Spectral Images Research Articles

Comparison of field and imaging spectroscopy to optimize soil organic carbon and nitrogen estimation in field laboratory conditions

Regenerative agriculture (RA) aims to improve soil health, water retention capacity, and resilience through sustainable regeneration and retention of soil organic carbon (SOC) and soil nitrogen (SN). Although laboratory analysis offers a reliable method for measuring SOC and SN, access to such facilities can be limited in remote areas and inconvenient, particularly if a large number of samples need to be analyzed. To address this, we compared two hyperspectral sensors, SVC HR-1024i field spectroradiometer (FS) and Specim IQ imaging spectrometer (IS), to estimate SOC and SN using 157 soil samples collected from agricultural sites in Taita Hills, Kenya. Reference SOC and SN content (%) were analyzed in the laboratory, and spectral measurements and images of the air-dried soil samples were generated using protocols suitable for field laboratories. Finally, we employed partial least squares regression (PLSR), Gaussian process regression (GPR), and least absolute shrinkage and selection operator (LASSO) to estimate SOC and SN from the preprocessed soil spectra over different wavelength ranges. Our best models yielded better accuracy for SOC estimation (R2 = 0.83andRMSE = 0.36 %) and an R2of 0.70with anRMSEof0.07 % for SN using the field spectroradiometer. Both SOC and SN modeling over the full wavelength and shortwave-infrared (SWIR) regions achieved considerably better predictive accuracy than visible to near-infrared regions. These results suggest that FS with the SWIR region is best suited for SOC and SN estimation to support the planning and monitoring needs of RA initiatives.

Analytical design of three-spectral-resolution scanning imaging spectrometer based on concave grating with multiple groove densities

Imaging spectrometers are generally designed with constant spectral resolution and do not have flexibility to achieve imaging different targets at respective spectral resolution. In addition, unnecessary increase in the data cube and transmission pressure result in an efficiency decrease in information gathering and processing. In this paper, a scanning imaging spectrometer with three spectral resolution (SISTR) is proposed. The concave grating with different groove densities is designed at the aperture stop. SISTR achieves the simultaneous acquisition of three sets of spectral images with the same spectral range but different spectral resolution. This paper studies the key issues of such broadband imaging spectrometers with different spectral resolution. Also evaluated are how groove densities of the concave grating affect the spectral overlap and the ways the diameters and decenters of three areas on the concave grating affect the uniformity of the energy distribution among three sets of spectral images. Accordingly, SISTR with three spectral resolution and high variation ratio is optimized. SISTR achieves a high numerical aperture of 0.33 and three spectral resolution of 0.31 nm, 0.64 nm, 1.40 nm in a wide spectral range of 400 to 800 nm. This paper provides a theoretical reference for further developments of imaging spectrometers with multiple resolution.

Hyperspectral-multispectral image fusion using subspace decomposition and Elastic Net Regularization

ABSTRACT The fusion of hyperspectral and multispectral images presents a challenge as it involves blending a low-resolution hyperspectral image (HSI) with a corresponding multispectral image (MSI) to produce a high-resolution hyperspectral image (HRI). A number of existing techniques have limitations; for instance, matrix decomposition-based approaches fail to retain adequate spatial and spectral image information during fusion, while tensor decomposition-based processes have high computational overhead. In this paper, we propose a novel method for fusing hyperspectral and multispectral images. Our method leverages the strong correlation among the spectral bands of hyperspectral images and employs the SVD technique to extract spectral feature subspaces. This approach results in a more compact and representative feature space for fusion. Secondly, the proposed method utilizes Elastic Net regularization in combination with L 1 and L 2 regularization for effective feature selection of highly covariate features. Weighted group sparse regularization is employed to enhance the fusion effect, enabling better representation of the image’s structure and features. The algorithm is subsequently evaluated on multiple datasets to confirm its effectiveness. The results of the experiment suggest that the suggested algorithm greatly enhances the spatial resolution and visual clarity of HSI images while preserving the spectral characteristics when compared to conventional methods for fusion of hyperspectral images. Additionally, the constraint for regulation can competently reduce any noise or artefacts, thereby boosting image discrimination. To summarize, the utilization of a sparse tensor-based hyperspectral image fusion algorithm with subspace learning provides an efficacious approach for processing hyperspectral imagery. This method is capable of improving spatial resolution, extracting advantageous features from hyperspectral imagery, and ultimately supports the process of remote sensing image analysis and application.

Line-field dynamic optical coherence tomography platform for volumetric assessment of biological tissues.

Dynamic optical coherence tomography (dOCT) utilizes time-dependent signal intensity fluctuations to enhance contrast in OCT images and indirectly probe physiological processes in cells. Majority of the dOCT studies published so far are based on acquisition of 2D images (B-scans or C-scans) by utilizing point-scanning Fourier domain (spectral or swept-source) OCT or full-field OCT respectively, primarily due to limitations in the image acquisition rate. Here we introduce a novel, high-speed spectral domain line-field dOCT (SD-LF-dOCT) system and image acquisition protocols designed for fast, volumetric dOCT imaging of biological tissues. The imaging probe is based on an exchangeable afocal lens pair that enables selection of combinations of transverse resolution (from 1.1 µm to 6.4 µm) and FOV (from 250 × 250 µm2 to 1.4 × 1.4 mm2), suitable for different biomedical applications. The system offers axial resolution of ∼ 1.9 µm in biological tissue, assuming an average refractive index of 1.38. Maximum sensitivity of 90.5 dB is achieved for 3.5 mW optical imaging power at the tissue surface and maximum camera acquisition rate of 2,000 fps. Volumetric dOCT images acquired with the SD-LF-dOCT system from plant tissue (cucumber), animal tissue (mouse liver) and human prostate carcinoma spheroids allow for volumetric visualization of the tissues' cellular and sub-cellular structures and assessment of cellular motility.

Role of effective atomic number of paraspinal muscles in the prediction of acute vertebral fracture risk assessment: a cross-sectional case-control study.

We aim to investigate the relations among effective atomic number (Zeff), density, and area of paraspinal muscles, volumetric bone mineral density (vBMD), and acute vertebral fractures (VF) by using spectral base images (SBIs) and routine CT images. A total of 223 patients (52 men and 171 women) with acute lumber VF and 776 subjects (286 men and 390 women) without VF of at least 60 years were enrolled and underwent dual-layer detector CT scans. We quantified the cross-sectional area, density (paraSMD), and Zeff of paraspinal muscles by CT images and SBIs and measured vBMD of the lumbar spine by quantitative CT. Higher vBMD was associated with lower VF risk in both sexes (adjusted OR, 0.33 and 0.43). After adjusting for age and body mass index, the associations of paraSMD with VF were not significant in men, and in women the association was borderline significant (OR, 0.80; 95% CI, 0.64-1.00). However, higher Zeff of paraspinal muscles was associated with lower VF risk in men (adjusted OR, 0.59; 0.36-0.96) but not in women. The associations of all muscle indexes with VF were not significant after further adjusting for vBMD. A higher Zeff of paraspinal muscles is associated with lower VF risk in older men but not in older women. The density, area, and Zeff of paraspinal muscles were not vBMD independent risk factors for acute VF. The effective atomic number of paraspinal muscles might be a potential marker for VF risk prediction.

Classification of Soybean Genotypes as to Calcium, Magnesium, and Sulfur Content Using Machine Learning Models and UAV–Multispectral Sensor

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field.

Self-immunological disease aid diagnosis with ConvSANet and Eu-clidean distance

BackgroundAnkylosing spondylitis (AS), Osteoarthritis (OA), and Sjögren's syndrome (SS) are three prevalent autoimmune diseases. If left untreated, which can lead to severe joint damage and greatly limit mobility. Once the disease worsens, patients may face the risk of long-term disability, and in severe cases, even life-threatening consequences. ResultIn this study, the Raman spectral data of AS, OA, and SS are analyzed to auxiliary disease diagnosis. For the first time, the Euclidean distance(ED) upscaling technique was used for the conversation from one-dimensional(1D) disease spectral data to two-dimensional(2D) spectral images. A dual-attention mechanism network was then constructed to analyze these two-dimensional spectral maps for disease diagnosis. The results demonstrate that the dual-attention mechanism network achieves a diagnostic accuracy of 100 % when analyzing 2D ED spectrograms. Furthermore, a comparison and analysis with s-transforms(ST), short-time fourier transforms(STFT), recurrence maps(RP), markov transform field(MTF), and Gramian angle fields(GAF) highlight the significant advantage of the proposed method, as it significantly shortens the conversion time while supporting disease-assisted diagnosis. Mutual information(MI) was utilized for the first time to validate the 2D Raman spectrograms generated, including ED, ST, STFT, RP, MTF, and GAF spectrograms. This allowed for evaluation of the similarity between the original 1D spectral data and the generated 2D spectrograms. SignificantThe results indicate that utilizing ED to transform 1D spectral data into 2D images, coupled with the application of convolutional neural network(CNN) for analyzing 2D ED Raman spectrograms, holds great promise as a valuable tool in assisting disease diagnosis. The research demonstrated that the 2D spectrogram created with ED closely resembles the original 1D spectral data. This indicates that ED effectively captures key features and important information from the original data, providing a strong descript.

Detection the internal quality of watermelon seeds based on terahertz imaging technology combined with image smoothing and enhancement algorithm

AbstractThe cultivation processes of watermelon seed are often affected by issues such as empty shells and defects, resulting in significant losses. To obtain high‐quality seeds, the terahertz imaging technology combined with image smoothing and enhancement algorithm was proposed to reduce the noise and non‐obvious features caused by the influence in the imaging process and realize the non‐destructive, efficient, and accurate detection of the internal quality of watermelon seeds. Initially, a terahertz imaging system with a spatial resolution of 0.4 mm was used to acquire images of watermelon seeds with varying levels of fullness. Subsequently, denoising techniques, including Gaussian filtering, median filtering, bilateral filtering, discrete wavelet transformation denoising, wavelet denoising, and principal component analysis denoising, were used to handle the terahertz spectral images of watermelon seeds in the frequency range of 1–1.5 THz, respectively. Image enhancement operations, involving segmented linear gray‐level transformation and fractional‐order differentiation, were performed on the terahertz images of watermelon seeds after denoising. The optimal image processing approach was determined based on defect assessment through threshold segmentation. Finally, the validation was conducted at a spatial resolution of 0.2 mm. The images at a spatial resolution of 0.4 mm were subjected to wavelet denoising and window slicing in segmented linear gray‐level transformation (WS‐SLT) enhancement; the results exhibited the following improvements in defect accuracy compared with untreated THz images. A 7.74% increase in accuracy was observed for empty seeds, along with a 6.29% increase in the defect ratio for defective seeds 1. The defect ratio for intact seeds was 0, and there was no significant difference in defect ratio accuracy for defective seeds 2. At a spatial resolution of 0.2 mm, the average defect ratio error of THz imaging handled by wavelet denoising and WS‐SLT was approximately 5.04%. In conclusion, the terahertz imaging technology coupled with wavelet denoising and WS‐SLT methods can be used to enhance the accuracy of internal defect detection in watermelon seeds, and it provides a technical foundation and reference for assessing watermelon seed fullness.

Assessing and segmenting salt-affected soils using in-situ EC measurements, remote sensing, and a modified deep learning MU-NET convolutional neural network

A study revealed that the Siwa Oasis faces high soil salinity, which negatively impacts agricultural areas and crop productivity, despite its significant economic and agricultural importance. The current research proposes an approach to detect and segment salinity and vegetation areas at Siwa Oasis, Egypt, by combining remote sensing and building a deep learning neural network model-based U-NET algorithm to detect salinity change areas, anticipate further degradation, and predict soil quality indicators. To locate changes among the available images, standard image improvement, classification, and change detection methods have been used. We applied a deep learning modified U-Net (MU-NET) algorithm to segment and produce salinity maps. The MU-NET architecture is a two-level nested U-structure merged with a residual U-block (RUb), which consists of an encoder and a decoder. We applied RUb, which consists of several layers and skip connections. Different combinations of the salinity and vegetation indices were added to the original image to improve segmentation precision. The model was validated and trained using actual data samples collected over a 10-year period from the Landsat 8 satellite, which can monitor and analyse present land cover changes. The dataset consisted of 91 OLI and TIRS spectral images. Each one consists of eleven bands with a spatial resolution of 30 m for bands 1 to 7 and 9. A field survey was used as the main source of data for comparing the proposed model's outputs to assess the error rate. The study region is experiencing an increase in soil salinity in all directions, particularly with regard to the spatial distribution of saline soils, not just the quantitative increase in salt-affected soils. These findings supported the acceleration of soil salinization and vegetation death. The proposed model achieved the highest performance results among the other models and literature and was based on applying method 12 using 13 image layers, with the highest accuracies of 91.27% and 90.83% for salinity and vegetation, respectively.

Photon-counting detector CT - first experiences in the field of musculoskeletal radiology.

The introduction of photon-counting detector CT (PCD-CT) marks a remarkable leap in innovation in CT imaging. The new detector technology allows X-rays to be converted directly into an electrical signal without an intermediate step via a scintillation layer and allows the energy of individual photons to be measured. Initial data show high spatial resolution, complete elimination of electronic noise, and steady availability of spectral image data sets. In particular, the new technology shows promise with respect to the imaging of osseous structures. Recently, PCD-CT was implemented in the clinical routine. The aim of this review was to summarize recent studies and to show our first experiences with photon-counting detector technology in the field of musculoskeletal radiology.We performed a literature search using Medline and included a total of 90 articles and reviews that covered recent experimental and clinical experiences with the new technology.In this review, we focus on (1) spatial resolution and delineation of fine anatomic structures, (2) reduction of radiation dose, (3) electronic noise, (4) techniques for metal artifact reduction, and (5) possibilities of spectral imaging. This article provides insight into our first experiences with photon-counting detector technology and shows results and images from experimental and clinical studies. · This review summarizes recent experimental and clinical studies in the field of photon-counting detector CT and musculoskeletal radiology.. · The potential of photon-counting detector technology in the field of musculoskeletal radiology includes improved spatial resolution, reduction in radiation dose, metal artifact reduction, and spectral imaging.. · PCD-CT enables imaging at lower radiation doses while maintaining or even enhancing spatial resolution, crucial for reducing patient exposure, especially in repeated or prolonged imaging scenarios.. · It offers promising results in reducing metal artifacts commonly encountered in orthopedic or dental implants, enhancing the interpretability of adjacent structures in postoperative and follow-up imaging.. · With its ability to routinely acquire spectral data, PCD-CT scans allow for material classification, such as detecting urate crystals in suspected gout or visualizing bone marrow edema, potentially reducing reliance on MRI in certain cases.. Bette S, Risch F, Becker J et al. Photon-counting detector CT - first experiences in the field of musculoskeletal radiology. Fortschr Röntgenstr 2024; DOI 10.1055/a-2312-6914.

Editorial: Deep learning approaches applied to spectral images for plant phenotyping

Editorial: Deep learning approaches applied to spectral images for plant phenotyping

Infrared Spectral Emissivity Dynamics of Surfaces Under Water Condensation

AbstractWater condensation on a surface strongly affects its effective emissivity, especially in the atmospheric window, a wavelength range essential for outdoor applications related to energetically passive cooling and heating. The evolution of emissivity of a silicon surface during dropwise and filmwise water deposition is studied. The evolution of the spectral radiative properties shows that the increase in effective emissivity due to the growth of a droplet pattern is steeper than for a growing water film of equivalent thickness. The change of surface emissivity takes place in the first moments of condensation where droplets as small as 10 µm drastically impact the reflectance of the pristine surface. The upper limit of effective emissivity is reached for a droplet radius or film thickness of 50 µm. During dropwise condensation, effective emissivity is weighted by the drop surface coverage and then remains within an asymptotic maximum value of 0.8, while in case of filmwise condensation, it is shown to reach 0.9 corresponding to water emissivity. Micrometer‐scale spatially‐resolved infrared spectral images enable to correlate the spatial variation of spectral properties to the droplet size and localization. Such findings are of interest to the implementation of moisture‐controlled emissivity tuning and radiative sky‐coolers for dew harvesting.

A clinical-radiomics nomogram based on spectral CT multi-parameter images for preoperative prediction of lymph node metastasis in colorectal cancer.

To develop a clinical-radiomics nomogram based on spectral CT multi-parameter images for predicting lymph node metastasis in colorectal cancer. A total of 76 patients with colorectal cancer and 156 lymph nodes were included. The clinical data of the patients were collected, including gender, age, tumor location and size, preoperative tumor markers, etc. Three sets of conventional images in the arterial, venous, and delayed phases were obtained, and six sets of spectral images were reconstructed using the arterial phase spectral data, including virtual monoenergetic images (40keV, 70keV, 100keV), iodine density maps, iodine no water maps, and virtual non-contrast images. Radiomics features of lymph nodes were extracted from the above images, respectively. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select features. A clinical model was constructed based on age and carcinoembryonic antigen (CEA) levels. The radiomics featuresselected were used to generate a composedradiomics signature (Com-RS). A nomogram was developed using age, CEA, and theCom-RS. The models' prediction efficiency, calibration, and clinical application value were evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis, respectively. The nomogram outperforms the clinical model and the Com-RS (AUC = 0.879, 0.824). It is well calibrated and has great clinical application value. This study developed a clinical-radiomics nomogram based on spectral CT multi-parameter images, which can be used as an effective tool for preoperative personalized prediction of lymph node metastasis in colorectal cancer.

Standardizing technical parameters and terms for abdominopelvic photon-counting CT: laying the groundwork for innovation and evidence sharing.

Photon-counting detector CT (PCD-CT) is a new technology that has multiple diagnostic benefits including increased spatial resolution, iodine signal, and radiation dose efficiency, as well as multi-energy imaging capability, but which also has unique challenges in abdominal imaging. The purpose of this work is to summarize key features, technical parameters, and terms, which are common amongst current abdominopelvic PCD-CT systems and to propose standardized terminology (where none exists). In addition, user-selectable protocol parameters are highlighted to facilitate both scientific evaluation and early clinical adoption. Unique features of PCD-CT systems include photon-counting detectors themselves, energy thresholds and bins, and tube potential considerations for preserved spectral separation. Key parameters for describing different PCD-CT systems are reviewed and explained. While PCD-CT can generate multi-energy images like dual-energy CT, there are new types of images such as threshold images, energy bin images, and special spectral images. The standardized terms and concepts herein build upon prior interdisciplinary consensus and have been endorsed by the newly created Society of Abdominal Radiology Photon-counting CT Emerging Technology Commission.

Global Inversion of Lunar Surface Oxides by Adding Chang’e-5 Samples

The chemical distribution on the lunar surface results from the combined effects of both endogenic and exogenic geological processes. Exploring global maps of chemical composition helps to gain insights into the compositional variation among three major geological units, unraveling the geological evolution of the Moon. The existing oxide abundance maps were obtained from spectral images of remote sensing and geochemical data from samples returned by Apollo and Luna, missing the chemical characteristics of the Moon’s late critical period. In this study, by adding geochemical data from Chang’e (CE)-5 lunar samples, we construct inversion models between the Christiansen feature (CF) and oxide abundance of lunar samples using the particle swarm optimization–extreme gradient boosting (PSO-XGBoost) algorithm. Then, new global oxide maps (Al2O3, CaO, FeO, and MgO) and Mg# with the resolution of 32 pixels/degree (ppd) were produced, which reduced the space weathering effect to some extent. The PSO-XGBoost models were compared with partial least square regression (PLSR) models and four previous results, indicating that PSO-XGBoost models possess the capability to effectively describe nonlinear relationships between CF and oxide abundance. Furthermore, the average contents of our results and the Diviner results for 21 major maria demonstrate high correlations, with R2 of 0.95, 0.82, 0.95, and 0.86, respectively. In addition, a new Mg# map was generated, which reveals different magmatic evolutionary processes in the three geologic units.

Fast computational approach with prior dimension reduction for three-dimensional chemical component analysis using CT data of spectral imaging.

Spectral image (SI) measurement techniques, such as X-ray absorption fine structure (XAFS) imaging and scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) or electron energy loss spectroscopy (EELS), are useful for identifying chemical structures in composite materials. Machine-learning techniques have been developed for automatic analysis of SI data, and their usefulness has been proven. Recently, an extended measurement technique combining SI with a computed tomography (CT) technique (CT-SI), such as CT-XAFS and STEM-EDS/EELS tomography, was developed to identify the three-dimensional (3D) structures of chemical components. CT-SI analysis can be conducted by combining CT reconstruction algorithms and chemical component analysis based on machine learning techniques. However, this analysis incurs high computational costs owing to the size of the CT-SI datasets. To address this problem, this study proposed a fast computational approach for 3D chemical component analysis in an unsupervised learning setting. The primary idea for reducing the computational cost involved compressing the CT-SI data prior to CT computation and performing 3D reconstruction and chemical component analysis on the compressed data. The proposed approach significantly reduced the computational cost without losing information about the 3D structure and chemical components. We experimentally evaluated the proposed approach using synthetic and real CT-XAFS data, which demonstrated that our approach achieved a significantly faster computational speed than the conventional approach while maintaining analysis performance. As the proposed procedure can be implemented with any CT algorithm, it is expected to accelerate 3D analyses with sparse regularized CT algorithms in noisy and sparse CT-SI datasets.

Optical-digital joint design of multi-order diffractive lenses for lightweight high-resolution computational imaging

Multi-order diffraction lenses can focus different wavelengths at the same focal point by utilizing various diffraction orders. However, due to material dispersion, the focal position shifts when the incident wavelength moves away from the primary wavelength, resulting in limited spectral bandwidth. This paper presents a computationally optimized multi-order diffractive lens design and a related image reconstruction algorithm, enabling broadband high-resolution imaging with a single element. We introduce a new type of lens, the Spectral Multi-Order Diffractive Lens (SMODLs), which redistributes the energy across different diffractive orders to achieve relatively high focusing performance within specified wavelength bands. Subsequently. The relatively sharp spectral images generated by SMODL within these specified bands serve as priors for reconstructing images across the full spectrum. A prototype lens with a 40 mm aperture and a 320 mm focal length was designed and fabricated. The prior images' wavebands were designated as 525 nm to 575 nm and 675 nm to 725 nm, with peak Strehl ratios of 0.47 and 0.35, respectively. Finally, grayscale images within the 500∼800 nm wavelength range were reconstructed. Experimental results confirm the effectiveness of our approach, achieving high-resolution imaging across a 300 nm bandwidth with a single diffractive element.

Ultra-high-speed four-dimensional hyperspectral imaging

We propose, to the best of our knowledge, a novel deep learning–enabled four-dimensional spectral imaging system composed of a reflective coded aperture snapshot spectral imaging system and a panchromatic camera. The system simultaneously captures a compressively coded hyperspectral measurement and a panchromatic measurement. The hyperspectral data cube is recovered by the U-net-3D network. The depth information of the scene is then acquired by estimating a disparity map between the hyperspectral data cube and the panchromatic measurement through stereo matching. This disparity map is used to align the hyperspectral data cube and the panchromatic measurement. A designed fusion network is used to improve the spatial reconstruction of the hyperspectral data cube by fusing aligned panchromatic measurements. The hardware prototype of the proposed system demonstrates high-speed four-dimensional spectral imaging that allows for simultaneously acquiring depth and spectral images with an 8 nm spectral resolution between 450 and 700 nm, 2.5 mm depth accuracy, and a 1.83 s reconstruction time.

Overview on Spectral Analysis Techniques for Gamma Ray Spectrometry

Gamma-ray spectrometry (GRS) is an exploration technology that distinguishes itself from other non-contact sensing technologies because it provides information from 30 to 50 cm below the ground. This technology has evolved through three significant turning points in mapping output. The first turning point, in the 1960s-1970s, was the transition from U concentration maps to weathered zoning maps utilizing K or eTh. The second turning point, occurring from the 1980s to 1990s, was marked by the application of radionuclide mapping to assess radioactive contamination. A third turning point, in the early 2000s, was the development of soil maps for precision agriculture, supported by the free statistics software R. This paper reviews advances in gamma-ray spectrometry spectral analysis since 2000. Traditionally, the gamma-ray spectrum is depicted as a two-dimensional graph with energy on the horizontal axis and counts on the vertical axis. The NASVD and MNF methods, developed around 2000, necessitate a reevaluation of this concept. By conducting principal component analysis of the gamma-ray spectrum in hyperspace, these techniques have unveiled new spectra, such as ground and sky spectra, and have facilitated the removal of noise components from the gamma-ray spectrum. Naturally occurring gamma-ray spectra typically exhibit energies ranging from 0.04 to 3 MeV. Observations from fusion reactors measure energies up to 20 MeV for diagnostics of nuclear plasma. These spectra may yield valuable insights when applied to innovative statistical analysis techniques. A comprehensive spectral analysis method developed in the early 2000s has demonstrated the potential to extract a variety of information beyond window nuclides, previously unexplored. The regression coefficient plots from the PLSR regression model have revealed novel spectral images. This model is set to influence future research on GRS by expanding the number of objectives and covariates. The innovative calibration method for full-spectrum analysis, which assesses different concentration areas, has proven that calibration is achievable even in the absence of a calibration pad. It is expected to become a formidable approach for spectrum analysis in the upcoming period.

The time-series GF-1 WFV data monitoring of sugarcane using a Random Forest Algorithm in South China

Abstract. There is large distribution of sugarcane growth in south China which is play an important role of sugar industry. Remote sensing technology is used in sugarcane monitoring for large areas. However, the optical satellite data coverage is influenced by the rainy weather especially in the grand growth period of sugarcane. GF-1 WFV has widely swath 800km and short revisit time which is ideal data for this study area. In this paper, the random forest model was chosen to get a precise classification result of sugarcane based on time-series band value and 5 spectral indexes image is 89.73% and the Kappa coefficient is 0.65 which is satisfied the overall extraction of sugarcane for large area is the southern China. Furthermore, the decision tree classification was chosen as a comparative experience research.