Spectral-Spatial classification of hyperspectral images using functional data analysis

In this letter, a spectral-spatial classification method using functional data analysis (FDA) is proposed. Since the efficacy of the FDA for hyperspectral image analysis instead of analysis inmultivariate analysis framework (MAF) was proved previously, we apply FDA to better extract spectral and spatial information for hyperspectral image classification. Therefore, in the FDA framework a support vector machine (SVM) classifier is used for hyperspectral image classification and a watershed segmentation algorithm is applied in order to extract spatial structures. Several approaches to figure a one-band gradient image, as an input to watershed transformation, are examined and investigated. As a result, the extracted segmentation map is used to improve the pixel-wise classification accuracy on which the classification and the segmentation results are combined together using majority vote approach. The efficiency of the proposed method is evaluated on two hyperspectral data sets. The experimental results show that the proposed spectral-spatial classification method provides better classification accuracies compared to some state-of-the-art spectral-spatial classification methods.