3D positioning accuracy and land cover classification performance of multispectral RTK UAVs

Yıl 2023, , 119 - 128, 05.07.2023

Umut Gunes Sefercik , Taşkın Kavzoğlu , İsmail Çölkesen , Mertcan Nazar , Muhammed Yusuf Öztürk , Samed Adalı , Salih Dinç

https://doi.org/10.26833/ijeg.1074791

Cited By: 6

Öz

Lately, unmanned aerial vehicle (UAV) become a prominent technology in remote sensing studies with the advantage of high-resolution, low-cost, rapidly and periodically achievable three-dimensional (3D) data. UAV enables data capturing in different flight altitudes, imaging geometries, and viewing angles which make detailed monitoring and modelling of target objects possible. Against earlier times, UAVs have been improved by integrating real-time kinematic (RTK) positioning and multispectral (MS) imaging equipment. In this study, positioning accuracy and land cover classification potential of RTK equipped MS UAVs were evaluated by point-based geolocation accuracy analysis and pixel-based ensemble learning algorithms. In positioning accuracy evaluation, ground control points (GCPs), pre-defined by terrestrial global navigation satellite system (GNSS) measurements, were used as the reference data while Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms were applied for land cover classification. In addition, the spectral signatures of some major land classes, achieved by UAV MS bands, were compared with reference terrestrial spectro-radiometer measurements. The results demonstrated that the positioning accuracy of MS RTK UAV is ±1.1 cm in X, ±2.7 cm in Y, and ±5.7 cm in Z as root mean square error (RMSE). In RF and XGBoost pixel-based land cover classification, 13 independent land cover classes were detected with overall accuracies and kappa statistics of 93.14% and 93.37%, 0.92 and 0.93, respectively.

Anahtar Kelimeler

UAV, Multispectral, RTK, Machine Learning, Land Cover Classification

Kaynakça

• Rabah, M., Basiouny, M., Ghanem, E., & Elhadary, A. (2018). Using RTK and VRS in direct geo-referencing of the UAV imagery. NRIAG Journal of Astronomy and Geophysics. 7(2), 1-7.
• Ekaso, D., Nex, F., & Kerle, N. (2020). Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing, Geo-spatial Information Science, 23(2), 165-181.
• Bae, J., Bae, H., Kim, G., Park, E., & Cho, B. (2020). Development of unmanned aerial vehicle remote sensing technology for abiotic stress monitoring of citrus ‘Unshiu’ using multispectral imaging. Journal of the Korean Society for Nondestructive Testing, 40, 274-284.
• Fields, N. R. (2012). Advantages and challenges of unmanned aerial vehicle autonomy in the Postheroic age. Master’s Thesis, James Madison University, 205.
• Forsman, J., & Westergren, M. (2019). Potential and limitations with UAV deliveries to ships at sea, Bachelor’s Thesis in Marine Engineering, Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 1-38.
• Fraser, B. T., & Congalton, R. G. (2019). Evaluating the effectiveness of Unmanned Aerial Systems (UAS) for collecting thematic map accuracy assessment reference data in New England forests. Forests, 10(1), 24.
• Du, L., Zhou, T., Zou, Z., Zhao, X., Huang, K., & Wu, H. (2014). Mapping forest biomass using remote sensing and national forest inventory in China. Forests, 5(6), 1267-1283.
• Sun, Z., Wang, D., & Zhong, G. (2018). Extraction of farmland geographic information using OpenStreetMap data. In 2018 7th International Conference on Agro-geoinformatics, 1-4.
• Kempen, B., Brus, D. J., & Heuvelink, G. B. (2012). Soil type mapping using the generalised linear geostatistical model: A case study in a Dutch cultivated peatland. Geoderma, 189, 540-553.
• Kavzoglu, T., Teke, A., & Yilmaz, E. O. (2021). Shared blocks-based ensemble deep learning for shallow landslide susceptibility mapping. Remote Sensing, 13(23), 4776.
• Rahaman, S. M., Khatun, M., Garai, S., Das, P., & Tiwari, S. (2022). Forest Fire Risk Zone Mapping in Tropical Forests of Saranda, Jharkhand, Using FAHP Technique. In Geospatial Technology for Environmental Hazards, 177-195, Springer, Cham.
• Şekertekin, A., & Marangoz, A. M. (2019). Zonguldak metropolitan alanındaki arazi kullanımı arazi örtüsünün yer yüzey sıcaklığına etkisi. Geomatik, 4(2), 101-111.
• Yılmaz, O. S., Gülgen, F., Güngör, R., & Kadı, F. (2018). Uzaktan algılama teknikleri ile arazi kullanım değişiminin incelenmesi: Köprübaşı İlçesi örneği. Geomatik, 10, 233-241.
• Harsanyi, J. C., & Chang, C. I. (1994). Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach. IEEE Transactions on Geoscience and Remote Sensing, 32(4), 779-785.
• Chapelle, O., Haffner, P., & Vapnik, V. N. (1999). Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks, 10(5), 1055-1064.
• Ciregan, D., Meier, U., & Schmidhuber, J. (2012). Multi-column deep neural networks for image classification. IEEE Conference on Computer Vision and Pattern Recognition, 3642-3649.
• Lavreniuk, M., Kussul, N., & Novikov, A. (2018). Deep learning crop classification approach based on sparse coding of time series of satellite data. In IEEE International Geoscience and Remote Sensing Symposium, 4812-4815.
• Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870.
• Hansen, M., Dubayah, R., & DeFries, R. (1996). Classification trees: an alternative to traditional land cover classifiers. International Journal of Remote Sensing, 17(5), 1075-1081.
• Rogan, J., Miller, J., Stow, D., Franklin, J., Levien, L., & Fischer, C. (2003). Land-cover change monitoring with classification trees using Landsat TM and ancillary data. Photogrammetric Engineering & Remote Sensing, 69(7), 793-804.
• Mondal, A., Kundu, S., Chandniha, S. K., Shukla, R., & Mishra, P. K. (2012). Comparison of support vector machine and maximum likelihood classification technique using satellite imagery. International Journal of Remote Sensing and GIS, 1(2), 116-123.
• Pal, M. (2008). Ensemble of support vector machines for land cover classification. International Journal of Remote Sensing, 29(10), 3043-3049.
• Chan, J. C. W., & 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 Sensing of Environment, 112(6), 2999-3011.
• 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 Geoscience and Remote Sensing Letters, 15(4), 607-611.
• Sun, L., & Schulz, K. (2015). The improvement of land cover classification by thermal remote sensing. Remote sensing, 7(7), 8368-8390.
• Sefercik, U. G., Kavzoglu, T., Colkesen, I., Adali, S., Dinc, S., Nazar, M., & Ozturk, M. Y. (2021). Land cover classification performance of multispectral RTK UAVs. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVI-4-W5-2021, 489-492.
• Bhosle, K., & Musande, V. (2019). Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images. Journal of the Indian Society of Remote Sensing, 47(11), 1949-1958.
• Avcı, C., Budak, M., Yağmur, N. & Balçık, F. (2023). Comparison between random forest and support vector machine algorithms for LULC classification. International Journal of Engineering and Geosciences, 8 (1), 1-10. https://doi.org/10.26833/ijeg.987605
• Khorrami, B., Gunduz, O., Patel, N., Ghouzlane, S., & Najjar, M. (2019). Land surface temperature anomalies in response to changes in forest cover. International Journal of Engineering and Geosciences, 4(3), 149-156.
• Jenal, A., Lussem, U., Bolten, A., Gnyp, M., Schellberg, J., Jasper, J., Bongartz, J., & Bareth, G. (2020). Investigating the potential of a newly developed UAV-based VNIR/SWIR imaging system for forage mass monitoring. PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science, 88, 493-507.
• Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Association for Computing Machinery, San Francisco, CA, USA, 785-794.
• Ma., L., Zhou, M., & Li, C. (2017). Land covers classification based on Random Forest method using features from full-waveform lidar data, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(2/W7), 263-268.
• Zhang, W., Li, W., Zhang, C., Hanink, D. M., Li, X., & Wang, W. (2017). Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View. Computer Environment Urban Systems, 64, 215-228.
• MAPIR calibration target capture procedure, https://www.mapir.camera/pages/calibration-target-capture-procedure-v2
• Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). ‘Structure-from-Motion’photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300-314.
• Sefercik, U. G., Tanrikulu, F., & Atalay, C. (2019). Photogrammetric 3D modelling potential comparison of SFM-based new generation image matching software. In The 40th Asian Conference on Remote Sensing, Korea.
• Yakar, M., & Dogan, Y. (2018, November). 3D Reconstruction of Residential Areas with SfM Photogrammetry. In Conference of the Arabian Journal of Geosciences (pp. 73-75). Springer, Cham.
• Şasi, A. & Yakar, M. (2018). Photogrammetric modelling of Hasbey Dar'ülhuffaz (Masjid) using an unmanned aerial vehicle. International Journal of Engineering and Geosciences, 3 (1), 6-11.
• Yakar, M., & Doğan, Y. (2018). GIS and three-dimensional modeling for cultural heritages. International Journal of Engineering and Geosciences, 3(2), 50-55.
• Ulvi, A., Yakar, M., Yiğit, A. Y. & Kaya, Y. (2020). İha Ve Yersel Fotogrametrik Teknikler Kullanarak Aksaray Kızıl Kilisenin 3b Modelinin Ve Nokta Bulutunun Elde Edilmesi. Geomatik, 5 (1), 19-26.
• Mırdan, O. & Yakar, M. (2017). Tarihi Eserlerin İnsansız Hava Aracı İle Modellenmesinde Karşılaşılan Sorunlar. Geomatik, 2 (3), 118-125.
• Teixeira, A. A., Mendes Júnior, C. W., Bredemeier, C., Negreiros, M., Aquino, R. D. S. (2020). Evaluation of the radiometric accuracy of images obtained by a Sequoia multispectral camera. Engenharia Agrícola, 40, 759-768.