In this paper, a three-component decomposition algorithm is proposed for processing compact polarimetric SAR images. By using the correspondence between the covariance matrix and the Stokes vector, three-component scattering models for CTLR and DCP m...
It is difficult to detect ports in polarimetric SAR images due to the complicated components, morphology, and coastal environment. This paper proposes an unsupervised port detection method by extracting the water of the port based on three-component...
China has eliminated absolute poverty and begun to tackle relative poverty, yet the change in relative poverty in China has been less studied. In this paper, we develop a three-component decomposition of the change in relative poverty and apply it to...
Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical compo...
The model-based polarimetric synthetic aperture radar (PolSAR) target decomposition decodes the scattering mechanism of the target by analyzing the essential scattering components. This paper presents a new general three-component scattering power de...
In this paper, the three-component power decomposition for polarimetric SAR (PolSAR) data with an adaptive volume scattering model is proposed. The volume scattering model is assumed to be reflection-symmetric but parameterized. For each image pixel,...
Reduced biquaternion (RB) matrices provide a commutative algebraic framework for coupled multicomponent data, but their factorizations are usually formulated through complex or real representation matrices. The aim of this study is to develop and val...
In the frame of polarimetric synthetic aperture radar (SAR) tomography, full-ranks reconstruction framework has been recognized as a significant technique for fully characterization of superimposed scatterers in a resolution cell. The technique, main...
New scattering models are constantly emerging to extract the detailed polarization information of ground targets. They contribute to the refined interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) images. However, an increasingly promine...
Forests play a significant role in terrestrial ecosystems by sequestering carbon, and forest biomass is a crucial indicator of carbon storage potential. However, the single-frequency SAR estimation of forest biomass often leads to saturation issues....
UWScat, a ground-based Ku- and X-band scatterometer, was used to compare forested and non-forested landscapes in a terrestrial snow accumulation environment as part of the NASA SnowEx17 field campaign. Field observations from Trail Valley Creek, Nort...
Polarimetric synthetic aperture radar (PolSAR) has attracted lots of attention from remote sensing scientists because of its various advantages, e.g., all-weather, all-time, penetrating capability, and multi-polarimetry. The three-component scatterin...
Accurate, real-time, and dynamic monitoring of crop planting distributions in hilly areas with complex terrain and frequent meteorological changes is highly important for agricultural production. Dual-polarization SAR has high application value in th...
Model-based polarimetric SAR (PolSAR) algorithms for bio- and geophysical parameter estimation rely on the effective separation of the combined scattering response of vegetation canopies and the soil surface through physically based models. However,...
The sustainable development of smart regions directly depends on the dependability and resilience of critical energy grids (CEGs) and their key components—electrotechnical systems (ETSs). Accurate and reliable assessment of ETS dependability is...
We propose a systematic way to construct trigonometric-like functions beyond the classical sine–cosine pair by factorising rational expressions in the umbral operator and then evaluating their action on the vacuum. The guiding idea is simple: t...
Coastal wetlands are a critical buffer between land and water that are threatened by land use and climate change, necessitating improved monitoring for management and resilience planning. The recently launched NASA-ISRO L- and S-band SAR satellite (N...
Alternate Wetting and Drying (AWD) is a proven water-saving irrigation technique that reduces irrigation water use and methane emissions from rice cultivation. The emission reduction achievable through AWD irrigation practices represents a significan...
Pseudo quad polarimetric (quad-pol) image reconstruction from the hybrid dual-pol (or compact polarimetric (CP)) synthetic aperture radar (SAR) imagery is a category of important techniques for radar polarimetric applications. There are three key asp...
A polarimetric synthetic aperture radar (PolSAR) has great potential in ground target classification. However, current methods experience difficulties in separating forests and buildings, especially oriented buildings. To address this issue, inspired...
High-resolution snow water equivalent studies are important for obtaining a clear picture of the potential of water resources in arid areas, and SAR-based sensors can achieve meter-level snow water equivalent inversion. The advanced C-band SAR satell...
In this work, the design and characterization of new supported ionic liquid membranes, as medium-temperature polymer electrolyte membranes for fuel-cell application, are described. These membranes were elaborated by the impregnation of porous polyimi...
Accurate prediction of wake interactions in twin vertical-axis hydroturbine (VAHT) arrays is important for dense tidal-farm layout assessment but remains computationally expensive when based directly on Computational Fluid Dynamics (CFD) reference si...
A fast inversion algorithm combined with the transient electromagnetic (TEM) detection system has important significance for improving the detection efficiency of unexploded ordnance. The traditional algorithms, such as differential evolution or Gaus...
In this study, a field experiment was performed at the Experimental Station of Vytautas Magnus University Agriculture Academy in Lithuania from 2018 to 2020. The researched soil comprised carbonate deeper gleyic leachate (IDg4-k) (Calc(ar)i Endohypog...
The present study investigated the structure, morphology, thermal behavior, and bacterial growth analysis of novel three-component hybrid materials synthesized by the sol-gel method. The inorganic silica matrix was weakly bonded to the network of two...
The possibilities surrounding positronics, a versatile noninvasive tool employing annihilating positrons to probe atomic-deficient sub-nanometric imperfections in a condensed matter, are analyzed in application to glassy arsenoselenides g-AsxSe100&mi...
As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accu...
Microbial communities inhabiting the deep-sea salt-saturated anoxic lakes of the Eastern Mediterranean operate under harsh physical-chemical conditions that are incompatible with the lifestyle of common marine microorganisms. Here, we investigated a...
The so-called arbitrary decomposition of a given Mueller matrix into a convex sum of nondepolarizing constituents provides a general framework for parallel decompositions of polarimetric interactions. Even though arbitrary decomposition can be perfor...
Partial information decomposition allows the joint mutual information between an output and a set of inputs to be divided into components that are synergistic or shared or unique to each input. We consider five different decompositions and compare th...
Forest height is a key parameter for quantifying forest biomass and carbon stocks and serves as an important indicator of forest ecosystem health. The successful launch of the European Space Agency’s P-band Biomass satellite, which provides Pol...
Wind power forecasting is challenging because of complex, nonlinear relationships between inherent patterns and external disturbances. Though much progress has been achieved in deep learning approaches, existing methods cannot effectively decompose a...
Accurate forecasting of electricity sales holds significant practical importance. On the one hand, it helps to implement and achieve the annual goals of power companies, and on the other hand, it helps to control the balance of enterprise profits. Th...
Runoff time series often exhibit nonlinear and fluctuating characteristics, and their complexity has further increased with the intensification of global climate change; high-precision daily-scale forecasting remains a core challenge in the field of...
Continuous glucose monitoring data have strong time variability as well as complex non-stationarity and nonlinearity. The existing blood glucose concentration prediction models often overlook the impacts of residual components after multi-scale decom...
As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and c...
Dealing with municipal sludge in an effective way is crucial for urban development and environmental protection. Co-processing the sludge by burning it in a decomposition furnace in the cement production line has been found to be a viable solution. T...
This research introduces a new hybrid forecasting approach based on a rolling decomposition–merging–prediction framework integrating Empirical Mode Decomposition (EMD), sample entropy, and Long Short-Term Memory (LSTM) to further enhance...
To overcome the limitations of long-term prediction of PM2.5 concentration, a multi-factor information flow causality analysis method is used to screen suitable meteorological and air pollutant-related factors and concatenate them with a PM2.5 sequen...
Thermal infrared remote satellite (TIRS) images combined with high-resolution optical images in a time series can be used to analyze the production status of iron and steel factories (ISF) in China, which is more objective compared with statistical d...
Research Highlight: Forest disturbance by insects or disease can have a significant influence on nutrient return by litterfall and decomposition, but information regarding disturbance gradients is scarce. This study demonstrated that the disturbance...
Evaporation (E) and transpiration (T) information is crucial for precise water resources planning and management in arid and semiarid areas. Two-source energy balance (TSEB) methods based on remotely-sensed land surface temperature provide an importa...
Electric vehicle (EV) smart charging changes the operating point of the vehicle on-board charger (OBC), so conversion efficiency and grid-side power quality can vary materially with the charging current. This study reanalyses an experimental dataset...
Dissolved organic matter (DOM) plays a significant role in the nutrient supply, energy flow, and pollutant transportation in the wetland ecosystem. However, little is known about the effect of the decomposition of different organic materials in alpin...
The extensive deployment of automatic water quality monitoring stations has generated substantial volumes of time-series data. Effectively utilizing these data is crucial for enhancing prediction accuracy. To address the limitations of existing model...
This paper considers the proportional–integral–derivative (PID) control for continuous-time positive systems. A three-stage strategy is introduced to design the PID controller. In the first stage, the proportional and integral components...
Images captured under low-light conditions often suffer from serious degradation due to insufficient light, which adversely impacts subsequent computer vision tasks. Retinex-based methods have demonstrated strong potential in low-light image enhancem...
The partial information decomposition (PID) framework is concerned with decomposing the information that a set of random variables has with respect to a target variable into three types of components: redundant, synergistic, and unique. Classical inf...
This study investigates the predictive performance of decomposition-based deep learning models through a focused case study on Ethereum price forecasting. Using hourly Ethereum price data from 5 September 2020 to 13 July 2025, we develop hybrid forec...
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