期刊
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
卷 7, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/ijgi7120488
关键词
independent component analysis (ICA); minimum noise fraction transformation (MNF); principal component analysis (PCA); APEX hyperspectral imagery; dimensionality reduction; tree species classification; random forest (RF); super-pixel segmentation
资金
- European Community [263479]
- Austrian Science Fund (FWF) through the Doctoral College GIScience [DK W1237-N23]
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.
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