Learning Locality-Constrained Sparse Coding for Spectral Enhancement of Multispectral Imagery

Owing to easy acquisition and large coverage from the space, multispectral (MS) imaging has garnered growing interest in various applications of remote sensing. However, the limited spectral information of MS data, to a great extent, leads to difficulties in classifying the materials more accurately, particularly for those classes that have very similar visual appearances. To address this issue effectively, we attempt to enhance the spectral resolution of MS imagery, enabling the identification of materials at a more precise level by the means of richer spectral information. More specifically, we propose to learn locality-constrained sparse coding (LCSC) for short, on partially overlapped hyperspectral (HS)-MS pairs (i.e., dictionary). LCSC is capable of capturing neighboring relations well by enforcing the local constraint for each pixel. Such a strategy makes it possible to better reconstruct HS products from MS images and partially overlapped HS images. Reconstruction and unmixing are explored as potential applications to assess the performance of spectral enhancement. Extensive experiments are conducted on two HS-MS data sets in comparison with several state-of-the-art baselines, which demonstrate the effectiveness of the proposed LCSC algorithm in the task of spectral enhancement.