  In remote sensing, hyperspectral (HS) and multispectral (MS) image fusion
have emerged as a synthesis tool to improve the data set resolution. However,
conventional image fusion methods typically degrade the performance of the land
cover classification. In this paper, a feature fusion method from HS and MS
images for pixel-based classification is proposed. More precisely, the proposed
method first extracts spatial features from the MS image using morphological
profiles. Then, the feature fusion model assumes that both the extracted
morphological profiles and the HS image can be described as a feature matrix
lying in different subspaces. An algorithm based on combining alternating
optimization (AO) and the alternating direction method of multipliers (ADMM) is
developed to solve efficiently the feature fusion problem. Finally, extensive
simulations were run to evaluate the performance of the proposed feature fusion
approach for two data sets. In general, the proposed approach exhibits a
competitive performance compared to other feature extraction methods.
