://doi.org/10.1002/widm.1249
• Sahin E K (2020). Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping. Geocarto International, 0(0), 1–25. https://doi.org/10.1080/10106049.2020.1831623
• Samat A, Li E, Du P, Liu S, Miao Z & Zhang W (2020). CatBoost for RS Image Classification With Pseudo Label Support From Neighbor Patches-Based Clustering. IEEE Geoscience and Remote Sensing Letters, 1–5. https://doi.org/10.1109/LGRS.2020.3038771
• Shi J, Shao T, Liu X, Zhang X, Zhang Z & Lei Y (2021). Evolutionary Multitask Ensemble Learning Model for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 936–950. https://doi.org/10.1109/JSTARS.2020.3037353
• Starý K, Jelínek Z, Kumhálova J, Chyba J & Balážová K (2020). Comparing RGB-based vegetation indices from uav imageries to estimate hops canopy area. Agronomy Research, 18(4), 2592–2601. https://doi.org/10.15159/AR.20.169
• Sumesh K C, Ninsawat S & Som-ard J (2021). Integration of RGB-based vegetation index, crop surface model and object-based image analysis approach for sugarcane yield estimation using unmanned aerial vehicle. Computers and Electronics in Agriculture, 180(July 2020), 105903. https://doi.org/10.1016/j.compag.2020.105903
• Sun X, Liu M & Sima Z (2020). A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, 32(November 2018), 101084. https://doi.org/10.1016/j.frl.2018.12.032
• Tehrany M S, Pradhan B & Jebuv M N (2014). A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto International, 29(4), 351–369. https://doi.org/10.1080/10106049.2013.768300
• Tonbul H, Colkesen I & Kavzoglu T (2020). Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery. Journal of Geodetic Science, 10(1), 14–22. https://doi.org/10.1515/jogs-2020-0003
• Ustuner M, Abdikan S, Bilgin G & Balik Sanli F (2020). Hafif Gradyan Artırma Makineleri ile Tarımsal Ürünlerin Sınıflandırılması. Türk Uzaktan Algılama ve CBS Dergisi, 1(2), 97–105.
• Wan L, Li Y, Cen H, Zhu J, Yin W, Wu W, Zhu H, Sun D, Zhou W & He Y (2018). Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape. Remote Sensing, 10(9), 1484. https://doi.org/10.3390/rs10091484
• Xue J & Su B (2017). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors, 2017, 1–17. https://doi.org/10.1155/2017/1353691
• Yao H & Qin R (2019). Unmanned Aerial Vehicle for Remote Sensing Applications — A Review. 11(12), 1443.
• Zhiwei Y, Juan Y, Xu Z & Zhengbing H (2016). Remote Sensing Textual Image Classification based on Ensemble Learning. International Journal of Image, Graphics and Signal Processing, 8(12), 21–29. https://doi.org/10.5815/ijigsp.2016.12.03
• Zou X, Liang A, Wu B, Su J, Zheng R & Li J (2019). UAV-based high-throughput approach for fast growing Cunninghamia lanceolata (Lamb.) cultivar screening by machine learning. Forests, 10(9). https://doi.org/10.3390/f10090815