A Hyperspectral Image NSST-HMF Model and Its Application in HS-Pansharpening

The high spectral resolution of hyperspectral (HS) images provides the possibility of omnidirectional feature identification of objects. However, the high-dimensional features and the high redundancy information properties make data processing and the application of HS images extremely challenging. Thus, effectively expressing and correlating the intrinsic correlations of HS images by establishing a statistical model is of great significance. This article proposes a nonsubsampled shearlet transform hidden Markov forest (NSST-HMF) model. This new approach has three key characteristics: 1) the statistical properties of the NSST coefficients are studied in the spatial and spectral directions, respectively, and the “clustering” and “aggregation” properties are observed in both directions; 2) the HMF structure is proposed to depict the multidimensional collaborative correlation of the HS image NSST coefficients, and the proposed method considers the multidirectional transfer relationships among the Markov structure of HS images NSST coefficient for the first time, which significantly improves the prediction ability of the model; and 3) a novel HS-pansharpening approach based on the NSST-HMF model and amplitude modulation of large state probability in the high-frequency subband direction region is proposed. Experimental results show that our method can efficiently improve the spatial resolution of HS images while simultaneously preserving their spectral features. The HMF structure is first proposed in this article, which provides a way to depict the collaborative correlation of multichannel images.