Comparison of Different Dimension Reduction Methods in Classification of Hyperspectral Images

Abstract

In remote sensing, which is becoming increasingly important today, researchers use high-dimensional data representing the surface of the earth to find relationships between various spectral signatures. In particular, images can consist of hundreds of high-resolution bands that reflect the properties of different materials. However, the presence of a large number of different bands in high-dimensional space can make interpretation of these features difficult. Various difficulties are encountered due to dimensionality problem for pre-processing of remote sensing data. Research in this area reveals that this is a difficult problem and not a single solution to all problems. However, recent studies show that manifold learning techniques are a very important solution in the preprocessing of hyperspectral images. In this study, the performance of the state-of-the-art manifold embedding methods on hyperspectral data is analyzed comparatively. The dimension reduction application of each method has been carried out by using two different data sets that are used most in this field and their performance have been verified by the nearest neighbor (1NN) classification. Even though there are class-based differences in the classification of hyperspectral data, it is seen that manifold embedding methods, which are compared according to the obtained results, yield successful results. In addition, the runtime of each method is presented graphically and explained along with the reasons for which method works faster.