Real-world remote sensing image super-resolution via a practical degradation model and a kernel-aware network

Super-resolution is an essential task in remote sensing. It can enhance low-resolution remote sensing images and benefit downstream tasks such as building extraction and small object detection. However, existing remote sensing image super-resolution methods may fail in many real-world scenarios because they are trained on synthetic data generated by a single degradation model or on a limited amount of real data collected from specific satellites. To achieve super-resolution of real-world remote sensing images with different qualities in a unified framework, we propose a practical degradation model and a kernel-aware network (KANet). The proposed degradation model includes blur kernels estimated from real images and blur kernels generated from pre-defined distributions, which improves the diversity of training data and covers more real-world scenarios. The proposed KANet consists of a kernel prediction subnetwork and a kernel-aware super-resolution subnetwork. The former estimates the blur kernel of each image, making it possible to cope with real images of different qualities in an adaptive way. The latter iteratively solves two subproblems, degradation and high-frequency recovery, based on unfolding optimization. Furthermore, we propose a kernel-aware layer to adaptively integrate the predicted blur kernel into super-resolution process. The proposed KANet achieves state-of-the-art performance for real-world image super-resolution and outperforms the competing methods by 0.2–0.8 dB in the peak signal-to-noise ratio (PSNR). Extensive experiments on both synthetic and real-world images demonstrate that our approach is of high practicability and can be readily applied to high-resolution remote sensing applications

Can we use deep learning models to identify the functionality of plastics from space?

The function of plastics is an important issue, especially since it determines whether or not they can be recycled. This study presents a two-stage workflow to identify the functions of plastic materials on land surfaces using a deep learning model trained with Sentinel-2 satellite images. First, a classification map identifying 10 distinct plastic types was obtained by evaluating spaceborne hyperspectral PRISMA data. Then, different deep learning algorithms were used to assign functions to the initially classified plastic targets based on the RGB information extracted from Sentinel-2 satellite images. A total of 1,645 plastic polygons were manually labeled on RGB images of Sentinel-2, and the following five main function types were identified: plastic cover sheeting for construction areas, greenhouse structures, photovoltaic panels (PVs), roof materials, and sport field floorings. By comparing three state-of-the-art deep learning models, including GoogLeNet, VGGNet, and ResNet, an overall accuracy of 78% was achieved on the test dataset using the VGG-13 network. The model performed well in identifying PVs, greenhouses, and construction sites, with F1 scores of 0.85, 0.77, and 0.71 respectively. The performance of the model in identifying roofs and sport field floorings was lower, with respective F1 scores of 0.57 and 0.59. Overall, the results show that the proposed workflow using deep learning algorithms trained on Sentinel-2 images has a great potential to identify the function of plastic materials on land surfaces

Formaldehyde and De/Methylation in Age-Related Cognitive Impairment

Formaldehyde (FA) is a highly reactive substance that is ubiquitous in the environment and is usually considered as a pollutant. In the human body, FA is a product of various metabolic pathways and participates in one-carbon cycle, which provides carbon for the synthesis and modification of bio-compounds, such as DNA, RNA, and amino acids. Endogenous FA plays a role in epigenetic regulation, especially in the methylation and demethylation of DNA, histones, and RNA. Recently, epigenetic alterations associated with FA dysmetabolism have been considered as one of the important features in age-related cognitive impairment (ARCI), suggesting the potential of using FA as a diagnostic biomarker of ARCI. Notably, FA plays multifaceted roles, and, at certain concentrations, it promotes cell proliferation, enhances memory formation, and elongates life span, effects that could also be involved in the aetiology of ARCI. Further investigation of and the regulation of the epigenetics landscape may provide new insights about the aetiology of ARCI and provide novel therapeutic targets

Comparison of bovine serum albumin glycation by ribose and fructose in vitro and in vivo

Advanced glycation end products (AGEs) play a critical pathogenic role in the development of diabetic complications. Recent studies have shown that diabetes is associated with not only abnormal glucose metabolism but also abnormal ribose and fructose metabolism, although glucose is present at the highest concentration in humans. The glycation ability and contribution of ribose and fructose to diabetic complications remain unclear. Here, the glycation ability of ribose, fructose and glucose under a mimic physiological condition, in which the concentration of ribose or fructose was one-fiftieth that of glucose, was compared. Bovine serum albumin (BSA) was used as the working protein in our experiments. Ribose generated more AGEs and was markedly more cytotoxic to SH-SY5Y cells than fructose. The first-order rate constant of ribose glycation was found to be significantly greater than that of fructose glycation. LC-MS/MS analysis revealed 41 ribose-glycated Lys residues and 12 fructose-glycated residues. Except for the shared Lys residues, ribose reacted selectively with 17 Lys, while no selective Lys was found in fructose-glycated BSA. Protein conformational changes suggested that ribose glycation may induce BSA into amyloid-like monomers compared with fructose glycation. The levels of serum ribose were correlated positively with glycated serum protein (GSP) and diabetic duration in type 2 diabetes mellitus (T2DM), respectively. These results indicate that ribose has a greater glycation ability than fructose, while ribose largely contributes to the production of AGEs and provides a new insight to understand in the occurrence and development of diabetes complications.</p

An adaptive image fusion method for Sentinel-2 images and high-resolution images with long-time intervals

Sentinel-2 imagery has garnered significant attention in many earth system studies due to free access and high revisit frequency. Since its spatial resolution is insufficient for many applications, e.g., fine-grained land cover mapping, some studies employ fusion technique that combines high-resolution RGB images with Sentinel-2 multispectral images to improve the resolution of the latter. However, there are two issues in the existing image fusion methods. First, these methods usually assume that the time intervals between images are short (within several days), which is a strong assumption for large-scale high-resolution images and many real-world applications. Second, the spectral discrepancy between multispectral and RGB images could induce spectral aberrations in Sentinel-2 imagery upon fusion. To alleviate these issues, we propose an adaptive image fusion approach named S2IFNet, adaptively fusing images with long-time intervals (from months to years) and spectral inconsistency, thereby increasing the multispectral band resolution of Sentinel-2 imagery. Building on top of the feature extraction and fusion modules, we propose a spectral feature compensation module and a change-aware feature reconstruction module. The former alleviates the possible degradation of spectral attributes in Sentinel-2 imagery resulting from feature fusion. The latter integrates semantic and texture information to avoid adding fake textures caused by land cover changes over time. The experiments demonstrate that S2IFNet surpasses existing image fusion and reference-based super-resolution methods on synthetic and real datasets, yielding fusion results that are clearer and more reliable