مهندسی اکوسیستم بیابان

مهندسی اکوسیستم بیابان

A Comparison of Wavelet Feature-Based Minimum Distance and Rule-Based Fuzzy System for Classifying Medium-Resolution Images in Heterogeneous Landscapes

نوع مقاله : مقاله پژوهشی

نویسندگان
الهه اکبری 1
مصطفی دستورانی 2
هادی سلیمانی مقدم 3
1 گروه سنجش از دور و GIS، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری
2 گروه سنجش از دور و GIS، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران
3 گروه جغرافیا و برنامه ریزی شهری، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران
10.22052/deej.2026.258032.1129
چکیده
Classification and labeling of satellite images in remote sensing (RS), as well as improvement in their classification accuracy, have received researchers' attention for decades. The present study compares two classification methods—namely, the Rule-Based Fuzzy System and the proposed Wavelet Feature-Based Minimum Distance (W-F-M-D) algorithm—for medium-resolution images, particularly in heterogeneous landscapes. The Advanced Land Imager (ALI) was studied in an area located in southwestern Tehran, Iran. For validation, the land cover map obtained from both methods was compared with ground truth data through confusion matrix analysis, Kappa coefficient, and overall accuracy. The best results for the W-F-M-D algorithm were achieved with an overall accuracy of 93.55% and a Kappa coefficient of 0.89. Meanwhile, the results obtained from the fuzzy method were also satisfactory, with an overall accuracy of 89.27% and a Kappa coefficient of 0.84. However, the simplicity and speed of the proposed W-F-M-D algorithm constitute an additional advantage over the fuzzy method. From a different perspective, in the heterogeneous urban-agricultural area with moderate spatial resolution, the accuracy obtained for the urban area map—compared to that of bare lands—using the W-F-M-D method was evaluated as satisfactory, with producer's accuracy of 99.25% and user's accuracy of 91.67%.
کلیدواژه‌ها
Wavelet
Feature extraction
Rule-Based Fuzzy System
Classification
medium Resolution
موضوعات
  1. Al-Aarajy, K. H. A., Zaeen, A. A., & Abood, K. I. (2024). Supervised classification accuracy assessment using remote sensing and geographic information system. TEM Journal, 13(1), 396.
  2. Al-Ani, Laith Abdul Aziz, 2013, Image Integration Based Ant Colony System for Multiband Satellite Image Classification, Journal of Al-Nahrain University, 16 (2), 129-139.
  3. Alshari, E. A., & Gawali, B. W. (2021). Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings, 2 (1), 8–17.
  4. Asklany, S.A., Elhelow, K., Youssef, I.K. and El-Wahab, M.A., 2011. Rainfall events prediction using rule-based fuzzy inference system. Atmospheric Research, 101(1), pp.228-236.
  5. Bardossy, A., Samaniego, L. 2002. Fuzzy rule-based classification of remotely sensed imagery, IEEE Transactions on Geoscience and Remote Sensing 40 (2). 362–374.
  6. Bartel A., 2000. Analysis of landscape pattern: towards a ‘top down’ indicator for evaluation of land use, Ecol. Modelling 130. 87–94.
  7. Bethuel, C., Arvor, D., Corpetti, T., Hélie, J., Descals, A., Gaveau, D., Chéron-Bessou, C., Gignoux, J. and Corgne, S., (2025). Applying the Dempster–Shafer fusion theory to combine independent land-use maps: A case study on the mapping of oil palm plantations in sumatra, Indonesia. Remote Sensing, 17(2), 234.
  8. Bunce, R.G.H., Bogers, M.M.B., Evans, D., Halada, L., Jongman, R.H.G., Mucher, C.A., Bauch, B., de Blust, G., Parr, T.W., Olsvig-Whittaker, L., 2013. The significance of habitats as indicators of biodiversity and their link to species. Ecol. Indic. 33, 19–25.
  9. Chavan, M. S., Mastorakis, N., Chavan, M. N., & Gaikwad, M. S. (2011, February). Implementation of SYMLET wavelets to removal of Gaussian additive noise from speech signal. In Proceedings of recent researches in communications, automation, signal processing, nanotechnology, astronomy and nuclear physics: 10th WSEAS international conference on electronics, hardware, wireless and optical communications (EHAC’11), Cambridge (Vol. 37).
  10. Del Giudice, M. (2023). Individual and group differences in multivariate domains: What happens when the number of traits increases? Personality and Individual Differences, 213, 112282.
  11. Dolman, M. R., Kolarik, N. E., Caughlin, T. T., Brandt, J. S., Castellano, R. L. S., & Cattau, M. E. (2025). Mapping built infrastructure in semi-arid systems using data integration and open-source approaches for image classification. Remote Sensing Applications: Society and Environment, 37, 101472.
  12. Donald, D.A., 2012. Wavelet basis selection for spectroscopic data analysis (Doctoral dissertation, James Cook University). DOI: 25903/knk9-ne60.
  13. Feret, J.B., Asner, G.P., 2012. Tree species discrimination in tropical forests using airborne imaging spectroscopy. IEEE Trans. Geosci. Remote 51, 73–84.
  14. García-Rubio, G., Huntley, D. and Russell, P., 2015. Evaluating shoreline identification using optical satellite images. Marine Geology, 359, pp.96-105.
  15. Garg, A., Naidu, S.V., Gupta, S., Singh, D., Brodu, N. and Yahia, H., 2016, July. A novel approach for optimal weight factor of DT-CWT coefficients for land cover classification using MODIS data. In Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International. pp. 4528-4531. IEEE.
  16. Ghaffarian, S. and Ghaffarian, S., 2014. Automatic histogram-based fuzzy C-means clustering for remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 97, pp.46-57.
  17. He, C., Xing, J., Li, J., Yang, Q., & Wang, R. (2015). A new wavelet threshold determination method considering inter-scale correlation in signal denoising. Mathematical Problems in Engineering, 2015(1), 280251.
  18. Khoo, D. W. Y., Ng, Z. Q., & Seah, L. K. (2025). A Wavelet-based Filtering Algorithm for Enhancing Signal Processing in Coriolis Flow Meters. In EPJ Web of Conferences (Vol. 323, p. 08002). EDP Sciences.
  19. Klir, G.J., 2001. Foundations of fuzzy set theory and fuzzy logic: a historical overview. Int. J. Gen Syst. 2, 91–132.
  20. Kostanjšek, B., & Golobič, M. (2023). Cultural ecosystem services of landscape elements and their contribution to landscape identity: The case of Slovenia. Ecological Indicators, 157, 111224.
  21. Li, L., Xu, T., & Chen, Y. (2017). Fuzzy classification of high-resolution remote sensing scenes using visual attention features. Computational Intelligence and Neuroscience, 2017(1), 9858531.
  22. Lillesand, T.M. and Kiefer, R.W., 1994. Remote sensing and photo interpretation. John Wiley and Sons: New York, p.750.
  23. Longépé, N., Rakwatin, P., Isoguchi, O., Shimada, M., Uryu, Y. and Yulianto, K., 2011. Assessment of ALOS PALSAR 50 m orthorectified FBD data for regional land cover classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 49(6), pp.2135-2150.
  24. Longley, P.A., Barnsley, M.J., Donnay, J.-P., 2001. Remote sensing and urban analysis: a research agenda. In: Donnay, J.-P., Barnsley, M.J., Longley, P.A. (Eds.), Remote Sensing and Urban Analysis. Taylor and Francis, London, New York, pp. 243– 255.
  25. Lu, S., Ge, M., Zhang, J., Zhu, W., Li, G., & Gu, F. (2025, July). Filtering with time-frequency analysis: an adaptive and lightweight model for sequential recommender systems based on discrete wavelet transform. In International Conference on Intelligent Computing (pp. 161-173). Singapore: Springer Nature Singapore.
  26. Matlab: v7.9.0 (R2015b), 2015. Documentation, the MathWorks, Inc.
  27. Melgani, F. Al Hashemy, B.A.R. Taha, S.M.R. 2000. An explicit fuzzy supervised classification method for multispectral remote sensing images, IEEE Transactions on Geoscience and Remote Sensing 38 (1). 287–295.
  28. Muad, A.M., Foody, G.M., 2012. Super-resolution mapping of lakes from imagery with a coarse spatial and fine temporal resolution. Int. J. Appl. Earth Obs. 15, 79– 91.
  29. Murmu, S. and Biswas, S., 2015. Application of fuzzy logic and neural network in crop classification: a review. Aquatic Procedia, 4, pp.1203-1210.
  30. Mwita, E., Menz, G., Misana, S., Becker, M., Kisanga, D., Boehme, B., 2013. Mapping small wetlands of Kenya and Tanzania using remote sensing techniques. Int. J. Appl. Earth Obs. 21, 173–183.
  31. Nagendra, H., 2001. Using remote sensing to assess biodiversity. Int. J. Remote Sens. 22, 2377–2400.
  32. Ojwang, G.O., Ogutu, J.O., Said, M.Y., Ojwala, M.A., Kifugo, S.C., Verones, F., Graae, B.J., Buitenwerf, R. and Olff, H., (2024). An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems. Frontiers in Remote Sensing, 4, 1188635.
  33. Padhy, R., Dash, S. K., & Mishra, J. (2024). Joint feature selection and classification of low-resolution satellite images using the SAT-6 dataset. High-Confidence Computing, 100278.
  34. Petrou, Z.I., Kosmidou, V., Manakos, I., Stathaki, T., Adamo, M., Tarantino, C., Tomaselli, V., Blonda, P. and Petrou, M., 2014. A rule-based classification methodology to handle uncertainty in habitat mapping employing evidential reasoning and fuzzy logic. Pattern Recognition Letters, 48, pp.24-33.
  35. Rajesh, S., Arivazhagan, S., Moses, K.P. and Abisekaraj, R., 2012. Land cover/land use mapping using different wavelet packet transforms for LISS IV Madurai imagery. Journal of the Indian Society of Remote Sensing, 40(2), pp.313-324.
  36. Reyes, A., Solla, M. and Lorenzo, H., 2017. Comparison of different object-based classifications in Landsat TM images for the analysis of heterogeneous landscapes. Measurement, 97, pp.29-37.
  37. Rokach, L., Maimon, O., 2005. Clustering Methods, Data Mining and Knowledge Discovery Handbook. Springer, New York, pp. 321–352.
  38. Schmeller, D.S., 2008. European species and habitat monitoring: where are we now? Biodivers. Conserv. 17, 3321–3326.
  39. Sebari, I. and He, D.C., 2013. Automatic fuzzy object-based analysis of VHSR images for urban objects extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 79, pp.171-184.
  40. Shankar, B.U., Meher, S.K. and Ghosh, A., 2011. Wavelet-fuzzy hybridization: Feature-extraction and land-cover classification of remote sensing images. Applied Soft Computing, 11(3), pp.2999-3011.
  41. Sharma Banjade, S., Rai, N., & Subedi, B. (2023). Comparison of supervised classification algorithms using a hyperspectral image for land use/land cover classification. Environmental Sciences Proceedings, 29(1), 59.
  42. Tang, J. Wang, L. Myint, S.W. 2007. Improving urban classification through fuzzy supervised classification and spectral mixture analysis, International Journal of Remote Sensing 28 (18). 4047–4063.
  43. Tso, B., Mather, P.M. 2009. Classification Methods for Remotely Sensed Data, 2nd ed., CRC Press, Boca Raton, Florida.
  44. Turkoglu, I. and Avci, E., 2008. Comparison of wavelet-SVM and wavelet-adaptive network based fuzzy inference system for texture classification. Digital Signal Processing, 18(1), pp.15-24.
  45. Turner M., 1989. Landscape ecology: the effect of pattern on process, Annu. Rev. Ecol. Syst. 20. 171–197.
  46. Vermaak, H., Nsengiyumva, P., & Luwes, N. (2016). Using the dual‐tree complex wavelet transform for improved fabric defect detection. Journal of Sensors, 2016(1), 9794723.
  47. Vyas, D., Krishnayya, N.S.R., Manjunath, K.R., Ray, S.S., Panigrahy, S., 2011. Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation. Int. J. Appl. Earth Obs. 13, 228–235.
  48. Walker, W.S., Stickler, C.M., Kellndorfer, J.M., Kirsch, K.M., Nepstad, D.C., 2010. Large-area classification and mapping of forest and land cover in the Brazilian Amazon: a comparative analysis of ALOS/PALSAR and Landsat data sources. IEEE J. Sel. Top. Appl. 3, 594–604.
  49. Wang, F. 1990. Fuzzy supervised classification of remote sensing images, IEEE Transactions on Geoscience and Remote Sensing 28 (2). 194–201.
  50. Wang, Y., Jamshidi, M. 2004. Fuzzy logic applied in remote sensing image classification, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 6378–6382.
  51. Wang, Y., Liu, J., Wang, D., Liu, X., Cao, P., & Hua, K. (2024). Noise reduction method for mine wind speed sensor data based on CEEMDAN-wavelet threshold. Scientific Reports, 14(1), 24869.
  52. Wei, G., Xu, J., Yan, W., Chong, Q., Xing, H., & Ni, M. (2024). Dual-domain fusion network based on wavelet frequency decomposition and fuzzy spatial constraint for remote sensing image segmentation. Remote Sensing, 16(19), 3594.
  53. Wolf, I. D., Sobhani, P., & Esmaeilzadeh, H. (2023). Assessing changes in land use/land cover and ecological risk to conserve protected areas in urban–rural contexts. Land, 12(1), 231.
  54. Yu, J. Ekstrom, M. 2003. Multispectral image classification using wavelets: a simulation study, Pattern Recognition 36 (4). 889–898.
  55. Zhang, S. Xue, X. Zhang, X. 2005. Feature extraction and classification with wavelet transform and support vector machines, in: Proceedings IEEE International Geoscience and Remote Sensing Symposium, IGARSS ’05, vol. 6, pp. 3795–3798.
مهندسی اکوسیستم بیابان
دوره 15، شماره 50
بهار 1405
صفحه 51-64