Fusion-based hyperspectral image (HSI) super-resolution has garnered growing attention in recent years due to its strong capability in reconstructing high spatial resolution (HR) hyperspectral images. However, most existing methods either rely on a supervised training strategy or require pairwise images which are difficult to acquire. To address these issues, we propose an unsupervised and unpaired fusion-based super-resolution network, called U2SRnet, which achieves super-resolution without the aid of any supervision nor pairwise images. Instead, an arbitrary low spatial resolution (LR) HSI is used as a fixed hyperspectral input to provide reflectance information for the spectral reconstruction of RGB images. U2SRnet consists of two modules, which are spectral correction module (SCM) and band generation module (BGM) respectively. The former aims to enforce material reflectance consistency between unpaired HS-RGB images, which is implemented via a joint perception attention module (JPAM). Considering the spatial inconsistency of material positions between HS and RGB images, we propose an elaborately-designed material matching mechanism in BGM to obtain HS-RGB block pairs with high material-semantic similarity. Furthermore, a differential migration mechanism guided by physical reflectance priors, universally applicable to unpaired images, is introduced to migrate the material reflectance properties between these block pairs. Extensive experiments were performed on Cave, Harvard and TG1HRSSC datasets. Experimental results validated the effectiveness and superiority of the proposed method.

High-dimensional mediation analysis is often associated with a multiple testing problem for detecting significant mediators. Assessing the uncertainty of this detecting process via false discovery rate (FDR) has garnered great interest. To control the FDR in multiple testing, two essential steps are involved: ranking and selection. Existing approaches either construct p-values without calibration or disregard the joint information across tests, leading to conservation in FDR control or non-optimal ranking rules for multiple hypotheses. In this paper, we develop an adaptive mediation detection procedure (referred to as "AMDP") to identify relevant mediators while asymptotically controlling the FDR in high-dimensional mediation analysis. AMDP produces the optimal rule for ranking hypotheses and proposes a data-driven strategy to determine the threshold for mediator selection. This novel method captures information from the proportions of composite null hypotheses and the distribution of p-values, which turns the high dimensionality into an advantage instead of a limitation. The numerical studies on synthetic and real data sets illustrate the performances of AMDP compared with existing approaches.

Endmember is defined as the spectral signature of pure material present in hyperspectral imagery. Estimation of the number of endmembers (NOE) present in a scene is an important preprocessing step and plays a crucial role in hyperspectral image processing, since over- or under-estimation of the NOE will lead to heavily incorrect results. In this article, we develop a thresholding ridge ratio (TRR) criterion based on eigendecomposition for NOE determination. Different from the widely used eigenvalue difference analysis methods, the TRR seeks an adaptive thresholding operation to the ridge ratio of eigenvalue differences, and ridge ratio combined with adaptive thresholding can theoretically guarantee a consistent estimate even when there are several local minima. Based on the TRR criterion, an algorithm is introduced to perform the estimation of NOE. Experimental results on both the simulated and real hyperspectral data sets have demonstrated that the proposed TRR-based algorithm has comparable and even better performances to several benchmark algorithms in the estimation accuracy of the NOE.