Multiscale spectral-spatial classification for hyperspectral imagery

In this paper, we explore hyperspectral classification using multiscale features. To reduce data dimensionality, principal component analysis (PCA) is applied to the original image. Then a multiscale transform technique (e.g., wavelet transform, contourlet transform, etc.) is applied to each of principal components (PCs). The resulting transform coefficients can be used as spatial features. In particular, local spatial neighbors are considered to generate smoother coefficients. Combining such spatial features with spectral features (e.g., PCs), improved performance can be achieved for hyperspectral classification. In this paper, several multiscale spatial features are also evaluated.