Evaluation and comparison of dimensionality reduction methods and band selection

For dimensionality reduction (DR) of a hyperspectral data cube or band selection, it is desirable to have one method that is suitable for all remote sensing applications. However, in reality this is not possible. A specific remote sensing application requires a specific DR or band selection method that best suits it. In this paper, the evaluation and comparison of three DR methods-namely, principal component analysis (PCA), wavelet, and minimum noise fraction (MNF)-and one band selection method were conducted. Based on the experiments, the following was observed. For endmember extraction, the PCA DR, wavelet DR, and band selection found all five endmembers. However, the MNF DR missed one endmember. For mineral detection, the MNF DR produced a map that is closest to the true map when compared with the other DR methods and band selection method. For classification, the PCA DR produced the highest classification rates whereas the other methods yielded less classification rates.