An Efficient Satellite Images Classification Approach Based on Fuzzy Cognitive Map Integration With Deep Learning Models Using Improved Loss Function

Classification applications in order to obtain the desired information from satellite images are one of the increasing areas of study. Remote sensing satellite images are very difficult to obtain, but nevertheless, they can be used in many different areas. In this context, satellite images have become an important data source for land use and land cover analysis. In this study, the public EuroSAT dataset, which is compatible with land cover and land use classification, has been used. There are a total of 27000 images in this dataset, with ten different class labels, such as industry, forest, and lake, and approximately 3000 images in each. In order to perform a more precise and efficient classification study, the images have first been subjected to data augmentation using various techniques and have been divided into a training, test, and validation set. In terms of classification, six different convolutional neural network (CNN) architectures and two different vision transformer architectures have been used. Three experiments have been carried out for the proposed system, and the selected models have been trained with three different loss functions, and the results have been evaluated. The first of the loss functions used is cross-entropy, which is one of the most well-known and basic functions; the second is label smoothing cross-entropy; and the third is a new custom loss function that has been improved and consists of a special combination of both loss functions. The eight proposed models have been trained for all three loss functions; performance evaluations have been made by analyzing metrics such as accuracy, precision, and recall; and comparisons have been realized. After the proposed models have been evaluated in terms of these metrics, a Fuzzy Cognitive Map (FCM) has been created to integrate the model outputs to obtain the final classification result. For this construction, the accuracy of each model has been used in the neighborhood matrix of FCM. The accuracy of the proposed FCM method has been calculated as 99.97%. When the results of the proposed different models have been examined, it has been clearly seen that the new loss function improved in this study has been more successful than the other two loss functions in all models. In addition, the simulation results of FCM-based integration indicate that our approach has very high accuracy.