Due to the influences of imaging conditions, spectral imagery can be coarse and contain a large number mixed pixels. These pixels lead inaccuracies in land-cover class (LC) mapping. Super-resolution mapping (SRM) used analyze such obtain LC information at subpixel level. However, traditional SRM methods mostly rely on spatial correlation based linear distance, which ignores nonlinear conditions. In addition, unmixing errors affect accuracy utilized properties. order overcome influence...

Farmland abandonment has important impacts on biodiversity and ecosystem recovery, as well food security rural sustainable development. Due to rapid urbanization industrialization, farmland become an increasingly problem in many countries, particularly China. To promote land-use management environmental sustainability, it is understand the socioeconomic causes spatial patterns of abandonment. In this study, we explored dynamic mechanisms Jiangxi province China using a spatially explicit...

Spectral super-resolution (SSR) aims at generating a hyperspectral image (HSI) from given RGB image. Recently, promising direction is to learn complicated mapping function the HSI counterpart using deep convolutional neural network. This essentially involves context within size-specific receptive field centered each pixel its spectrum in HSI. The focus thereon appropriately determine size and establish corresponding spectrum. Due their differences category or spatial position, pixels HSIs...

Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as control and route guidance. Traditional data driven models tend to ignore self-features (e.g., periodicities), commonly suffer from shifts brought by various complex factors weather holidays). These would reduce precision robustness of models. To tackle this problem, paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts...

Spaceborne high-resolution synthetic aperture radar (SAR) is a potential powerful tool for rainfall pattern and intensity observations over the sea surface. However, many interesting rain-related phenomena revealed by SAR images are still not fully understood due to poor theoretical modeling of rain–wind–wave interactions. This paper attempts develop physics-based radiative transfer model capture scattering behavior rough Raindrops modeled as Rayleigh nonspherical particles, whereas...

Abstract Cloud detection is an important step in remote sensing image processing and a prerequisite for subsequent analysis interpretation of images. Traditional cloud methods are difficult to accurately detect clouds snow with very similar features such as color texture. In this paper, the images deeply extracted, accurate method proposed based on advantages Unet3+ network feature fusion. Firstly, space conversion performed images, RGB HIS used input network. Resnet 50 replace extraction...

Multispectral image (MS) and panchromatic (PAN) fusion, which is also named as multispectral pansharpening, aims to obtain MS with high spatial resolution spectral resolution. However, due the usual neglect of noise blur generated in imaging transmission phases data during training, many deep learning (DL) pansharpening methods fail perform on dataset containing blur. To tackle this problem, a variational optimization-guided two-stage network (VOGTNet) for proposed work, performance...

Pansharpening technique is used to merge the original multispectral image (MS) with a high spatial resolution panchromatic (PAN). Due its robustness, multiresolution analysis (MRA) an important part of pansharpening. The scale regression model effective for improving MRA. However, existing MRA based on results into single-scale information, thus affecting final pansharpening result. To address this problem, in work, we propose dual-scale regression-based First, establish model. Then,...

The joint use of hyperspectral image (HSI) and light detection ranging (LiDAR) data has gained significant performance on land-cover classification. Although spatial-spectral feature learning methods based convolutional neural networks (CNNs) transformer have achieved prominent advances, contextual information described by fixed kernels all self-attention heads selected limited ability to characterize the detailed non-redundant features land-covers multimodal data. In this paper, a...

Pansharpening refers to the fusion between a multispectral (MS) image with abundant spectral information and panchromatic (PAN) high spatial resolution obtain (HRMS) image. The traditional pansharpening methods often ignore effect of path-radiation caused by scattering from different atmospheric components, few that introduce haze correction only calibrate each band MS individually, without exploring intrinsic correlation among bands. To address this problem, low rank tensor completion based...

To address the problems of noise interference and image blurring in hyperspectral imaging (HSI), this paper proposes a denoising method for HSI based on deep learning total variation (TV) prior. The minimizes first-order moment distance between prior Fast Flexible Denoising Convolutional Neural Network (FFDNet) Enhanced 3D TV (E3DTV) prior, obtaining dual priors that complement reinforce each other’s advantages. Specifically, original is initially processed with random binary sparse...

This letter proposes an alternative underdetermined framework for fault location that utilizes current measurements along with the branch-bus matrix, providing another option besides traditional voltage-based methods. To enhance accuracy in presence of multiple outliers, robust YALL1 algorithm is used to resist outlier interference and accurately recover sparse vector, thereby pinpointing precisely. The results on IEEE 39-bus test system demonstrate effectiveness robustness proposed method.

Metakaolin-based geopolymers, which are emerging as a novel and eco-friendly construction material, have garnered significant attention in recent years. This study employs bibliometric techniques to systematically analyze 1,553 relevant publications from the Web of Science database, with goal identifying research hotspots trends over period 2003 2023 this field. The findings reveal pronounced growth trend annual publication volume metakaolin-based particularly entering phase accelerated...

ABSTRACT Aim The priming effect (PE) refers to changes in the decomposition of native soil organic carbon induced by exogenous inputs. Specifically, an increase is termed positive PE, whereas a decrease referred as negative PE. In this study, we aimed investigate how and PE respond experimental warming factors controlling these responses at global scale. Location Global. Time Period 2008–2025. Major Taxa Studied Soil matter priming. Methods We conducted meta‐analysis combining 370 paired...

Adequate detection of the production carbapenemase in Enterobacteriaceae isolates is crucial for infection control measures and appropriate choice antimicrobial therapy. In this study, we investigated frequency false positive results carbapenemases carbapenemase-negative Escherichia coli Klebsiella pneumoniae clinical by modified Hodge test (MHT). Three hundred one E. K. were investigated. All produced extended spectrum β-lactamases (ESBLs) but susceptible to carbapenems. Antimicrobial...

This paper incorporates the concept of guanxi—a Chinese version personal connections, networks or social capital—into discussion police corruption and rise extra-legal protectors. Using published materials fieldwork data collected from two cities (Chongqing Qufu), it demonstrates how guanxi distorts China’s legal system by facilitating buying selling public offices promoting formation corrupt between locally based criminals government officials. weak framework encourages individuals...