A spatial-spectral SIFT for hyperspectral image matching and classification

Abstract

The scale-invariant feature transform (SIFT) is known as one of the most robust local invariant feature and is widely applied to image matching and classification. However, There is few studies on SIFT for hyperspectral image (HSI). Hyperspectral image (HSI) embraces the spectral information reflecting the material radiation property and the geometrical relationship of the objects. Thus, HSI provides much more information than gray and color image. Therefore, this paper puts forward a spatial-spectral SIFT for HSI matching and classification by using the geometric algebra as its mathematic tool. It extracts and describes the spatial-spectral SIFT feature in the spatial-spectral domain to exploit both the spectral and spatial information of HSI. Firstly, a spatial-spectral unified model of spectral value and gradient change (UMSGC for short) is built to analyze spectral and spatial information for HSI synthetically. Secondly, the scale space for HSI based on UMSGC is designed. Finally, both the new detector and descriptor of the spatial-spectral SIFT for HSI that comprehensively consider spectral and spatial information are proposed. The experimental results show that the proposed algorithm demonstrates excellent performance in HSI matching and classification.