Spectral-Spatial Feature Transformations With Controlling Contextual Information Through Smoothing Filtering and Morphological Analysis

A fusion method for spectral-spatial classification of hyperspectral images is proposed in this paper. In the proposed framework, at first, the dimension of hyperspectral image is reduced by several state-of-the-art spectral feature extraction methods, i.e., Binary Coding Based Feature Extraction (BCFE), Clustering Based Feature Extraction (CBFE), Feature Extraction Based on Ridge Regression (FERR), Feature Extraction Using Attraction Points (FEUAP), Feature Extraction using Weighted Training samples (FEWT), and Feature Space Discriminant Analysis (FSDA). Then, the spatial features are calculated from the spectral features extracted from each spectral feature extraction method individually using the proposed smoothing filters and morphological operators. Finally, majority voting decision rule is used to obtain the final classification map. The proposed framework, in addition to removing the useless spatial information such as noise and distortions, adds useful spatial information such as shape and size of objects presented in scene image. The use of complement information obtained from six spectral feature extraction methods with different ideas for class discrimination, significantly improves the classification results. The proposed framework provides in average 6.64%, 7.07%, 8.23%, 7.52% and 20.52% improvement in classification results of three real hyperspectral images compared to generalized composite kernel (GCK), multiple feature learning (MFL), weighted joint collaborative representation (WJCR), original hyperspectral bands stacked on extended morphological profile (HS+EMP) and original hyperspectral bands (HS), respectively in terms of overall accuracy.