Comparing the Performance of Fuzzy Operators in the Object-based Image Analysis and Support Vector Machine Kernel Functions for the Snow Cover Estimation in Alvand Mountain

Abstract Snow cover is a significant driver in many ecological, climatic, and hydrological processes regarding the mountainous regions and high-latitude areas. Researchers believe that remote-sensing data can provide better estimates of snow cover ranges in comparison with the traditional surveying methods. Therefore, the present study was conducted using Sentinel-2B satellite images to compare the performance of the support vector machine (SVM) kernel functions and object-oriented fuzzy operators in estimating the amount of snow cover in the Alvand Mountain. In this research, the data consists of four Sentinel-2B satellite bands at 10 m spatial resolution (B2, 3, 4 & 8), launched on March 6, 2020. In this research study, the linear, polynomial, radial, and sigmoid SVM kernel functions, as well as the object-oriented fuzzy operators (AND, OR, MGE, MAR, MGWE, and ALP) have been employed. The results indicated that among these algorithms, AND algorithm, which represents the logical commonality, included the lowest return fuzzy value of 98%; therefore, this algorithm seems to provide the overall highest accuracy. Based on these findings, in the digital image classification, the object-oriented processing method can make it possible to achieve the highest accuracy compared to the SVM kernel functions. The reason is that a wide range of information, such as texture, shape, position, content, and bandwidth is associated with the objects in this classification method.