An efficient framework for spectral-spatial classification of hyperspectral images in urban areas

Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. Recently, an effective approach for spectral-spatial classification has been proposed using Minimum Spanning Forest (MSF) grown from automatically selected markers. This paper aims at improving this approach for classification of hyperspectral images in urban areas. The proposed framework is based on deriving the optimal spatial and spectral features of the original hyperspectral image using the Principal Component Analysis (PCA). The spatial features extracted in this study are wavelet, Gabor filter, mean, contrast, entropy, variance, homogeneity, dissimilarity, second moment, and correlation. The experimental results on three hyperspectral datasets demonstrate that compared to the original MSF-based approach, the proposed framework yields more accurate classification maps.