Integration of LIME with segmentation techniques for SVM classification in Sentinel-2 imagery - Yilmaz, Elif Ozlem; Uzun, Yakup Kaan; Berkan, Emre; Kavzoglu, Taskin (Advances in Geodesy and Geoinformation, 2025)
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.