Geomatics and Environmental Engineering, vol. 19, no. 2

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Vol. 19 No. 2 (2025): Geomatics and Environmental Engineering

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Submitted
Dec 30, 2024
Accepted
Mar 19, 2025
Published
Apr 30, 2025
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Comparison of Machine-Learning Algorithms for SPOT 7 Multispectral Image Classification

https://doi.org/10.7494/geom.2025.19.2.71
Davide Morale
International PhD Programme “Environment, Resources, and Sustainable Development,” University of Naples “Parthenope,” Department of Science and Technology, Naples, Italy; Fondazione CMCC-Centro Euro Mediterraneo sui Cambiamenti Climatici, Italy
https://orcid.org/0000-0001-8139-8935
Claudio Parente
University of Naples “Parthenope,” Department of Science and Technology, Naples, Italy
https://orcid.org/0000-0002-8328-9764
Salvatore Falanga Bolognesi
Ariespace s.r.l., Naples, Italy
https://orcid.org/0000-0003-0134-2574

Corresponding Author(s) : Claudio Parente

claudio.parente@uniparthenope.it

Geomatics and Environmental Engineering, Vol. 19 No. 2 (2025): Geomatics and Environmental Engineering

Abstract

Precise and timely land-cover identification plays an important role in effective environmental monitoring and land management. This study compares theperformanceoffive machine-learningclassifiers –supportvectormachine (SVM), decision tree (DT), normal Bayes (NB), random forest (RF), and k-nearest neighbor (k-NN) – in the land-cover mapping of the Agro Nocerino Sarnese area (Southern Italy) using high-resolution SPOT 7 pan-sharpened multispectral images with a pixel size of 1.5 m × 1.5 m. The data set consisted of blue, green, red, and near-infrared (NIR) bands and was processed with Orfeo ToolBox (OTB) software. Two data sets were analyzed: DS-3B (which included only the visible bands [blue, green, and red]), and DS-4B (which also included the NIR band). A comparison of the classifiers’ performances across various land-cover classes was conducted in order to assess their respective classification accuracy. The results showed that SVM and k-NN achieved the highest overall accuracy levels (93% and 92%, respectively) using only the visible bands, whereas the decision tree classifier performed best when the NIR band was included. Random forest achieved excellent accuracy in vegetation classes (88–99%) but struggled with misclassifications in bare soil and man-made classes such as buildings and roads. These results emphasized the significant impact of data set characteristics on classifier performance as well as the importance of band selection and pan-sharpening techniques in high-resolution land-cover mapping.

Keywords

land-cover classification machine learning SPOT 7 Orfeo ToolBox support vector machine random forest decision tree high-resolution imagery
Morale, D., Parente, C., & Falanga Bolognesi, S. (2025). Comparison of Machine-Learning Algorithms for SPOT 7 Multispectral Image Classification. Geomatics and Environmental Engineering, 19(2), 71–97. https://doi.org/10.7494/geom.2025.19.2.71
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References
  1. Papale D., Barbati A.: Supporto informativo del telerilevamento per il monitoraggio e la valutazione funzionale dei rimboschimenti come mezzi di lotta alla desertificazione. Geotema, vol. 25, 2005, pp. 31–37.
  2. Acharki S.: PlanetScope contributions compared to Sentinel-2, and Landsat-8 for LULC mapping. Remote Sensing Applications: Society and Environment, vol. 27, 2022, 100774. https://doi.org/10.1016/j.rsase.2022.100774.
  3. European Environment Agency (EEA): E. CORINE Land Cover 2018 (Raster 100 m), Europe, 6-Yearly – Version 2020_20u1,May 2020. 13.05.2020. https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac.
  4. Diaz-Pacheco J., Gutiérrez J.: Exploring the limitations of CORINE land cover for monitoring urban land-use dynamics in metropolitan areas. Journal of Land Use Science, vol. 9(3), 2014, pp. 243–259. https://doi.org/10.1080/1747423X.2012.761736.
  5. Brown C.F., Brumby S.P., Guzder-Williams B., Birch T., Hyde S.B., Mazzariello J., Czerwinski W., Pasquarella V.J., Haertel R., Ilyushchenko S., Schwehr K., Weisse M., Stolle F., Hanson C., Guinan O., Moore R., Tait A.M.: Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, vol. 9(1), 2022, 251. https://doi.org/10.1038/s41597-022-01307-4.
  6. Venter Z.S., Barton D.N., Chakraborty T., Simensen T., Singh G.: Global 10 m land use land cover datasets: A comparison of dynamic world, world cover and Esri land cover. Remote Sensing, vol. 14(16), 2022, 4101. https://doi.org/10.3390/rs14164101.
  7. Giri C., Pengra B., Long J., Loveland T.R.: Next generation of global land cover characterization, mapping, and monitoring. International Journal of Applied Earth Observation and Geoinformation, vol. 25(1), 2013, pp. 30–37. https://doi.org/10.1016/j.jag.2013.03.005.
  8. Qin R., Liu T.: A review of landcover classification with very-high resolution remotely sensed optical images – Analysis unit, model scalability and transferability. Remote Sensing, vol. 14(3), 2022, 646. https://doi.org/10.3390/rs14030646.
  9. Rahman A., Abdullah H.M., Tanzir M.T., Hossain M.J. Khan B.M., Miah M.G., Islam I.: Performance of different machine learning algorithms on satellite image classification in rural and urban setup. Remote Sensing Applications: Society and Environment, vol. 20(5), 2020, 100410. https://doi.org/10.1016/j.rsase.2020.100410.
  10. Maxwell A.E., Warner T.A., Fang F.: Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, vol. 39(9), 2018, pp. 2784–2817. https://doi.org/10.1080/01431161.2018.1433343.
  11. Das S., Dey A., Pal A., Roy N.: Applications of artificial intelligence in machine learning: Review and prospect. International Journal of Computer Applications, vol. 115(9), 2015, pp. 31–41. https://doi.org/10.5120/20182-2402.
  12. Jordan M.I., Mitchell T.M.: Machine learning: Trends, perspectives, and prospects.Science,vol.349(6245),2015,pp.255–260.https://doi.org/10.1126/science.aaa8415.
  13. Stilgoe J.: Machine learning, social learning and the governance of self-driving cars. Social Studies of Science, vol. 48(1), 2018, pp. 25–56. https://doi.org/10.1177/0306312717741687.
  14. Karpatne A., Ebert-Uphoff I., Ravela S., Babaie H.A., Kumar V.: Machine learning for the geosciences: Challenges and opportunities. IEEE Transactions on Knowledgeand Data Engineering,vol. 31(8), 2018, pp. 1544–1554. https://doi.org/10.1109/TKDE.2018.2861006.
  15. Ziadia A., Habibi M., Kelouwani S.: Machine learning study of the effect of process parameters on tensile strength of FFF PLA and PLA-CF. Eng, vol. 4(4), 2023, pp. 2741–2763. https://doi.org/10.3390/eng4040156.
  16. Caruana R., Niculescu-Mizil A.: An empirical comparison of supervised learning algorithms, [in:] ICML ‘06: Proceedings of the 23rd International Conference onMachineLearning,AssociationforComputingMachinery,NewYork, pp. 161–168. https://doi.org/10.1145/1143844.1143865.
  17. Campbell C., Ying Y.: Support Vector Machines for Classification, [in:] Campbell C., Ying Y., Learning with Support Vector Machines, Synthesis Lectures on Artificial Intelligence and Machine Learning, Springer, Cham, Switzerland 2022, pp. 1–25.
  18. Pires de Lima R., Marfurt K.: Convolutional neural network for remote-sensing scene classification: Transfer learning analysis. Remote Sensing, vol. 12(1), 2019, 86. https://doi.org/10.3390/rs12010086.
  19. Perlich C., Provost F., Simonoff J.: Tree induction vs. logistic regression: A learning-curve analysis. Journal of Machine Learning Research, vol. 4, 2003, pp. 211–255.
  20. Tariq A., Jiango Y., Li Q., Gao J., Lu L., Soufan W., Almutairi K.F., HabiburRahman M.: Modelling, mapping and monitoring of forest cover changes, using support vector machine, Kernel logistic regression and naive Bayes tree models with optical remote sensing data. Heliyon, vol. 9(2), 2023, e13212. https://doi.org/10.1016/j.heliyon.2023.e13212.
  21. Belgiu M., Drăguţ L.: Random Forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, 2016, pp. 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011.
  22. Xu M., Watanachaturaporn P., Varshney P.K., Arora M.K.: Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, vol. 97(3), 2005, pp. 322–336. https://doi.org/10.1016/j.rse.2005.05.008.
  23. Blanzieri E., Melgani F.: Nearest neighbor classification of remote sensing images with the maximal margin principle. IEEE Transactions on Geoscience and Remote Sensing, vol. 46(6), 2008, pp. 1804–1811. https://doi.org/10.1109/TGRS.2008.916090.
  24. Dash S.S., Nayak S.K., Mishra D.: A review on machine learning algorithms, [in:] Mishra D., Buyya R., Mohapatra P., Patnaik S. (eds.), Intelligent and Cloud Computing: Proceedings of ICICC 2019, Volume 2, Smart Innovation, Systems and Technologies, vol. 153, Springer, Singapore 2020, pp. 495–507. https://doi.org/10.1007/978-981-15-6202-0_51.
  25. Kassambara A.: Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning. STHDA, Marseille 2017.
  26. Alloghani M., Al-Jumeily D., Mustafina J., Hussain A., Aljaaf A.J.: A systematic review on supervised and unsupervised machine learning algorithms for data science, [in:] Berry M.W., Mohamed A., Bee W.Y. (eds.), Supervised and Unsupervised Learning for Data Science, Springer, Cham 2020, pp. 3–21. https://doi.org/10.1007/978-3-030-22475-2_1.
  27. Abbas A.W., Minallh N., Ahmad N., Abid S.A.R., Khan M.A.A.: K-Means andISODATAclusteringalgorithmsforlandcoverclassification usingremote sensing. Sindh University Research Journal (Science Series), vol. 48(2), 2016, pp. 315–318.
  28. European Space Agency (ESA). https://earth.esa.int/eogateway/missions/spot-7 [access: 8.12.2024].
  29. NationalAeronauticsandSpaceAdministration(NASA):LandsatOrbit Swath. https://svs.gsfc.nasa.gov/11481 [access: 26.01.2025].
  30. Ehlers M., Klonus S., Johan Åstrand P., Rosso P.: Multi-sensor image fusion for pansharpening in remote sensing. International Journal of Image and Data Fusion, vol. 1(1), 2010, pp. 25–45. https://doi.org/10.1080/19479830903561985.
  31. Alcaras E., Parente C.: The effectiveness of pan-sharpening algorithms on different land cover types in GeoEye-1 satellite images. Journal of Imaging, vol. 9(5), 2023, 93. https://doi.org/10.3390/jimaging9050093.
  32. Gupta D.K., Prashar S., Singh S., Srivastava P.K., Prasad R.: Introduction to RADAR remote sensing, [in:] Srivastava P.K., Gupta D.K., Islam T., Han D., Prasad R. (eds.), Radar Remote Sensing: Applications and Challenges, Elsevier, 2022, pp. 3–27. https://doi.org/10.1016/B978-0-12-823457-0.00018-5.
  33. Vapnik V.: The Nature of Statistical Learning Theory. Springer Science & Business Media, New York 1999. https://doi.org/10.1007/978-1-4757-3264-1.
  34. Pal M., Mather P.M.: Support vector machines for classification in remote sensing. International Journal of Remote Sensing, vol. 26(5), 2005, pp. 1007–1011. https://doi.org/10.1080/01431160512331314083.
  35. Amarappa S., Sathyanarayana S.V.: Data classification using Support Vector Machine (SVM), a simplified approach. International Journal of Electronics and Computer Science Engineering, vol. 3(4), 2014, pp. 435–445.
  36. Melgani F., Bruzzone L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, vol. 42(8), 2004, pp. 1778–1790. https://doi.org/10.1109/TGRS.2004.831865.
  37. Shan F., He X., Xu H., Armaghani D.J., Sheng D.: Applications of machine learning in mechanised tunnel construction: A systematic review. Eng, vol. 4(2), 2023, pp. 1516–1535. https://doi.org/10.3390/eng4020087.
  38. Wang M., Wan Y., Ye Z., Lai X.: Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm. Information Sciences, vol. 402, 2017, pp. 50–68. https://doi.org/10.1016/j.ins.2017.03.027.
  39. Foody G.M., Mathur A.: A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, vol. 42(6), 2004, pp. 1335–1343. https://doi.org/10.1109/TGRS.2004.827257.
  40. Pal M., Foody G.M.: Evaluation of SVM, RVM and SMLR for accurate image classification with limited ground data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5(5), 2012, pp. 1344–1355. https://doi.org/10.1109/JSTARS.2012.2215310.
  41. Pal M., Mather P.M.: An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, vol. 86(4), 2003, pp. 554–565. https://doi.org/10.1016/S0034-4257(03)00132-9.
  42. Amoroso P.P., Ciaramella A., Ferone A., Parente C., Staiano A.: Application of decision tree algorithm on Landsat 9 OLI-2 images for coastline extraction, [in:] 2024 IEEE International Workshop on Metrology for the Sea: Learning to Measure Sea Health Parameters (MetroSea), IEEE, pp. 358–363. https://doi.org/10.1109/MetroSea62823.2024.10765729.
  43. Suthaharan S.: Decision tree learning, [in]: Suthaharan S., Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, Integrated Series in Information Systems, vol. 36, Springer, Boston 2016, pp. 237–269. https://doi.org/10.1007/978-1-4899-7641-3_10.
  44. Pal M., Mather P.M.: Decision tree-based classification of remotely sensed data, [in:] Proceedings of the 22nd Asian Conference on Remote Sensing: 5–9 November, 2001, Singapore. Volume 5, Center for Remote Imaging, Sensing and Processing, National University of Singapore, Queenstown 2001, p. 9.
  45. Berhane T.M., Lane C.R., Wu Q., Autrey B.C., Anenkhonov O.A., Chepinoga V.V., Liu, H.: Decision-tree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory. Remote Sensing, vol. 10(4), 2018, 580. https://doi.org/10.3390/rs10040580.
  46. Friedl M.A., Brodley C.E.: Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, vol. 61(3), 1997, pp. 399–409. https://doi.org/10.1016/S0034-4257(97)00049-7.
  47. Fukunaga K.: Introduction to Statistical Pattern Recognition. Academic Press, New York 1990.
  48. Open Computer Vision: Normal Bayes Classifier. https://docs.opencv.org/2.4/modules/ml/doc/normal_bayes_classifier.html [access: 8.12.2024].
  49. Ahmad A., Sakidin H., Sari M.Y.A., Sufahani S.F.: Naïve Bayes classification of high-resolution aerial imagery. International Journal of Advanced Computer Science and Applications, vol. 12(11), 2021, pp. 168–177. https://doi.org/10.14569/IJACSA.2021.0121120.
  50. Solares C., Sanz A.M.: Different Bayesian network models in the classification of remote sensing images, [in:] Yin H., Tino P., Corchado E., Byrne W., Yao X. (eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2007: 8th International Conference, Birmingham, UK, December 16–19, 2007: Proceedings, Lecture Notes in Computer Science, vol. 4881, Springer, Berlin, Heidelberg 2007, pp. 10–16. https://doi.org/10.1007/978-3-540-77226-2_2.
  51. Yang B., Yu X.: Remote sensing image classification of geoeye-1 high-resolution satellite. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-4, 2014, pp. 325–328. https://doi.org/10.5194/isprsarchives-XL-4-325-2014.
  52. Chao L.I., Wen-Hui Z., Ran L.I., Jun-Yi W., Ji-Ming L.: Research on star/galaxy classification based on stacking ensemble learning. Chinese Astronomy and Astrophysics, vol. 44(3), 2020, pp. 345–355. https://doi.org/10.1016/j.chinastron.2020.08.005.
  53. Breiman L.: Random forests. Machine Learning, vol. 45(1), 2001, pp. 5–32. https://doi.org/10.1023/A:1010933404324.
  54. Levatić J., Ceci M., Kocev D., Džeroski S.: Semi-supervised classification trees. Journal of Intelligent Information Systems, vol. 49(3), 2017, pp. 461–486. https://doi.org/10.1007/s10844-017-0457-4.
  55. Waske B., Benediktsson J.A., Sveinsson J.R.: Random Forest Classification of Remote Sensing Data, [in:] Chen C.H. (ed.), Signal and Image Processing for Remote Sensing, CRC Press, Boca Raton 2012, pp. 365–374.
  56. Gislason P.O., Benediktsson J.A., Sveinsson J.R.: Random Forest classification of multisource remote sensing and geographic data, [in:] IGARSS 2004: 2004 IEEE International Geoscience and Remote Sensing Symposium. Volume 2, IEEE, 2004, pp. 1049–1052. https://doi.org/10.1109/IGARSS.2004.1368591.
  57. Akar Ö., Güngör O.: Classification of multispectral images using Random Forestalgorithm.Journal of Geodesy and Geoinformation, vol. 1(2), 2012, pp. 105–112. https://doi.org/10.9733/jgg.241212.1.
  58. McRoberts R.E., Nelson M.D., Wendt D.G.: Stratified estimation of forest area using satellite imagery, inventory data, and the k-Nearest Neighbors technique. Remote Sensing of Environment, vol. 82(2–3), 2002, pp. 457–468. https://doi.org/10.1016/S0034-4257(02)00064-0.
  59. Nurwauziyah I., Umroh Dian S., Putra I.G.B., Firdaus M.I.: Satellite image classification using Decision Tree, SVM and k-Nearest Neighbor. Department of Geomatics, National Cheng Kung University, Tainan, Taiwan, July 2018.
  60. Alcaras E., Amoroso P.P., Figliomeni F.G., Parente C., Vallario A.: Machine learning approaches for coastline extraction from Sentinel-2 images: K-Means and K-Nearest Neighbour algorithms in comparison, [in:] Borgogno-Mondino E., Zamperlin P. (eds.), Geomatics for Green and Digital Transition: 25th Italian Conference, ASITA 2022, Genova, Italy, June 20–24, 2022: Proceedings, Communications in Computer and Information Science, vol. 1651, Springer, Cham 2022, pp. 368–379. https://doi.org/10.1007/978-3-031-17439-1_27.
  61. Triguero I., García-Gil D., Maillo J., Luengo J., García S., Herrera F.: Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 9(2), 2019, e.1289. https://doi.org/10.1002/widm.1289.
  62. Thanh Noi P., Kappas M.: Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors, vol. 18(1), 2017, 18. https://doi.org/10.3390/s18010018.
  63. Abedi R., Bonyad A.E.: Estimation and mapping forest attributes using “k nearest neighbor” method on IRS-P6 LISS III satellite image data. Ecologia Balkanica, vol. 7(1), 2015, pp. 93–102.
  64. Strahler A.H., Boschetti L., Foody G.M., Friedl M.A., Hansen M.C., Herold M., Mayaux P., Morisette J.T., Stehman S.V., Woodcock C.E.: Global Land Cover Validation: Recommendations for Evaluation and Accuracy Assessment of Global Land Cover Maps. European Communities, Luxembourg 2006.
  65. Foody G.M.: Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering & Remote Sensing, vol. 70(5), 2004, pp. 627–633. https://doi.org/10.14358/PERS.70.5.627.
  66. Fung T., LeDrew E.: The determination of optimal threshold levels for change detection using various accuracy. Photogrammetric Engineering and Remote Sensing, vol. 54(10), 1988, pp. 1449–1454.
  67. Lawrence R.L., Moran C.J.: The AmericaView classification methods accuracy comparison project: A rigorous approach for model selection. Remote Sensing of Environment, vol. 170, 2015, pp. 115–120. https://doi.org/10.1016/j.rse.2015.09.008.
  68. Rogan J., Franklin J., Stow D., Miller J., Woodcock C., Roberts D.: Mapping land-cover modifications over large areas: A comparison of machine learning algorithms. Remote Sensing of Environment, vol. 112(5), 2008, pp. 2272–2283. https://doi.org/10.1016/j.rse.2007.10.004.
  69. Lippitt C.D., Rogan J., Li Z., Eastman J.R., Jones T.G.: Mapping selective logging in mixed deciduous forest. Photogrammetric Engineering & Remote Sensing, vol. 74(10), 2008, pp. 1201–1211. https://doi.org/10.14358/PERS.74.10.1201.
  70. Pal M.: Random forest classifier for remote sensing classification. International Journal of Remote Sensing, vol. 26(1), 2005, pp. 217–222. https://doi.org/10.1080/01431160412331269698.
  71. Adam E., Mutanga O., Odindi J., Abdel-Rahman E.M.: Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing, vol. 35(10), 2014, pp. 3440–3458. https://doi.org/10.1080/01431161.2014.903435.
  72. Zhang C., Xie Z.: Object-based vegetation mapping in the Kissimmee River watershed using HyMap data and machine learning techniques. Wetlands, vol. 33(2), 2013, pp. 233–244. https://doi.org/10.1007/s13157-012-0373-x.
  73. Maxwell A.E., Strager M.P., Warner T.A., Zégre N.P., Yuill C.B.: Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation. GIScience & Remote Sensing, vol. 51(3), 2014, pp. 301–320. https://doi.org/10.1080/15481603.2014.912874.
  74. Maxwell A.E., Warner T.A., Strager M.P., Pal M.: Combining RapidEye satellite imagery and Lidar for mapping of mining and mine reclamation. Photogrammetric Engineering & Remote Sensing, vol. 80(2), 2014, pp. 179–189. https://doi.org/10.14358/PERS.80.2.179-189.
  75. Maxwell A.E., Warner T.A., Strager M.P., Conley J.F., Sharp A.L.: Assessing machine-learning algorithms and image-and lidar-derived variables for GEOBIA classification of mining and mine reclamation. International Journal of Remote Sensing, vol. 36(4), 2015, pp. 954–978. https://doi.org/10.1080/01431161.2014.1001086.
  76. Ibrahim S.A.: Improving land use/cover classification accuracy from random forest feature importance selection based on synergistic use of sentinel data and digital elevation model in agriculturally dominated landscape. Agriculture, vol. 13(1), 2022, 98. https://doi.org/10.3390/agriculture13010098.
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