Unsupervised and Unregistered Hyperspectral Image Super-Resolution With Mutual Dirichlet-Net
Ying Qu, Hairong Qi, Chiman Kwan, Naoto Yokoya, Jocelyn Chanussot
Abstract
Hyperspectral images (HSIs) provide rich spectral information that has contributed to the successful performance improvement of numerous computer vision and remote sensing tasks. However, it can only be achieved at the expense of images’ spatial resolution. HSI super-resolution (HSI-SR), thus, addresses this problem by fusing low-resolution (LR) HSI with the multispectral image (MSI) carrying much higher spatial resolution (HR). Existing HSI-SR approaches require the LR HSI and HR MSI to be well registered, and the reconstruction accuracy of the HR HSI relies heavily on the registration accuracy of different modalities. In this article, we propose an unregistered and unsupervised mutual Dirichlet-Net ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$u^{2}$ </tex-math></inline-formula> -MDN) to exploit the uncharted problem domain of HSI-SR <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without the requirement of multimodality registration</i> . The success of this endeavor would largely facilitate the deployment of HSI-SR since registration requirement is difficult to satisfy in real-world sensing devices. The novelty of this work is threefold. First, to stabilize the fusion procedure of two unregistered modalities, the network is designed to extract spatial information and spectral information of two modalities with different dimensions through a shared encoder–decoder structure. Second, the mutual information (MI) is further adopted to capture the nonlinear statistical dependencies between the representations from two modalities (carrying spatial information) and their raw inputs. By maximizing the MI, spatial correlations between different modalities can be well characterized to further reduce the spectral distortion. We assume that the representations follow a similar Dirichlet distribution for their inherent sum-to-one and nonnegative properties. Third, a collaborative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula> -norm is employed as the reconstruction error instead of the more common <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2}$ </tex-math></inline-formula> -norm to better preserve the spectral information. Extensive experimental results demonstrate the superior performance of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$u^{2}$ </tex-math></inline-formula> -MDN as compared to the state of the art.