Hybrid Feature Extraction for Object-based Hyperspectral Image Classification

The purpose of feature extraction is to reduce the dimensionality of hyperspectral images to solve classification problems caused by limited training samples. In this study, a hybrid feature extraction method which integrates spectral features and spatial features simultaneously is proposed. Firstly, the spectral-feature images are calculated along the spectral dimension of hyperspectral images using wavelet decomposition because wavelet has been proven effective in extracting spectral features.
Secondly, ten different kinds of spatial-features, which are calculated along the two spatial dimensions of hyperspectral images, are implemented on the wavelet spectral-feature images. Then a feature selection method based on the optimization of class separability is performed on the extracted spectral-spatial features to get the hybrid features which could be suitable for classification applications. In this study, the object-based image analysis (OBIA) is used for hyperspectral image classification. The experiment results showed that the overall accuracy for the classification of a real hyperspectral data set using our proposed approach could reach approximately 94%. Moreover, it is worth mentioning that the hybrid features and OBIA classification could significantly rise the overall accuracy of hyperspectral images which contain poor separability between classes, after the spectral features were extracted. The experiment result also showed that the overall accuracy would go up by 20% by using our proposed approach on hyperspectral images with poor class separability.