Optimization-based automated unsupervised classification method: A novel approach

• We present two new unsupervised methods to classify remotely sensed data. • These methods perform classification process automatically. • The proposed methods allow to focus on specific classes in the image. • The performance of proposed method was compared with in four study areas. Unsupervised classification algorithms are methods for the analysis of remotely sensed images. Since these methods do not include a training phase, they require less time to apply and are more practical to use. Traditional unsupervised classification methods work with parameters given by the user, such as the number of classes, the stop criterion or the number of iterations of the algorithm. Determining the optimum values of these parameters to obtain successful classification result is a major problem. In this study, we propose two new methods, the weighted density based optimized classification method (DBOC-Weighted) and the automatic density based optimized classification method (DBOC-Automatic). Both work automatically without the need for parameters from the user, but the DBOC-Weighted only requires layer weights. These methods consist of data range expansion, useful data selection, segmentation and optimization stages, and perform the classification automatically. Both create new layers of data using remotely sensed images. After creating the initial classes based on density from all the data layers, the results are created by optimizing all classes in terms of quality indices. Four Sentinel 2 images are used to test the performance of the proposed methods. These images are selected from regions that have different geographical, climatic and vegetation properties. The results obtained are compared with the unsupervised classification methods frequently used in the literature. The accuracy analysis results show that the proposed classification algorithms produce satisfactory accuracy compared to the results of other algorithms. The results show that the proposed methods can be used successfully in the creation of expert and intelligent analysis systems, by eliminating user-induced error in the analysis of remotely sensed images. Thus, smart analysis tools can be created so that users from various professional disciplines can easily use them without being image processing specialists.