Multiband Image Fusion via Regularization on a Riemannian Submanifold

Abstract

Multiband image fusion aims to generate high spatial resolution hyperspectral images by combining hyperspectral, multispectral or panchromatic images. However, fusing multiband images remains a challenge due to the identifiability and tracking of the underlying subspace across varying modalities and resolutions. In this paper, an efficient multiband image fusion model is proposed to investigate the latent structures and intrinsic physical properties of a multiband image, which is characterized by the Riemannian submanifold regularization method, nonnegativity and sum-to-one constraints. An alternating minimization scheme is proposed to recover the latent structures of the subspace via the manifold alternating direction method of multipliers (MADMM). The subproblem with Riemannian submanifold regularization is tackled by the projected Riemannian trust-region method with guaranteed convergence. The effectiveness of the proposed method is demonstrated on two multiband image fusion problems: (1) hyperspectral and panchromatic image fusion and (2) hyperspectral, multispectral and panchromatic image fusion. The experimental results confirm that our method demonstrates superior fusion performance with respect to competitive state-of-the-art fusion methods.

Hyperspectral Image Mixed Noise Removal via Double Factor Total Variation Nonlocal Low-Rank Tensor Regularization

“…The R mainly characterizes the LR of the HSI, whose sensitivity analysis is presented in Figure 12. When the R is between [12,34], the results exhibit a stable and superior performance for various datasets and noise cases. Considering a larger R leads to a higher calculation cost; so, the R should be set 12 or estimated by the HySime.…”

“…[8,9]. The existence of the above noises greatly degrades the quality of the HSI, limiting the subsequent tasks, such as classification [10], unmixing [11], fusion [12], feature learning [13], super-resolution [14], and target detection [15]. Hence, HSI denoising is a fundamental preprocessing step for further applications.…”