Pansharpening under thin cloudy conditions is a practically significant yet rarely addressed task, challenged by simultaneous spatial resolution degradation and cloud-induced spectral distortions. Existing methods often address cloud removal and pansharpening sequentially, leading to cumulative errors and suboptimal performance due to the lack of joint degradation modeling. To address these challenges, we propose a Unified Pansharpening Model with Thin Cloud Removal (Pan-TCR), an end-to-end framework that integrates physical priors. Motivated by theoretical analysis in the frequency domain, we design a frequency-decoupled restoration (FDR) block that disentangles the restoration of multispectral image (MSI) features into amplitude and phase components, each guided by complementary degradation-robust prompts: the near-infrared (NIR) band amplitude for cloud-resilient restoration, and the panchromatic (PAN) phase for high-resolution structural enhancement. To ensure coherence between the two components, we further introduce an interactive inter-frequency consistency (IFC) module, enabling cross-modal refinement that enforces consistency and robustness across frequency cues. Furthermore, we introduce the first real-world thin-cloud contaminated pansharpening dataset (PanTCR-GF2), comprising paired clean and cloudy PAN-MSI images, to enable robust benchmarking under realistic conditions. Extensive experiments on real-world and synthetic datasets demonstrate the superiority and robustness of Pan-TCR, establishing a new benchmark for pansharpening under realistic atmospheric degradations.

Fusion-based spectral super-resolution aims to yield a high-resolution hyperspectral image (HR-HSI) by integrating the available high-resolution multispectral image (HR-MSI) with the corresponding low-resolution hyperspectral image (LR-HSI). With the prosperity of deep convolutional neural networks, plentiful fusion methods have made breakthroughs in reconstruction performance promotions. Nevertheless, due to inadequate and improper utilization of cross-modality information, the most current state-of-the-art (SOTA) fusion-based methods cannot produce very satisfactory recovery quality and only yield desired results with a small upsampling scale, thus affecting the practical applications. In this article, we propose a novel progressive spatial information-guided deep aggregation convolutional neural network (SIGnet) for enhancing the performance of hyperspectral image (HSI) spectral super-resolution (SSR), which is decorated through several dense residual channel affinity learning (DRCA) blocks cooperating with a spatial-guided propagation (SGP) module as the backbone. Specifically, the DRCA block consists of an encoding part and a decoding part connected by a channel affinity propagation (CAP) module and several cross-layer skip connections. In detail, the CAP module is customized by exploiting the channel affinity matrix to model correlations among channels of the feature maps for aggregating the channel-wise interdependencies of the middle layers, thereby further boosting the reconstruction accuracy. Additionally, to efficiently utilize the two cross-modality information, we developed an innovative SGP module equipped with a simulation of the degradation part and a deformable adaptive fusion part, which is capable of refining the coarse HSI feature maps at pixel-level progressively. Extensive experimental results demonstrate the superiority of our proposed SIGnet over several SOTA fusion-based algorithms.

Currently, leading methods for spectral super-resolution (SSR) depend heavily on constructing diverse network architectures in a heuristic manner, in order to learn a full mapping from the RGB image to its corresponding hyperspectral image (HSI). Despite promising results in reconstruction performance, significant challenges remain with respect to model interpretation and the capture of long-range dependencies. In response to these issues, based on a comprehensive exploration of the physical imaging mechanism between spectral response curve (SRC) and HSI, we have developed a novel model-driven degradation-aware unfolding network (DAUNet) in an iterative way. Besides, the learning process is explicitly integrated with the intrinsic generation mechanism of the SSR task. To be specific, we unfold each step into a degradation-aware gradient decent (DAGD) module and a proximal mapping module (PMM), using the framework of maximum a posteriori (MAP) theory. Additionally, to introduce more discriminative learning capabilities to our network, we have further enhanced the PMM architecture by incorporating a fine-grained multihead spectral-wise transformer (FMST) block, which improves global feature representation compared to the channel-wise transformer block. Extensive experiments over several spectral datasets finely demonstrate the superior performance of our method beyond the current representative state-of-the-art (SOTA) SSR methods.

Spectral super-resolution (SSR), referring to the recovery of a reasonable hyperspectral image (HSI) from a single RGB image, has achieved satisfactory performance as part of the continued development of a convolutional neural network (CNN) in remote sensing image processing. However, the majority of existing algorithms focus on the pursuit of networks with deeper or broader architecture. Such algorithms have a poor channel or band feature extraction and fusing performance, and fail to fully leverage the input RGB images. To overcome these issues, we present a novel hybrid attentional CNN with structure information consistency (HASIC-net) that uses a two-pathway architecture. Specifically, both sides are stacked with several 2-D residual groups (2-DRGs) and residual groups (1-DRGs) equipped with channel or band attention (BA) modules, which mainly focuses on extracting channel statistics and bandwise features, respectively, by a parallel pooling architecture. We introduce several transversal connections from 2-DRG to 1-DRG to realize the interaction of information flow between both sides. In addition, we take the structure information of both RGB images and HSI into consideration and devise a structure information consistency (SIC) module to merge the structure tensor prior to the RGB images with the input of each 2-DRG. We then combine spectral gradient constraint loss with mean relative absolute error as a novel loss function to further restrain the spectral distortion and smooth the reconstructed spectral response curves. Experimental results on four benchmark datasets (i.e., NTIRE 2020, NTIRE 2018, CAVE, and Harvard) demonstrate that our proposed HASIC-net achieves state-of-the-art performance.