## 2 State of Art  
The authors of one article [34] used a different approach to image classification. This work's advantage is that image classification methods were used for RGB data from a UAV. The Iso Cluster and Maximum Likelihood methods are older but widely used methods and sometimes provide good results. Images were classified into three classes in two time records. In this article, a 46% accuracy of unsupervised classification (also the worst) and 92% accuracy of supervised classification (also the best) were achieved.  
The article [18] compares selected supervised classification methods to classify fluvial scenes. Data were in RGB spectra from the existing dataset. The dataset was formed by 11 rivers worldwide (Japan, Italy, Canada, the UK, and Costa Rica) in the RGB images. In the first phase, they compare Maximum Likelihood, random forest, and a multilayer perceptron with a 70-80% F1 result. In the second phase, they use the CSC method and obtain a 92-95% F1 result. This demonstrates the suitability of deep learning or methods based on deep learning.  
The study [35] introduces a technique to use UAV-acquired RGB images coupled with ground information for a reliable and fast estimation of sugarcane yield for two popular varieties in Thailand. This article is interested in two first approaches (OBIA and ExG). OBIA provided better results with 92 and 96% accuracy. The pixel-based method provided 84 and 88% accuracy. The utilization of the UAV with a standard camera with an RGB sensor and the OBIA method are the contributions of this study. The OBIA method was identified as a good classifier for heterogeneous data.  
The study's primary goal [36] was to measure the accuracy of the selected methods (SVM, ML) to assess historical, current, and future land use and land cover patterns. The data were based on satellite imagery (river Birupa), and the methods were compared. The data were obtained from Landsat with a 30 m spatial resolution. From the results, it is possible to observe that both classifiers in some land covers (build-up area and fallow land) have identical accuracy. Furthermore, you can see in other land cover types that the results are very different. According to Kappa statistics, the SVM is a better classifier with an 86% average accuracy.  
This article [37] describes an automatic method of identifying soil and water conservation measures from the centimetre-resolution imagery of UAVs. The authors chose an object-based image analysis (OBIA) approach, machine learning models, and a support vector machine (SVM). Data were obtained from a UAV with a low-cost RGB camera. The images from a UAV provide have very high spatial resolution in cm*px-1. After obtaining the data, they used vegetation indices as the first step. They obtained 91% accuracy.  
The article [38] classifies sea ice from high-resolution observations. Data were collected during OIB Arctic summer campaigns with the nominal flight altitude. They obtained RGB images from a Canon EOS 5D camera with a high spatial resolution of 10 cm*px-1. They are classified into selected classes by histograms. This article focused on classifying ice and water in the RGB spectre.  
The article [39] investigates the potential of SAR imagery for sea ice classification. This article classified the imagery into two approaches (pixel-based and region-based). The classification accuracy of ice ranges from 80 to 90%, depending on the set of classification classes used.  
The classification of sea ice images was the aim of the authors [40]. In this article, hyperspectral data obtained from satellites were used. As a classifier, deep learning was used, specifically, 3D-CNNs. They reach very high accuracy, around 98%.  
The authors of another study [41] focused on differentiating snow and rock in colour imagery (RGB). They used unsupervised and supervised approaches. The Maximum Likelihood classification and a new approach, Polynomial Thresholding were used as supervised methods. Both supervised approaches reach an accuracy of around 95%.