Search Results (147)

Hyperspectral technology has been widely applied to the retrieval of soil properties, such as soil organic matter (SOM) and particle size distribution (PSD). However, most previous studies have focused on hyperspectral data acquired from the nadir direction, and the influence of viewing geometry on hyperspectral-based soil property retrieval remains unclear. In this study, bidirectional reflectance factors (BRFs) were collected at 48 different viewing angles for 154 soil samples with varying SOM contents and PSDs. SOM and PSD were then retrieved using combinations of ten spectral preprocessing methods (raw reflectance, Savitzky–Golay filter (SG), first derivative (D1), second derivative (D2), standard normal variate (SNV), multiplicative scatter correction (MSC), SG + D1, SG + D2, SG + SNV, and SG + MSC), one sensitive wavelength selection method, and three retrieval algorithms (partial least squares regression (PLSR), support vector machine (SVM), and convolutional neural networks (CNNs)). The influence of viewing geometry on the selection of spectral preprocessing methods, retrieval algorithms, sensitive wavelengths, and retrieval accuracy was systematically analyzed. The results showed that soil BRFs are influenced by both soil properties and viewing angles. The viewing geometry had limited effects on the choice of preprocessing method and retrieval algorithm. Among the preprocessing methods, D1, SG + D1, and SG + D2 outperformed the others, while PLSR achieved a higher accuracy than SVM and CNN when retrieving soil properties. The selected sensitive wavelengths for both SOM and PSD varied slightly with viewing angle and were mainly located in the near-infrared region when using BRFs from multiple viewing angles. Compared with single-angle data, multi-angle BRFs significantly improved retrieval performance, with the R² increasing by 11% and 15%, and RMSE decreasing by 16% and 30% for SOM and PSD, respectively. The optimal viewing zenith angle ranged from 10° to 20° for SOM and around 40° for PSD. Additionally, backward viewing directions were more favorable than forward directions, with the optimal viewing azimuth angles being 0° for SOM and 90° for PSD. These findings provide useful insights for improving the accuracy of soil property retrieval using multi-angle hyperspectral observations. Full article

Snow water equivalent (SWE) can be estimated using Differential SAR Interferometry (DInSAR), which captures changes in snow depth and density between two SAR acquisitions. However, challenges arise due to SAR signal penetration into the snowpack and the intrinsic limitations of DInSAR measurements. This study addresses these issues and explores the use of multi-band SAR data to derive SWE maps in alpine regions characterized by steep terrain, small spatial extent, and a potentially heterogeneous snowpack. We first conducted a performance analysis to assess SWE estimation precision and the maximum unambiguous SWE variation, considering incidence angle, wavelength, and coherence. Based on these results, we selected C-band Sentinel-1 and L-band SAOCOM data acquired over alpine areas and applied tailored DInSAR processing. Atmospheric artifacts were corrected using zenith total delay maps from the GACOS service. Additionally, sensitivity maps were generated for each interferometric pair to identify pixels suitable for reliable SWE estimation. A comparative analysis of the C- and L-band results revealed several critical issues, including significant atmospheric artifacts, phase decorrelation, and phase unwrapping errors, which impact SWE retrieval accuracy. A comparison between our Sentinel-1-based SWE estimations and independent measurements over an instrumented site shows results fairly in line with previous works exploiting C-band data, with an RSME in the order of a few tens of mm. Full article

In this investigation, we established an intelligent computational framework comprising a novel starfish-optimization-algorithm-optimized support vector regression (SOA-SVR) model and a multi-algorithm joint strategy to evaluate the processing applicability of wheat flour in terms of sedimentation value (SV) and falling number (FN) using near-infrared (NIR) data (900–1700 nm) obtained using a miniaturized NIR spectrometer. By employing an improved whale optimization algorithm (iWOA) coupled with a successive projections algorithm (SPA), we selected the 20 most informative wavelengths (MIWs) from the full range spectra, allowing the iWOA/SPA-SOA-SVR model to predict SV with correlation coefficient and root-mean-square errors in prediction (R_P and RMSE_P) of 0.9605 and 0.2681 mL. Additionally, RFE, in combination with the iWOA, identified 30 MIWs and enabled the RFE/iWOA-SOA-SVR model to predict the FN with an R_P and RMSE_P of 0.9224 and 0.3615 s. The robustness and reliability of the two SOA-SVR models were further validated using 50 independent samples per index, a statistical two-sample F-test, and a t-test. In conclusion, the combination of a portable miniaturized NIR spectrometer and an SOA-driven SVR algorithm demonstrated technical feasibility in quantifying the SV and FN of wheat flour, thus providing a novel strategy for the on-site assessment of wheat flour processing applicability. Full article

A novel single-layer multi-coupling 3D frequency-selective surface featuring high selectivity and a 3D dual-band frequency-selective surface (3D-DBFSS) is presented in this paper. By incorporating three different coupling gaps within each unit cell, the design achieves two distinct passbands with wide bandwidths and steep transition edges. For the first passband, the −3 dB bandwidth ranges from the lower cutoff frequency of 9.9 GHz to the upper cutoff frequency of 15.2 GHz, yielding a bandwidth (BWL3dB) of 5.3 GHz. For the second passband, the −3 dB bandwidth extends from 22 GHz to 26 GHz, providing a bandwidth (BWU3dB) of 4 GHz. The structure exhibits eight transmission poles and four transmission zeros within a single layer, enhancing its selectivity. The simulation results indicate that the dual passbands are centered at 12 GHz and 24 GHz, respectively, with bandwidths sufficient for practical applications. The proposed frequency-selective surface demonstrates a low insertion loss of just 0.8 dB, which is significantly lower compared to most reported dual-band FSS designs. Furthermore, the thickness of the 3D-DBFSS is only one-third of the wavelength in free space, making it considerably thinner than other 3D-FSS structures operating in the same frequency range. The proposed design also ensures stable performance over a wide range of incident angles, which is crucial for practical deployment. Additionally, the overall size of the unit cell of the frequency-selective surface is 4 × 4 × 10.8 m³. The structure is easy to fabricate, which contributes to its potential for cost-effective mass production. Overall, the 3D-DBFSS offers high frequency selectivity, effective bandpass performance, and strong suppression in the stopband region. Full article

The nutritional quality of rice seeds is mainly determined by the content of key components such as protein, fat, and starch. Traditional chemical detection methods are time-consuming, labor-intensive, inefficient, and harmful to the environment. To overcome these limitations, this study developed a non-destructive detection method using near-infrared spectroscopy (1000–2200 nm) combined with linear regression modeling to achieve efficient and simultaneous multi-component analysis through the principle of anharmonic molecular vibration. By combining nutrient data from chemical analysis with spectroscopic measurements, we established a comprehensive rice seed composition dataset. After preprocessing with Gaussian denoising, first-order derivative transformation, SPA wavelength selection, and multiplicative scatter correction (MSC), we constructed partial least squares regression (PLS) and orthogonal partial least squares (OPLS), as well as artificial neural network (ANN) models. The OPLS model performed well in fat prediction (R² = 0.971, Q² = 0.926, RMSE = 0.175, RMSECV = 0.186), followed by starch (R² = 0.956, Q² = 0.907, RMSE = 0.159, RMSECV = 0.146) and protein (R² = 0.967, Q² = 0.936, RMSE = 0.164, RMSECV = 0.156). Our results confirm that the combination of the moving average, first order derivative, SPA, and MSC preprocessing of the OPLS model significantly improves the prediction. The developed non-destructive testing equipment provides a practical solution for automated, high-precision sorting of rice seeds based on nutrient composition. Full article

With the rapid development of multispectral detection technology, realizing the synergistic camouflage and thermal management of materials in multi-band has become a major challenge. In this paper, a multifunctional radiation-selective hierarchical metamaterial (RSHM) is designed to realize the modulation of optical properties in a wide spectral range through the delicate design of microstructures and nanostructures. In the atmospheric windows of 3–5 μm and 8–14 μm, the emissivity of the material is as low as 0.14 and 0.25, which can effectively suppress the radiation characteristics of the target in the infrared band, thus realizing efficient infrared stealth. Simultaneously, it exhibits high emissivity in the 2.5–3 μm (up to 0.80) and 5–8 μm (up to 0.98) bands, significantly improving thermal radiation efficiency and enabling active thermal management. Notably, RSHM achieves low reflectivity at 1.06 μm (0.13) and 1.55 μm (0.005) laser wavelengths, as well as in the 8–14 μm (0.06) band, substantially improving laser stealth performances. Additionally, it maintains high transmittance in the visible light range, ensuring excellent visual camouflage effects. Furthermore, the RSHM demonstrates exceptional incident angle and polarization stability, maintaining robust performances even under complex detection conditions. This design is easy to expand relative to other frequency bands of the electromagnetic spectrum and holds significant potential for applications in military camouflage, energy-efficient buildings, and optical devices. Full article

In this study, novel focusing performances of high-numerical-aperture (NA) micro-Fresnel zone plates (FZPs) with selective occlusion are identified and investigated through numerical calculations based on vectorial angular spectrum (VAS) theory, and further rigorously validated using the finite-difference time-domain (FDTD) method. The central occlusion of a standard micro-FZP can significantly extend the depth of focus while keeping the lateral size of the focusing spot essentially unchanged. When a standard micro-FZP only retains two separated transparent rings and all other rings are obstructed, it will result in multi-focus phenomena; at the same time, the number of focal points is equal to the difference in number between the two separated transparent rings. Furthermore, a focusing light needle can be generated by combining the central occlusion and wavelength shift of a standard micro-FZP. This study not only provides new ideas for the design and optimization of micro-FZPs but also provides reference for the expansion of practical applications of FZPs. Full article

Total soluble solids (TSSs) serve as a crucial maturity indicator and quality determinant in apricots, influencing harvest timing and postharvest management decisions. This study develops an advanced framework integrating adaptive boosting (Adaboost) ensemble learning with high-frequency spectral variables selected by uninformative variable elimination (UVE) for the rapid non-destructive detection of fruit quality. Near-infrared (NIR) spectra (1000~2500 nm) were acquired and then preprocessed through robust principal component analysis (ROBPCA) for outlier detection combined with z-score normalization for spectral pretreatment. Subsequent data processes included three steps: (1) 100 continuous runs of UVE identified characteristic wavelengths, which were classified into three levels—high-frequency (≥90 times), medium-frequency (30–90 times), and low-frequency (≤30 times) subsets; (2) the development of the base optimal partial least squares regression (PLSR) models for each wavelength subset; and (3) the execution of adaptive weight optimization through the Adaboost ensemble algorithm. The experimental findings revealed the following: (1) The model established based on high-frequency wavelengths outperformed both full-spectrum model and full-characteristic wavelength model. (2) The optimized UVE-PLS-Adaboost model achieved the peak performance (R = 0.889, RMSEP = 1.267, MAE = 0.994). This research shows that the UVE-Adaboost fusion method enhances model prediction accuracy and generalization ability through multi-dimensional feature optimization and model weight allocation. The proposed framework enables the rapid, non-destructive detection of apricot TSSs and provides a reference for the quality evaluation of other fruits in agricultural applications. Full article

In this study, we propose a color-based multispectral approach using four selected wavelengths (453, 556, 668, and 708 nm) from the visible to near-infrared range to separate clothing from the background. Our goal is to develop a human detection camera that supports real-time processing, particularly under daytime conditions and for common fabrics. While conventional deep learning methods can detect humans accurately, they often require large computational resources and struggle with partially occluded objects. In contrast, we treat clothing detection as a proxy for human detection and construct a lightweight machine learning model (multi-layer perceptron) based on these four wavelengths. Without relying on full spectral data, this method achieves an accuracy of 0.95, precision of 0.97, recall of 0.93, and an F1-score of 0.95. Because our color-driven detection relies on pixel-wise spectral reflectance rather than spatial patterns, it remains computationally efficient. A simple four-band camera configuration could thus facilitate real-time human detection. Potential applications include pedestrian detection in autonomous driving, security surveillance, and disaster victim searches. Full article

High-redshift active galactic nuclei (AGN) provide key insights into early supermassive black hole growth and cosmic evolution. This study investigates the parsec-scale properties of 86 radio-loud quasars at z ≥ 3 using very long baseline interferometry (VLBI) observations. Our results show predominantly compact core and core-jet morphologies, with 35% having unresolved cores, 59% with core–jet structures, and only 6% with core–double jet morphology. Brightness temperatures are generally lower than expected for highly radiative sources. The jets’ proper motions are surprisingly slow compared to those of lower-redshift samples. We observe a high fraction of young and/or confined peak-spectrum sources, providing insights into early AGN evolution in dense environments during early cosmic epochs. The observed trends may reflect genuine evolutionary changes in AGN structure over cosmic time, or selection effects favoring more compact sources at higher redshifts. These results stress the complexity of high-redshift radio-loud AGN populations and emphasize the need for multi-wavelength, high-resolution observations to fully characterize their properties and evolution through cosmic history. Full article

A wideband dual-polarized dielectric resonator antenna (DRA) with gain-filtering response was proposed in this paper. First, a differentially fed, low-profile crossed-DRA was used to obtain orthogonal polarizations with two resonant modes. A radiation null at upper band edge was also generalized. Second, with the introduction of four parasitic patches at the top of the crossed DRA, another resonant mode at lower band was excited, and the bandwidth was greatly expanded. Moreover, the introduction of parasitic patches could also help improve the selectivity of realized gain with another radiation null at the upper band edge. Furthermore, four sequentially rotated shorting coupling structures (SRSCSs) were proposed for the first time to generalize two additional radiation nulls. Finally, a wideband bandpass filtering response of the realized gain with four radiation nulls was obtained. Prototypes of the proposed antennas were fabricated, and the testing results showed that the antenna had a wide operation band of 57.1% from 2.75 GHz to 4.95 GHz with sharp roll-off at the band edge. This technique could also be used in wireless communication devices at millimeter/optical front ends and other multi-wavelength fiber lasers with micro structures. Full article

Over the course of NASA’s Dawn Discovery mission, the onboard framing camera mapped Ceres across a wide wavelength spectrum at varying polar science orbits and altitudes. With increasing resolution, the uniqueness of the 92 km wide, young Occator crater became evident. Its central cryovolcanic dome, Cerealia Tholus, and especially the associated bright carbonate and ammonium chloride deposits—named Cerealia Facula and the thinner, more dispersed Vinalia Faculae—are the surface expressions of a deep brine reservoir beneath Occator. Understandably, this made this crater the target for future sample return mission studies. The planning and preparation for this kind of mission require the characterization of potential landing sites based on the most accurate topography and orthorectified image data. In this work, we demonstrate the capabilities of the freely available and open-source USGS Integrated Software for Imagers and Spectrometers (ISIS 3) and Ames Stereo Pipeline (ASP 2.7) in creating high-quality image data products as well as stereophotogrammetric (SPG) and multi-view shape-from-shading (SfS) digital terrain models (DTMs) of the aforementioned spectroscopically challenging features. The main data products of our work are four new DTMs, including one SPG and one SfS DTM based on High-Altitude Mapping Orbit (HAMO) (CSH/CXJ) and one SPG and one SfS DTM based on Low-Altitude Mapping Orbit (LAMO) (CSL/CXL), along with selected Extended Mission Orbit 7 (XMO7) framing camera (FC) data. The SPG and SfS DTMs were calculated to a GSD of 1 and 0.5 px, corresponding to 136 m (HAMO SPG), 68 m (HAMO SfS), 34 m (LAMO SPG), and 17 m (LAMO SfS). Finally, we show that the SPG and SfS approaches we used yield consistent results even in the presence of high albedo differences and highlight how our new DTMs differ from those previously created and published by the German Aerospace Center (DLR) and the Jet Propulsion Laboratory (JPL). Full article

This paper presents an integrated tunable hybrid multi-laser module designed to simultaneously generate multiple radiofrequency (RF) local oscillator (LO) signals through optical heterodyning. The device consists of five hybrid InP/Si₃N₄ integrated lasers, each incorporating an intracavity wavelength-selective optical filter formed by two micro-ring resonators. Through beating the wavelengths generated from three of these lasers, we demonstrate the simultaneous generation of two LO signals within bands crucial for satellite communications (SatCom): one in the Ka-band and the other in the V-band. The device provides an extensive wavelength tuning range across the entire C-band and exhibits exceptionally narrow optical linewidths, below 40 kHz in free-running mode. This results in ultra-wideband tunable RF signals with narrow electrical linewidths below 100 kHz. The system is compact and highly scalable, with the potential to generate up to 10 simultaneous LO signals, being a promising solution for advanced RF signal generation in high throughput satellite payloads. Full article

We propose and demonstrate a tunable and switchable multi-wavelength fiber ring laser configuration based on a Mach–Zehnder interferometer (MZI) filter. The MZI was fabricated using a core-offset splicing technique, with a 2 cm piece of thin-core erbium-doped fiber (TCEDF), with a core diameter of 2.90 µm, coupled in the central region of the MZI between two segments of single-mode fiber (SMF). By applying curvature to the MZI filter, we generated lasing single-, double-, triple-, and quadruple-emission lines with a curvature range from 2.3452 m⁻¹ to 6.0495 m⁻¹. A single-emission lasing line can be tuned from 1556.63 nm to 1564.25 nm with a tuning span of 7.62 nm and an SMSR of 49.80 dB. The laser emission can be switched to quadruple- and triple-emission lasing signals, with SMSR values of 39.96 dB and 36.83 dB, respectively. The dual-narrow emission lasing signal can be tuned from 1564.56 nm to 1561.34 nm, with an SMSR of 40.46 dB. Another lasing dual-emission signal can be tuned from 1585.69 nm to 1576.89 nm, producing an 8.8 nm tuning range, and from 1572.53 nm to 1563.66 nm, producing an 8.87 nm range, with the best SMSR of 42.35 dB. Full article

For accurate segmentation of lettuce canopy images, dealing with uneven illumination and background interference, hyperspectral imaging technology was applied to capture images of lettuce from the rosette to nodule stages. The spectral ratio method was used to select the characteristic wavelengths, and the characteristic wavelength images were denoised and image fused before being processed by filtering and threshold segmentation. To verify the accuracy of this segmentation method, the manual segmentation method and the segmentation method used in this study were compared, and the area overlap degree ($AOM$) and misclassification rate ($ME$) were used as criteria to evaluate the segmentation results. The results showed that the segmentation effect was the best when 553.8 nm, 702.5 nm and 731.3 nm were selected as the characteristic wavelengths of lettuce for the spectral ratio method, with an $AOM$ of 0.9526 and an $ME$ of 0.0477. Both have a variance of less than 0.01 and have the best stability. Hyperspectral imaging technology combined with multi-wavelength image and multi-threshold segmentation can achieve accurate segmentation of lettuce canopy images. Full article