دانشگاه کاشان مهندسی اکوسیستم بیابان 2538-6336 15 50 2026 05 22 A Comparison of Wavelet Feature-Based Minimum Distance and Rule-Based Fuzzy System for Classifying Medium-Resolution Images in Heterogeneous Landscapes A Comparison of Wavelet Feature-Based Minimum Distance and Rule-Based Fuzzy System for Classifying Medium-Resolution Images in Heterogeneous Landscapes 51 64 115447 ‎10.22052/deej.2026.258032.1129 FA الهه اکبری گروه سنجش از دور و GIS، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری مصطفی دستورانی گروه سنجش از دور و GIS، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران هادی سلیمانی مقدم گروه جغرافیا و برنامه ریزی شهری، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران Journal Article 2025 12 20 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%. 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%. https://deej.kashanu.ac.ir/article_115447_1ae8bfea4f5a5866cb0759371107c051.pdf