###### Abstract  
This article describes a comparison of the pixel-based classification methods used to distinguish ice from other land cover types. The article focuses on processing RGB imagery, as these are very easy to obtained. The imagery was taken using UAVs and has a very high spatial resolution. Classical classification methods (ISODATA and Maximum Likelihood) and more modern approaches (support vector machines, random forests, deep learning) have been compared for image data classifications. Input datasets were created from two distinct areas: The Pond Skrin and the Baroch Nature Reserve. The images were classified into two classes: ice and all other land cover types. The accuracy of each classification was verified using a Cohen's Kappa coefficient, with reference values obtained via manual surface identification. Deep learning and Maximum Likelihood were the best classifiers, with a classification accuracy of over 92% in the first area of interest. On average, the support vector machine was the best classifier for both areas of interest. A comparison of the selected methods, which were applied to highly detailed RGB images obtained with UAVs, demonstrates the potential of their utilization compared to imagery obtained using satellites or aerial technologies for remote sensing. imagery classification; RGB imagery data; UAV; supervised classification; unsupervised classification; Iso Cluster; Maximum Likelihood; random trees; support vector machine; deep learning; pixel-based classification +
Footnote †: journal: Medical  
of imagery data, see Figure 1. The combination of image classification, \"drone\", and RGB is less covered by the published works than the combination of deep learning with \"drone\" and RGB. The focus on particular classification methods is significantly lower, see Tables 1 and 2. The term \"drone\" stands for UAV, UAS, drone, RPAS, MAV, unmanned aerial vehicle, and unmanned aerial system. This low attention is another reason for research in this area. namely a very high spatial resolution in cm \(\times\) px\({}^{-1}\). Smaller UAVs equipped with an RGB camera can also be considered a low-cost data acquisition solution [10]. The sensor is one of the main parameters that affect the price and quality of images. When looking for relevant contributions in scientific databases, it is necessary to clarify the terminology [11; 12; 13] because many terms are used. UAV, or unmanned aerial vehicle, is the technical term, which is the most widely used term for these machines. A UAV is a correct designation only for the machine itself. An unmanned aircraft system (UAS) is used to describe the entire set. This designation is often used in Anglo-Saxon terms. The term drone can be found very often as well. It comes from the French designation. The term drone has gained tremendous popularity for naming machines among the public and in non-scientific contributions. The most formal and international term is remotely piloted aircraft system or RPAS [14]. With the development of technology and the downsizing of the machines themselves, the term micro aerial vehicle (MAV) can also be found. Therefore, it is necessary to consider these terms when searching for scientific contributions. Table 2 shows the frequency of occurrences of particular terms when searching in the scientific databases WOS and SCOPUS. Figure 2 shows that it is appropriate to focus on the terms UAV, UAS, drone, and unmanned aerial system when searching. Other terms are not used so often compared to the above-stated terms. All the terms are often used in a supplementary way. As mentioned earlier, image classification methods can be divided into two fundamental types of methods. The first type is an automatic image classification based on learning without a teacher or unsupervised classifications. The basis of this approach is the clustering method, i.e., it is based on the clustering of pixels based on the similarity of a given classification class.