Hyperspectral image classification based on adaptive optimisation of morphological profile and spatial correlation information

Abstract

ABSTRACTHyperspectral 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.