Contribution to Visualization of Hyperspectral Data Using Self-Organizing Maps

The main goal of the paper was to present the importance of hyperspectral data visualization. Hyperspectral data are represented as objects in a multidimensional space. A data analyst working with such data has no intuitive understanding of their distribution or the topological proximity of individual classes and corresponding objects. The expected outcome of visualization is to enhance data comprehension for further processing, such as classification. In our study, we focused on dimensionality reduction and data visualization using Self-Organizing Maps. Class overlaps were identified, which was confirmed by evaluating the classification accuracy of the applied convolutional neural network.