Edge-Aware Possibilistic Clustering with Uncertainty-Weighted Ensemble Learning for Land Cover Mapping

doi:10.23940/ijpe.25.10.p2.549558

Abstract

Abstract: Geographical areas are rapidly changing due to urban development and global change, which are creating difficulties in land cover classification affected by mixed pixels, noise, and spectral variability. To overcome this challenge, this study proposed a novel hybrid classification model that integrates Possibilistic C-Mean (PCM) clustering with learnable spatial filters and uncertainty aware ensemble learning to enhance the classification performance on remote sensing data. PCM is a soft classification method that is known for its robustness and outliers. For generation of land cover maps, PCM is used as a base classifier, and these generated soft classification maps are refined using learnable Gaussian (LGF) and learnable bilateral filters (LBF) whose parameters are optimized using Bayesian optimization to preserve edges and enhance spatial coherence. For improvement in accuracy, we added uncertainty weighted random forest (UW-RF) and uncertainty weighted XGBoost (UW-XGB) classifiers that incorporate pixel-wise uncertainty derived from the previous steps to weight the parameter's influence of each sample during training, where uncertainty is measured using Shannon entropy. The proposed hybrid models PCM+LGF+UW-RF, PCM+LGF+UW-XGB, PCM+LBF+UW-RF, PCM+LBF+UW-XGB are introduced and examined using Landsat 8 images of the Nainital region in India. Experimental results show the improvements in overall accuracy with 97.23% and kappa coefficient of 0.965 while compared to traditional methods. This study highlights the effectiveness of hybridizing the soft classification, adaptive spatial filtering and uncertainty modelling for robust land cover mapping in complex terrains.

Key words: Bayesian optimization, PCM, Shannon entropy, uncertainty, learnable spatial filters

Swati Vishnoi and Meenakshi Pareek. Edge-Aware Possibilistic Clustering with Uncertainty-Weighted Ensemble Learning for Land Cover Mapping [J]. Int J Performability Eng, 2025, 21(10): 549-558.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] Zhu Z., Qiu S., andYe S., 2022. Remote sensing of land change: A multifaceted perspective.Remote Sensing of Environment, 282, 113266.
[2] Tiwari P.,2008. Land use changes in Himalaya and their impacts on environment, society and economy: A study of the lake region in Kumaon Himalaya, India. Advances in Atmospheric Sciences,25(6), pp. 1029-1042.
[3] Haidarh M., Mu C., Liu Y., andHe X., 2025. Exploring traditional, deep learning and hybrid methods for hyperspectral image classification: A review.Journal of Information and Intelligence.
[4] Wang C., Xiao W., andLiu J., 2023. Developing an improved extreme gradient boosting model for predicting the international roughness index of rigid pavement.Construction and Building Materials, 408, 133523.
[5] Alshari E.A., andGawali B.W., 2021. Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings,2(1), pp. 8-17.
[6] Krishnapuram R., andKeller J.M., 2002. A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems,1(2), pp. 98-110.
[7] Kang S.Y., McGree J., andMengersen K., 2014. The choice of spatial scales and spatial smoothness priors for various spatial patterns.Spatial and Spatio-Temporal Epidemiology, 10, pp. 11-26.
[8] Tomasi C., andManduchi R., 1998. Bilateral filtering for gray and color images. InSixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271), pp. 839-846.
[9] Khan A.A., Chaudhari O., andChandra R., 2024. A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation.Expert Systems with Applications, 244, 122778.
[10] Sharma R., andRavinder M., 2023. Remote sensing image segmentation using feature based fusion on FCM clustering algorithm. Complex & Intelligent Systems,9(6), pp. 7423-7437.
[11] Zhang D., Zhao J., Chen J., Zhou Y., Shi B., andYao R., 2022. Edge-aware and spectral-spatial information aggregation network for multispectral image semantic segmentation.Engineering Applications of Artificial Intelligence, 114, 105070.
[12] Nair P., Srivastava D.K., andBhatnagar R., 2025. Multi-modal deep embedded clustering (MM-DEC): A novel framework for mineral detection using hyperspectral imagery.IEEE Access.
[13] Morales-Alvarez P., Pérez-Suay A., Molina R., andCamps-Valls G., 2017. Remote sensing image classification with large-scale gaussian processes. IEEE Transactions on Geoscience and Remote Sensing,56(2), pp. 1103-1114.
[14] Paris S.,2007. A gentle introduction to bilateral filtering and its applications. In ACM SIGGRAPH 2007 Courses, pp. 3-es.
[15] Li Q., Mou L., Shi Y., andZhu X.X., 2025. BANet: A bilateral attention network for extracting changed buildings between remote sensing imagery and cadastral maps.International Journal of Applied Earth Observation and Geoinformation, 139, 104486.
[16] Awujoola Olalekan J., Ogwueleka F., andOdion P., 2020. Effective and accurate bootstrap aggregating (bagging) ensemble algorithm model for prediction and classification of hypothyroid disease.Int. J. Comput. Appl, 975, 8887.
[17] Azmi S.S., andBaliga S., 2020. An overview of boosting decision tree algorithms utilizing AdaBoost and XGBoost boosting strategies. Int. Res. J. Eng. Technol,7(5), pp. 6867-6870.
[18] Hakkal S., andAit Lahcen A., 2024. XGBoost to enhance learner performance prediction.Computers and Education: Artificial Intelligence, 7, 100254.
[19] Lin L., Li S., Wang K., Guo B., Yang H., Zhong W., Liao P., andWang P., 2024. A new FCM-XGBoost system for predicting pavement condition index.Expert Systems with Applications, 249, 123696.
[20] Hassan M., Chaudhry A., Khan A., andKim J.Y., 2012. Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering. Computer Methods and Programs in Biomedicine,108(3), pp. 1261-1276.
[21] Adegun A.A., Viriri S., andTapamo J.R., 2023. Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis.Journal of Big Data, 10(1), 93.
[22] Özdemir Ö., andKaya A., 2019. Comparison of FCM, PCM, FPCM and PFCM algorithms in clustering methods.
[23] Guo W., Lu P., Peng X., andZhao Z., 2025. Learnable adaptive bilateral filter for improved generalization in single image super-resolution.Pattern Recognition, 162, 111396.
[24] Thanh D.K., Ngoc D.L., Dieu H.D., andTran V.A., 2025. Comparison of random forest and extreme gradient boosting algorithms in land cover classification in van yen district, yen bai province, Vietnam.J Hydro-Environ Res, 23, pp. 50-59.
[25] Abulfaraj A.W., andBinzagr F., 2025. A deep ensemble learning approach based on a vision transformer and neural network for multi-label image classification.Big Data and Cognitive Computing, 9(2), 39.
[26] Ghezloo F., Seyfioglu M.S., Soraki R., Ikezogwo W.O., Li B., Vivekanandan T., Elmore J.G., Krishna R., andShapiro L., 2025. Pathfinder: A multi-modal multi-agent system for medical diagnostic decision-making applied to histopathology.Arxiv Preprint Arxiv:2502.08916.
[27] Yan L., Roy D.P., Zhang H., Li J., andHuang H., 2016. An automated approach for sub-pixel registration of landsat-8 operational land imager (OLI) and sentinel-2 multi spectral instrument (MSI) imagery.Remote Sensing, 8(6), 520.
[28] Singh N., Singh V., andPandey A., 2025. City profile: nainital.Cities, 161, 105864.
[29] USGS, EarthExplorer, https://earthexplorer.usgs.gov/, accessed on October 1, 2025.
[30] Moradi Dashtpagerdi M., Nohegar A., Vagharfard H., Honarbakhsh A., Mahmoodinejad V., Noroozi A., andGhonchehpoor D., 2013. Application of spatial analysis techniques to select the most suitable areas for flood spreading. Water Resources Management,27(8), pp. 3071-3084.
[31] Pelikan M.,2005. Bayesian optimization algorithm. InHierarchical Bayesian Optimization Algorithm: Toward A New Generation of Evolutionary Algorithms, pp. 31-48.
[32] Keykha A., Imanipour M., Shahrokhi J., andAmiri M., 2025. The advantages and challenges of electronic exams: A qualitative research based on shannon entropy technique.Journal of Advances in Medical Education & Professionalism, 13(1), 1.
[33] Chen T., andGuestrin C., 2016. XGboost: A scalable tree boosting system. InProceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785-794.