Hyperspectral imagery provides detailed spectral information that can be used for tree species discrimination. The aim of this study is to assess spectral–spatial complexity reduction techniques for tree species classification using an airborne prism experiment (APEX) hyperspectral image. The methodology comprised the following main steps: (1) preprocessing (removing noisy bands) and masking out non-forested areas; (2) applying dimensionality reduction techniques, namely, independent component analysis (ICA), principal component analysis (PCA), and minimum noise fraction transformation (MNF), and stacking the selected dimensionality-reduced (DR) components to create new data cubes; (3) super-pixel segmentation on the original image and on each of the dimensionality-reduced data cubes; (4) tree species classification using a random forest (RF) classifier; and (5) accuracy assessment. The results revealed that tree species classification using the APEX hyperspectral imagery and DR data cubes yielded good results (with an overall accuracy of 80% for the APEX imagery and an overall accuracy of more than 90% for the DR data cubes). Among the classification results of the DR data cubes, the ICA-transformed components performed best, followed by the MNF-transformed components and the PCA-transformed components. The best class performance (according to producer’s and user’s accuracy) belonged to Picea abies and Salix alba. The other classes (Populus x (hybrid), Alnus incana, Fraxinus excelsior, and Quercus robur) performed differently depending on the different DR data cubes used as the input to the RF classifier.
Comparison of Independent Component Analysis, Principal Component Analysis, and Minimum Noise Fraction Transformation for Tree Species Classification Using APEX Hyperspectral Imagery
