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Hyperspectral image feature extraction generally does not consider the optimisation of structure element types, resulting in the loss of spatial correlation information. To solve this issue, this paper proposes a novel adaptive classification method (MPDTNC-SVM), in which spatial information is extracted by Morphological Profile Filter (MPF) and Domain Transform Normalised Convolution Filter (DTNCF). First, MPF extracts the spatial features on multiple principal component analysis (PCA) components of the hyperspectral image, and DTNCF works over all spectral bands to extract spatially correlated features. The two spatial features are then combined and fed into Support Vector Machine (SVM). Second, a two-step optimisation is implemented in the classification process. Specifically, the best structure of MPF is chosen for classification. Next, the optimal parameters of the structural elements are obtained through iterative classification optimisation, with the best classification performance produced in the process. Experimental results of actual hyperspectral images show that the proposed MPF and DTNCF with SVM (MPDTNC-SVM) method is superior to other classification methods, including Edge-Preserving Filter and Recursive Filter method, morphological feature-based methods, and the SVM methods with raw spectral features, reduced-dimensional and spatial-spectral information. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel residual fusion classification method for hyperspectral image using spatial–spectral information, abbreviated as RFC-SS. The RFC-SS method first uses the Gabor texture features and the non-parametric weighted spectral features to describe the hyperspectral image from both aspects of spatial and spectral information. Then it applies the residual fusion method to save the useful information from different classification methods, which can greatly improve the classification performance. Finally, the test sample is assigned to the class that has the minimal fused residuals. The RFC-SS classification method is tested on two classical hyperspectral images (i.e. Indian Pines, Pavia University). The theoretical analysis and experimental results demonstrate that the RFC-SS classification method can achieve a better performance in terms of overall accuracy, average accuracy, and the Kappa coefficient when compared to the other classification methods. [ABSTRACT FROM PUBLISHER]