Integration of LIME with segmentation techniques for SVM classification in Sentinel-2 imagery

• Autorzy: Yilmaz Elif Ozlem , Uzun Yakup Kaan , Berkan Emre , Kavzoglu Taskin

Identyfikatory

DOI

10.24425/agg.2025.150691

• Języki publikacji: EN

Abstrakty

EN

Recent advancements in remote sensing technology have facilitated the acquisition of images with higher spatial resolution. In response to this rapid technological evolution, the paradigm of OBIA has emerged as a key approach. An essential component of OBIA is image segmentation, where the careful selection of an appropriate segmentation algorithm and its parameters significantly influences the quality of the segmentation output. This study aims to conduct LULC analysis on Sentinel-2 imagery and compare the accuracy of the SVM classifier across different segmentation methods produced by MRS, SLIC, Mean Shift, and Quick Shift algorithms. The selected study area is located in the Marmara region of Turkey and characterized by seven major LULC classes. The segmentation was conducted though four algorithms, with 60 segment features being extracted for each output, considering spectral, textural, and geometric attributes separately. Following the classification process with SVM, overall accuracies of 96.14% for the MRS, 91.00% for the SLIC, 89.95% for the Mean Shift and 87.95% for the Quick Shift approach were estimated. These results underscore the superior performance of the MRS algorithm with significant level of improvement. This high level of accuracy holds significant potential for delivering more dependable and precise outcomes in planning and decision-making processes. Moreover, integrating XAI, specifically the LIME algorithm, enhances the transparency and comprehensibility of classification analysis within the OBIA framework. Features associated with the NIR and SWIR bands were found to have predominantly positive effects. This integration contributes to improved transparency, enabling more informed and reliable decision-making processes.

Słowa kluczowe

EN

LIME; multi-resolution segmentation; simple linear iterative clustering; mean shift; Quick Shift

PL

teledetekcja; użytkowanie gruntów; segmentacja obrazu

• Wydawca: Komitet Geodezji PAN
• Czasopismo: Advances in Geodesy and Geoinformation
• Rocznik: 2025
• Tom: Vol. 74, no. 1
• Strony: art. no. e61, 2025
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Yilmaz Elif Ozlem

• Gebze Technical University, Kocaeli, Turkey

• https://orcid.org/0000-0002-6853-2148

autor

Uzun Yakup Kaan

• Gebze Technical University, Kocaeli, Turkey

• https://orcid.org/0009-0007-2556-8363

autor

Berkan Emre

• Gebze Technical University, Kocaeli, Turkey

• https://orcid.org/0009-0009-9861-6113

autor

Kavzoglu Taskin

• Gebze Technical University, Kocaeli, Turkey

• https://orcid.org/0000-0002-9779-3443

Bibliografia

• 1. Achanta, R., Shaji, A., Smith, K. et al. (2010). SLIC Superpixels, Technical Report no. 149300, EPFL.
• 2. Alshari, E.A. and Gawali, B.W. (2021). Development of Classification System for LULC Using Remote Sensing and GIS. Global Trans. Proc., 2(1), 8–17. DOI: 10.1016/j.gltp.2021.01.002.
• 3. Aryal, J., Sitaula, C., and Frery, A.C. (2023). Land Use and Land Cover (LULC) Performance Modeling Using Machine Learning Algorithms: A Case Study of the City of Melbourne, Australia. Sci. Rep., 13(13510). DOI: 10.1038/s41598-023-40564-0.
• 4. Baatz, M., and Schäpe, A. (2000). Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation. In: J. Strobl, T. Blaschke and G. Griesebner (Eds.): Angewandte Geographische Informationsverarbeitung XII, pp. 12–23, Wichmann, Heidelberg, Germany.
• 5. Ben-Hur, A., Ong, C.S., Sonnenburg, S. et al. (2008). Support vector machines and kernels for computational biology. PLoS Comput. Biol., 4(10), e1000173. DOI: 10.1371/journal.pcbi.1000173.
• 6. Cheng, Y. (1995). Mean Shift, Mode Seeking, and Clustering. IEEE Trans. Pattern Anal. Mach., 17(8), 790–799. DOI: 10.1109/34.400568.
• 7. Comaniciu, D., and Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24(5), 603–619. DOI: 10.1109/34.1000236.
• 8. Cortes, C., and Vapnik, V. (1995). Support-Vector Networks. Mach. Learn., 20. 273–297.
• 9. Dragut, L., Csillik, O., Eisank, C. et al. (2014). Automated Parameterisation for Multi-Scale Image Segmentation on Multiple Layers. ISPRS J. Photogramm. Remote Sens., 88, 119–127. DOI:10.1016/j.isprsjprs.2013.11.018.
• 10. Evgeniou, T., Pontil, M., and Poggio, T. (2000). Regularization networks and support vector machines. Adv. Comput. Math., 13, 1-50. DOI: 10.1023/A:1018946025316.
• 11. Feng, C., Zhang, W., Deng, H. et al. (2023). A Combination of OBIA And Random Forest Based on Visible UAV Remote Sensing for Accurately Extracted Information About Weeds in Areas with Different Weed Densities in Farmland. Remote Sens., 15(19), 4696. DOI: 10.3390/rs15194696.
• 12. Gani, M.A., Sajib, A.M., Siddik, M.A. et al. (2023). Assessing The Impact of Land Use and Land Cover on River Water Quality Using Water Quality Index and Remote Sensing Techniques. Environ. Monit. Assess., 195(4), 449. DOI: 10.1007/s10661-023-10989-1.
• 13. Ghnemat, R., Alodibat, S., and Abu Al-Haija, Q. (2023). Explainable Artificial Intelligence (XAI) for deep learning based medical imaging classification. J. Imaging, 9(9), 177. DOI: 10.3390/jimaging9090177.
• 14. Humbal, A., Chaudhary, N., Pathak, B. (2023). Urbanization Trends, Climate Change, and Environmental Sustainability. In: Pathak, B., Dubey, R.S. (Eds.) Climate Change and Urban Environment Sustainability. Disaster Resilience and Green Growth. Springer, Singapore. DOI: 10.1007/978-981-19-7618-6_9.
• 15. Ishikawa, S.N., Todo, M., Taki, M. et. al. (2023). Example-Based Explainable AI and Its Application for Remote Sensing Image Classification. Int. J. Appl. Earth Obs. Geoinf., 118, 103215. DOI:10.1016/j.jag.2023.103215.
• 16. Kavzoglu, T., and Colkesen, I. (2009). A kernel Functions Analysis for Support Vector Machines for Land Cover Classification. Int. J. Appl. Earth Obs. Geoinf., 11(5), 352–359. DOI: 10.1016/j.jag.2009.06.002.
• 17. Kavzoglu, T., Yildiz Erdemir, M. and Tonbul, H. (2016). A Region-Based Multi-Scale Approach for Object-Based Image Analysis. ISPRS Archives, 41(B7), 241–247. DOI: 10.5194/isprs-archives-XLIB7-241-2016.
• 18. Kavzoglu, T., and Tonbul, H. (2017). A Comparative Study of Segmentation Quality for Multi-Resolution Segmentation and Watershed Transform, Istanbul, Turkey. 8th International Conference on Recent Advances in Space Technologies (RAST), 19–22 June (pp. 113–117), Istanbul, Turkey. DOI:10.1109/RAST.2017.8002984.
• 19. Kavzoglu, T., Erdemir, M.Y., and Tonbul, H. (2017). Classification of Semiurban Landscapes from Very High-Resolution Satellite Images Using a Regionalized Multiscale Segmentation Approach. J. Appl. Remote Sens., 11(3), 035016–035016. DOI: 10.1117/1.JRS.11.035016.
• 20. Kavzoglu, T., and Tonbul, H. (2018). An Experimental Comparison of Multi-Resolution Segmentation. SLIC And K-Means Clustering for Object-Based Classification of VHR Imagery. Int. J. Remote Sens., 39(18), 6020–6036. DOI: 10.1080/01431161.2018.1506592.
• 21. Kavzoglu, T., Tonbul, H., Yildiz Erdemir, M. et al. (2018). Dimensionality Reduction and Classification of Hyperspectral Images Using Object-Based Image Analysis. J. Indian Soc. Remote Sens., 46, 1297–1306. DOI: 10.1007/s12524-018-0803-1.
• 22. Kavzoglu, T., Tso, B., and Mather, P.M. (2024). Classification Methods for Remotely Sensed Data. Third Edition, Boca Raton: CRC Press. DOI: 10.1201/9781003439172.
• 23. Lourenço, P., Teodoro, A.C., Gonçalves, J.A. et al. (2021). Assessing The Performance of Different OBIA Software Approaches for Mapping Invasive Alien Plants Along Roads with Remote Sensing Data. Int. J. Appl. Earth Obs. Geoinf., 95, 102263. DOI: 10.1016/j.jag.2020.102263.
• 24. Mohan, A., Singh, A.K., Kumar, B. et al. (2021). Review on Remote Sensing Methods for Landslide Detection Using Machine and Deep Learning. T. Emerg. Telecommun.. T., 32(7), e3998. DOI:10.1002/ett.3998.
• 25. Pal, N.R., and Pal, S.K. (1993). A Review on Image Segmentation Techniques. Pattern Recognit., 26(9), 1277–1294.
• 26. Pipaud, I., and Lehmkuhl, F. (2017). Object-based Delineation and Classification of Alluvial Fans by Application of Mean-Shift Segmentation and Support Vector Machines. Geomorphology, 293, 178–200. DOI: 10.1016/j.geomorph.2017.05.013.
• 27. Quader, M.A. (2019). Impact Of Rohingya Settlement on The Landcovers at Ukhiya Upazila in Cox’s Bazar, Bangladesh. Jagannath University Journal of Life and Earth Sciences, 5(1), 13–28.
• 28. Schölkopf, B. (2000). The kernel trick for distances. Adv. Neural Inf. Process. Syst., 13.
• 29. Steinhausen, M.J., Wagner, P.D., Narasimhan, B. et al. (2018). Combining Sentinel-1 and Sentinel-2 Data for Improved Land Use and Land Cover Mapping of Monsoon Regions. Int. J. Appl. Earth Obs. Geoinf., 73, 595–604. DOI: 10.1016/j.jag.2018.08.011.
• 30. Su, T., and Zhang, S. (2017). Local and Global Evaluation for Remote Sensing Image Segmentation. ISPRS J. Photogramm. Remote Sens., 130, 256–276. DOI: 10.1016/j.isprsjprs.2017.06.003.
• 31. Su, T., Li, H., Zhang, S. et al. (2015). Image Segmentation Using Mean Shift for Extracting Croplands from High-Resolution Remote Sensing Imagery. Remote Sens. Lett., 6(12), 952–961. DOI:10.1080/2150704X.2015.1093188.
• 32. Tassi, A., Daniela, G., Giuseppe, M. et al. (2021). Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park. Remote Sens., 13(12), 2299. DOI: 10.3390/rs13122299.
• 33. Teke, A., and Kavzoglu, T. (2024). Exploring the Decision-Making Process of Ensemble Learning Algorithms in Landslide Susceptibility Mapping: Insights from Local and Global eXplainable AI analyses. Adv. Space Res., 74(8), 3765–3785. DOI: 10.1016/j.asr.2024.06.082.
• 34. Temenos, A., Temenos, N., Kaselimi, M. et al. (2023). Interpretable Deep Learning Framework for Land Use and Land Cover Classification in Remote Sensing Using SHAP. IEEE Geosci. Remote S., 20, 8500105. DOI: 10.1109/LGRS.2023.3251652.
• 35. Tonbul, H., and Kavzoglu, T. (2020a). Semi-Automatic Building Extraction from Worldview-2 Imagery Using Taguchi Optimization. Photogramm. Eng. Remote Sensing, 86(9), 547–555. DOI:10.14358/PERS.86.9.547.
• 36. Tonbul, H., and Kavzoglu, T. (2020b). A Spectral Band Based Comparison of Unsupervised Segmentation Evaluation Methods for Image Segmentation Parameter Optimization. Int. J. Environ. Geoinformatics, 7(2), 132–139. DOI: 10.30897/ijegeo.641216.
• 37. Tonbul, H., Colkesen, I., and Kavzoglu, T. (2020). Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery. J. Geod. Sci., 10(1), 14–22. DOI: 10.1515/jogs-2020-0003.
• 38. Vedaldi, A., and Soatto, S. (2008). Quick Shift and Kernel Methods for Mode Seeking. In Computer Vision–ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, 12-18 October 2008, Proceedings, Part IV 10 (pp. 705–718). DOI: 10.1007/978-3-540-88693-8_52.
• 39. Yilmaz, E.O., and Kavzoglu, T. (2024). Quality Assessment for Multi-Resolution Segmentation and Segment-Anything Model Using WORLDVIEW-3 Imagery. ISPRS Archives, 48, 383–390. DOI:10.5194/isprs-archives-XLVIII-4-W9-2024-383-2024.
• 40. Zhu, L., Song, R., Sun, S. et al. (2022). Land Use/Land Cover Change and Its Impact on Ecosystem Carbon Storage in Coastal Areas of China from 1980 to 2050. Ecol. Indic., 142, 109178. DOI:10.1016/j.ecolind.2022.109178.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025)