{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T14:29:36Z","timestamp":1774880976282,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In general, the quality of imagery from Unmanned Aerial Vehicles (UAVs) is evaluated after the flight, and then a decision is made on the further value and use of the acquired data. In this paper, an a priori (preflight) image quality prediction methodology is proposed to estimate the preflight image quality and to avoid unfavourable flights, which is extremely important from a time and cost management point of view. The XBoost Regressor model and cross-validation were used for machine learning of the model and image quality prediction. The model was learned on a rich database of real-world images acquired from UAVs under conditions varying in both sensor type, UAV type, exposure parameters, weather, topography, and land cover. Radiometric quality indices (SNR, Entropy, PIQE, NIQE, BRISQUE, and NRPBM) were calculated for each image to train and test the model and to assess the accuracy of image quality prediction. Different variants of preflight parameter knowledge were considered in the study. The proposed methodology offers the possibility of predicting image quality with high accuracy. The correlation coefficient between the actual and predicted image quality, depending on the number of parameters known a priori, ranged from 0.90 to 0.96. The methodology was designed for data acquired from a UAV. Similar prediction accuracy is expected for other low-altitude or close-range photogrammetric data.<\/jats:p>","DOI":"10.3390\/rs13234757","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4757","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Application of the XBoost Regressor for an A Priori Prediction of UAV Image Quality"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9291-8163","authenticated-orcid":false,"given":"Aleksandra","family":"Sekrecka","sequence":"first","affiliation":[{"name":"Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Honkavaara, E., and Khoramshahi, E. (2018). Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment. Remote Sens., 10.","DOI":"10.3390\/rs10020256"},{"key":"ref_2","first-page":"1","article-title":"Procedures for correcting Digital Camera Imagery Acquired by the AggieAir remote sensing platform","volume":"186","author":"Clemens","year":"2012","journal-title":"All Grad. Plan B Other Rep."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kedzierski, M., Wierzbicki, D., Sekrecka, A., Fryskowska, A., Walczykowski, P., and Siewert, J. (2019). Influence of Lower Atmosphere on the Radiometric Quality of Unmanned Aerial Vehicle Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11101214"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3101","DOI":"10.1080\/01431161.2016.1230291","article-title":"A physical-based atmospheric correction algorithm of unmanned aerial vehicles images and its utility analysis","volume":"38","author":"Yu","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.cja.2016.01.012","article-title":"Haze removal for UAV reconnaissance images using layered scattering model","volume":"29","author":"Yuqing","year":"2016","journal-title":"Chin. J. Aeronaut."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6633","DOI":"10.3390\/s150306633","article-title":"Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images","volume":"15","author":"Yoon","year":"2015","journal-title":"Sensors"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wierzbicki, D., Kedzierski, M., and Sekrecka, A. (2020). A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts. Remote Sens., 12.","DOI":"10.3390\/rs12010025"},{"key":"ref_8","unstructured":"St\u00f6cker, C., Nex, F.C., Koeva, M.N., and Zevenbergen, J.A. (2018, January 29). Data quality assessment of UAV-based products for land tenure recording in East Africa. Proceedings of the NCG Symposium 2018, Wageningen, The Netherlands."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Burdziakowski, P. (2021). Polymodal Method of Improving the Quality of Photogrammetric Images and Models. Energies, 14.","DOI":"10.3390\/en14123457"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"013019","DOI":"10.1117\/1.JEI.22.1.013019","article-title":"Image informative maps for component-wise estimating parameters of signal-dependent noise","volume":"22","author":"Uss","year":"2013","journal-title":"J. Electron. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yuan, L., Sun, J., Quan, L., and Shum, H.Y. (2007, January 5\u20139). Image deblurring with blurred\/noisy image pairs. Proceedings of the ACM SIGGRAPH 2007 Papers, San Diego, CA, USA.","DOI":"10.1145\/1275808.1276379"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sekrecka, A., Wierzbicki, D., and Kedzierski, M. (2020). Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles. Remote Sens., 12.","DOI":"10.3390\/rs12061040"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1080\/01431161.2016.1275060","article-title":"Photogrammetric reconstruction of homogenous snow surfaces in alpine terrain applying near-infrared UAS imagery","volume":"38","author":"Adams","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Brouk, I., Nemirovsky, A., and Nemirovsky, Y. (2008, January 13\u201314). Analysis of noise in CMOS image sensor. Proceedings of the 2008 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems, Tel-Aviv, Israel.","DOI":"10.1109\/COMCAS.2008.4562800"},{"key":"ref_15","first-page":"372","article-title":"Assessing a 35mm Fixed-Lens Sony Alpha-5100 Intrinsic Parameters Prior to, During, and Post UAV Flight Mission","volume":"4","author":"Tjahjadi","year":"2019","journal-title":"KnE Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kelcey, J., and Lucieer, A. (September, January 25). Sensor correction and radiometric calibration of a 6-band multispectral imaging sensor for UAV remote sensing. Proceedings of the 2012 XXII ISPRS Congress, Melbourne, Australia.","DOI":"10.5194\/isprsarchives-XXXIX-B1-393-2012"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"04018058","DOI":"10.1061\/(ASCE)AS.1943-5525.0000879","article-title":"New crack detection method for bridge inspection using UAV incorporating image processing","volume":"31","author":"Lei","year":"2018","journal-title":"J. Aerosp. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.isprsjprs.2019.11.020","article-title":"A two-step approach for the correction of rolling shutter distortion in UAV photogrammetry","volume":"160","author":"Zhou","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zuo, Y., Liu, J., Bai, G., Wang, X., and Sun, M. (2017). Airborne infrared and visible image fusion combined with region segmentation. Sensors, 17.","DOI":"10.3390\/s17051127"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wierzbicki, D., Kedzierski, M., Fryskowska, A., and Jasinski, J. (2018). Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV. Remote Sens., 10.","DOI":"10.3390\/rs10091348"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2005.11.016","article-title":"Remote sensing image-based analysis of the relationship between urban heat island and land use\/cover changes","volume":"104","author":"Chen","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_22","unstructured":"Ozturk, E., Erden, F., and Guvenc, I. (2020). RF-Based Low-SNR Classification of UAVs Using Convolutional Neural Networks. arXiv, Available online: https:\/\/arxiv.org\/abs\/2009.05519."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Burdziakowski, P., Specht, C., Dabrowski, P.S., Specht, M., Lewicka, O., and Makar, A. (2020). Using UAV photogrammetry to analyse changes in the coastal zone based on the sopot tombolo (Salient) measurement project. Sensors, 20.","DOI":"10.3390\/s20144000"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.measurement.2016.06.003","article-title":"Methodology of improvement of radiometric quality of images acquired from low altitudes","volume":"92","author":"Kedzierski","year":"2016","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Burdziakowski, P. (2020). A Novel Method for the Deblurring of Photogrammetric Images Using Conditional Generative Adversarial Networks. Remote Sens., 12.","DOI":"10.3390\/rs12162586"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"04020028","DOI":"10.1061\/(ASCE)CP.1943-5487.0000907","article-title":"Deep Learning\u2013Based Enhancement of Motion Blurred UAV Concrete Crack Images","volume":"34","author":"Liu","year":"2020","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, R., Ma, G., Qin, Q., Shi, Q., and Huang, J. (2018). Blind UAV Images Deblurring Based on Discriminative Networks. Sensors, 18.","DOI":"10.3390\/s18092874"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pashaei, M., Starek, M.J., Kamangir, H., and Berryhill, J. (2020). Deep Learning-Based Single Image Super-Resolution: An Investigation for Dense Scene Reconstruction with UAS Photogrammetry. Remote Sens., 12.","DOI":"10.3390\/rs12111757"},{"key":"ref_29","first-page":"9","article-title":"Deep learning as an alternative to super-resolution imaging in UAV systems","volume":"2","author":"Deshpande","year":"2020","journal-title":"Imaging Sens. Unmanned Aircr. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1109\/ICPR.1996.547440","article-title":"3D-measurement of geometrical shapes by photogrammetry and neural networks","volume":"Volume 4","author":"Lilienblum","year":"1996","journal-title":"Proceedings of the 13th International Conference on Pattern Recognition"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1007\/s11263-019-01235-8","article-title":"Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges","volume":"128","author":"Ren","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4162","DOI":"10.1109\/TCSVT.2020.3046625","article-title":"Multi-level Fusion and Attention-guided CNN for Image Dehazing","volume":"31","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.neucom.2021.01.042","article-title":"Haze concentration adaptive network for image dehazing","volume":"439","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_34","unstructured":"Ocampo, R. (2021, June 20). Deep CNN-Based Blind Image Quality Predictor in Python. Available online: https:\/\/towardsdatascience.com\/deep-image-quality-assessment-with-tensorflow-2-0-69ed8c32f195."},{"key":"ref_35","unstructured":"Ocampo, R. (2021, June 20). Image Quality Assessment: A Survey. Available online: https:\/\/medium.com\/@ocampor\/advanced-methods-for-iqa-37581ec3c31f."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, Z., Lu, L., and Bovik, A. (2002, January 13\u201317). Why Is Image Quality Assessment so difficult?. Proceedings of the IEEE International Conference on Acoustics, Speech, & Signal Processing, Orlando, FL, USA.","DOI":"10.1109\/ICASSP.2002.5745362"},{"key":"ref_37","unstructured":"Wang, Z., Simoncelli, E.P., and Bovik, A.C. (2003, January 9\u201312). Multiscale structural similarity for image quality assessment. Proceedings of the Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","unstructured":"Mohammadi, P., Ebrahimi-Moghadam, A., and Shirani, S. (2014). Subjective and objective quality assessment of image: A survey. arXiv, Available online: http:\/\/arxiv.org\/abs\/1406.7799."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TCSVT.2006.887086","article-title":"An image quality evaluation method based on digital watermarking","volume":"17","author":"Wang","year":"2007","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_41","unstructured":"Carnec, M., Le Callet, P., and Barba, D. (2003, January 14\u201317). An image quality assessment method based on perception of structural information. Proceedings of the 2003 International Conference on Image Processing IEEE (Cat. No. 03CH37429), Barcelona, Spain."},{"key":"ref_42","unstructured":"Aja-Fern\u00e1ndez, S., Est\u00e9par, R.S., Alberola-L\u00f3pez, C., and Westin, C.F. (September, January 30). Image Quality Assessment based on Local Variance. Proceedings of the 28th IEEE EMBS Annual International Conference, New York, NY, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Venkatanath, N., Praneeth, D., Bh, M.C., Channappayya, S.S., and Medasani, S.S. (March, January 27). Blind image quality evaluation using perception based features. Proceedings of the 2015 Twenty First National Conference on Communications (NCC), Mumbai, India.","DOI":"10.1109\/NCC.2015.7084843"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201cCompletely Blind\u201d Image Quality Analyzer","volume":"20","author":"Mittal","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-reference image quality assessment in the spatial domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process. Dec."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"64920I","DOI":"10.1117\/12.702790","article-title":"The Blur Effect: Perception and estimation with a new no-reference perceptual blur metric","volume":"6492","author":"Crete","year":"2007","journal-title":"Proc. SPIE"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1109\/TNN.2011.2120620","article-title":"Blind image quality assessment using a general regression neural network","volume":"22","author":"Li","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ding, K., Ma, K., Wang, S., and Simoncelli, E.P. (2020). Image quality assessment: Unifying structure and texture similarity. arXiv, Available online: http:\/\/arxiv.org\/abs\/2004.07728.","DOI":"10.1109\/TPAMI.2020.3045810"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Shi, S., Bai, Q., Cao, M., Xia, W., Wang, J., Chen, Y., and Yang, Y. (2021, January 19\u201325). Region-adaptive deformable network for image quality assessment. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Virtual.","DOI":"10.1109\/CVPRW53098.2021.00042"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"26285","DOI":"10.1007\/s11042-020-09229-2","article-title":"Natural scene statistics model independent no-reference image quality assessment using patch based discrete cosine transform","volume":"79","author":"Nizami","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_51","unstructured":"(2020, June 10). Synoptic Data. Available online: http:\/\/www.pogodynka.pl\/polska\/mapa_synoptyczna."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Contreras-de-Villar, F., Garc\u00eda, F.J., Mu\u00f1oz-Perez, J.J., Contreras-de-Villar, A., Ruiz-Ortiz, V., Lopez, P., Garcia-L\u00f3pez, S., and Jigena, B. (2021). Beach Leveling Using a Remotely Piloted Aircraft Sys-tem (RPAS): Problems and Solutions. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9010019"},{"key":"ref_53","unstructured":"Klaas, P. (2019, December 27). Trimble UX5 Aerial Imaging Solution Vegetation Monitoring Frequently Asked Questions. Trimble Navigation Ltd.. Available online: http:\/\/surveypartners.trimble.com."},{"key":"ref_54","unstructured":"(2021, September 17). Parrot. Available online: https:\/\/www.parrot.com\/business-solutions-us\/parrot-professional\/parrot-sequoia."},{"key":"ref_55","unstructured":"Leventis, D. (2021, June 17). XGBoost Mathematics Explained. A walkthrough of the Gradient Boosted Trees Algorithm\u2019s Maths. Available online: https:\/\/towardsdatascience.com\/xgboost-mathematics-explained-58262530904a."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_57","unstructured":"Duan, Y., Li, S., Chen, S., Tan, Q., Chen, C., and Wang, M. (2020, January 19\u201321). Forecasting the short-term urban gas daily demand in winter based on the XGBoost algorithm. Proceedings of the IOP Conference Series: Earth and Environmental Science, Xiamen, China."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1080\/00202967.2021.1898183","article-title":"The prediction of the ZnNi thickness and Ni% of ZnNi alloy electroplating using a machine learning method","volume":"99","author":"Katirci","year":"2021","journal-title":"Trans. Inst. Met. Finish."},{"key":"ref_59","unstructured":"(2021, September 15). XGBOOST: Differences between Gbtree and Gblinear. Available online: https:\/\/www.avato-consulting.com\/?p=28903&lang=en."},{"key":"ref_60","unstructured":"(2021, August 10). Matlab Documentation. Available online: https:\/\/www.mathworks.com\/help\/images\/ref\/niqe.html."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"359","DOI":"10.5194\/isprs-archives-XLII-4-359-2018","article-title":"Analysis of UAV image quality using Edge Analysis. International Archives of the Photogrammetry","volume":"XLII-4","author":"Lim","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_62","first-page":"19","article-title":"A UAV Reconnaissance Image Quality Assessing Method","volume":"37","author":"Ding","year":"2007","journal-title":"Radio Eng. China"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2016.09.010","article-title":"Automatic detection of blurred images in UAV image sets","volume":"122","author":"Sieberth","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"8","DOI":"10.4236\/jcc.2019.73002","article-title":"Image Quality Assessment through FSIM, SSIM, MSE, and PSNR-A Comparative Study","volume":"7","author":"Sara","year":"2019","journal-title":"J. Comput. Commun."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mantiuk, R.K., Tomaszewska, A., and Mantiuk, R. (2012). Comparison of four subjective methods for image quality assessment. Computer Graphics Forum, Blackwell Publishing Ltd.","DOI":"10.1111\/j.1467-8659.2012.03188.x"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1260\/1369-4332.17.3.289","article-title":"Quality assessment of unmanned aerial vehicle (UAV) based visual inspection of structures","volume":"17","author":"Morgenthal","year":"2014","journal-title":"Adv. Struct. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4757\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:35:10Z","timestamp":1760168110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4757"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,24]]},"references-count":66,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234757"],"URL":"https:\/\/doi.org\/10.3390\/rs13234757","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,24]]}}}