Evaluate the efficiency of kernel functions of vector support machine and object-oriented fuzzy operators in estimating the level of snow cover using Sentinel-2B satellite data - Case study: Almabolagh Mountain

The snow cover is one of the quickest changing phenomena on earth that considerably affects the climate, amount of radiation, the balance of energy between atmosphere and earth, hydrology cycle and also, biogeochemical as well as human activities. Precise estimate of snow cover is regarded as one of the fundamental operations in precipitation. Thus, monitoring the snow-covered surfaces is of specific importance from the perspective of climatic, ecologic and hydrologic studies. Researchers believe that remote sensing data can lead to assess the snow-covered areas better than traditional topography methods. Therefore, nowadays, in efficient management of water resources, applying remote sensing data aims to achieve exact information on snow-covered areas operationally. Satellites are suitable tools to measure the mentioned areas since high snow reflection creates a good contrast with other natural surfaces except clouds. This research is conducted to compare the performance of Cornell functions of support vector and object-oriented Fuzzy operators in estimating the desired areas in Almabolaq Mountain, Asadabad.

The data used in this research are the bands with 10 m spatial segregation of 2B Sentinel satellite including bands 2, 3, 4 and 8 on 6th March 2020. To classify Cornell functions of support vector machine and compute their accuracy, ENVI software was implemented. The eCognation software was used to partition and categorize those with the same object-oriented Fuzzy operators. Separating similar spectral sets and classifying those with the same spectral behaviour are regarded as satellite information classification. In other words, categorizing the photo pixels, and allocating one pixel to one class or phenomenon are the mentioned classification. Support vector machine is one of the most common classifiers in learning machine, which divides data using an optimum separation super plate. One of the important advantages of support vector machine is the ability to deal with high dimensional data using almost less training samples for remote sensing applications. Objective analysis is an advanced technique of image processing which is used to assess the digital images and typical conflicts of basic pixel classification based on different methods. Traditionally, pixel-based analysis is done by available data of each pixel whereas object-based analysis considers a set of similar pixels called objects or image objects. It regards adjacent pixels with the same information value as one distinct unit called piece or segment. In fact, pieces are the areas produced by one or few homogeneous criteria in one or few dimensions of a specific space so that the pieces have extra spectral information in each band, mean, maximum and minimum amounts, variance, etc. as compared to single pixels. Combined object-oriented and Fuzzy methods provide the classification of image pieces with a specific membership degree. In this process, image pieces with different membership degrees are classified in more than one class and according to the membership degree, image piece classification is done leading to the increased final precision.

In the research, after preparing satellite images in SNAP software using Sen2Cor, radiometric correction was conducted on the images. To prepare the classification map of Cornell functions of support vector machine, TIFF satellite images were called by ENVI software. Using the shape file of the case study, the area cutting operation was done. Afterwards, two classes of snow and non-snow regions were created to pick up the training points and based on imagery processing, training points were specified for each class. To classify support vector machine algorithm, linear, polynomial, radial and sigmoid Cornell functions were applied and classification maps were separately produced. To draw the classification map of object-oriented Fuzzy operators, satellite images pre-processed in previous stages were called by eCognation software and then they were defined as a project. Afterwards, two mentioned classes were defined to do the classification process and for each class, the desired Fuzzy operator was determined. For suitable classification, it was done in various scales and weight coefficients of shape and compactness. Scale 75, shape 6.0 and compactness 8.0 presented suitable classification. After selecting the training samples, parameters of lighting, mean and standard deviations were chosen as distinct features of classes for object-oriented classification. Using the nearest adjacent neighbor algorithm, object-oriented classification was done for each of the Fuzzy operators. After drawing the snow-covered areas through Cornell functions of support vector machine and object-oriented fuzzy operators, the accuracy of classification was computed.

The results indicate that AND algorithm showing the logic share and minimum return value out of Fuzzy values is of the highest accuracy (98%) and to classify digital images, the object-oriented processing methods of satellite imagery enable more precision due to the data related to texture, shape, position, content and geometrical features as compared to Cornell functions of support vector machine.