Hyperspectral Image Classification Based On 3d Sharpened Cosine Similarity Operation

Hyperspectral remote sensing images contain abundant spectral information and have a broad application in various areas, such as precision agriculture and geological exploration. However, due to the existence of many bands and the correlation between these bands, it is easy to cause the curse of dimensionality. Therefore, how to efficiently extract spectral and spatial features should be addressed. In this paper, a novel and efficient sharpened cosine similarity operation is applied in hyperspectral images classification to enhance the ability of feature extraction. To validate the efficiency of the proposed method, experiments are conducted on a real hyperspectral dataset, e.g., the University of Pavia (UP). Quantitative and qualitative results demonstrate that sharpened cosine distance operation can efficiently extract discriminative features and achieve a better classification performance.