Search Results (6,007)

Hyperspectral image classification holds significant applications across multiple domains due to its rich spectral and spatial information. However, it faces challenges such as spectral variation within the same object, spectral variation across different objects, and noise interference. Existing methods like convolutional neural networks perform well in local feature extraction but inadequately model long-range dependencies. While Transformers can capture global relationships, they struggle to effectively coordinate spectral and spatial information modeling. To address these limitations, this paper proposes a dual-branch collaborative Transformer network (PST-Net). This architecture integrates an adaptive spectral–spatial token (ASST) module, a Parallel Attention-Augmented lightweight CNN branch (PA-SSCNN), and a collaborative fusion layer. The ASST constructs joint representation tokens through local spectral smoothing and learnable spatial embedding. PA-SSCNN employs 3D-2D cascaded convolutions and channel–spatial attention mechanisms to enhance local texture and spatial feature extraction; CHIB enables deep interaction and synergistic fusion of dual-branch features across different levels and scales. Experimental results demonstrate that with only 2% labeled samples, PST-Net achieves overall classification accuracies of 96.31%, 96.59%, 95.27%, and 89.06% on the Salinas and Whuhh, and the two complex urban scene datasets Qingyun and Houston. Especially in fine-grained categories and complex scenes, it exhibits strong robustness. The ablation experiment further validated the effectiveness and complementarity of each module. This study provides an efficient collaborative modeling framework for hyperspectral image classification that balances global dependencies and local details. Full article

Rapid, standardised discrimination of soil taxonomic units remains challenging when relying solely on conventional field descriptions and laboratory analyses, particularly at high sampling densities. This study evaluated whether proximal spectroscopy and hyperspectral imaging can support the classification of Brazilian Soil Classification System (SiBCS) suborders and pedogenetic horizons when surface and subsurface spectra are treated separately. Six intact soil monoliths (0.12 × 1.60 m) were collected in Paraná State, southern Brazil, representing one Organossolo (Ooy), three Latossolos (LVd, LVd1, and LVd2) and two Argissolos (PVAd and PVd). For each monolith, 800 spectra were acquired per sensor with a non-imaging VIS–NIR–SWIR spectroradiometer (350–2500 nm), and 800 spectra per sensor per monolith were extracted from the SWIR hyperspectral images (1200–2450 nm). Principal component analysis (PCA) was used to summarise spectral variability, and supervised classification was performed via k-nearest neighbours, random forest, decision tree and gradient boosting for suborders (10-fold cross-validation), and a neural network was used for within-profile horizon classification. PCA indicated that most of the spectral variance was captured by a dominant axis, with clearer separation among suborders in the SWIR space than in the full VIS–NIR–SWIR range. With respect to suborder classification, subsurface spectra outperformed surface spectra, and SWIR outperformed VIS–NIR–SWIR: the best accuracies were 0.96 for subsurface SWIR (gradient boosting; AUC = 0.99; MCC = 0.95) and 0.89 for surface SWIR (k-nearest neighbours; AUC = 0.98; MCC = 0.87). Within-profile horizon classification via VIS–NIR–SWIR achieved accuracies of 0.84–0.97 with the Neural Network, with most misclassifications occurring between adjacent horizons. Overall, subsurface SWIR information provided the most reliable basis for taxonomic discrimination, whereas horizon classification was feasible but reflected gradual spectral transitions along the profile. Full article

This study evaluates the effectiveness of laser cleaning techniques for the non-contact removal of unwanted deposits from the surface of contemporary urban mural paintings. Two sets of mock-up samples, painted with popular graffiti spray paints on lime-based plaster, and artificially contaminated, were subjected to various cleaning procedures using Nd:YAG lasers operated in Q-switched (QS), long Q-switched (LQS) or short free-running mode (SFR). A multi-analytical approach—including X-ray fluorescence spectroscopy (XRF), Fourier-transform infrared spectroscopy (FTIR), colorimetry, and hyperspectral imaging (HSI)—was used to identify pigments and binders, and to evaluate cleaning efficiency and pigment preservation. XRF and FTIR were useful in understanding the composition of the sprays, while colorimetric ΔE values quantified cleaning efficiency and potential damage, and hyperspectral reflectance and LSU (linear spectral unmixing) abundance maps provided spatial distribution insights into contaminant removal and pigment preservation. The results demonstrate that laser cleaning effectiveness and selectivity are strongly dependent on the operational regime and fluence. In particular, long Q-switched laser irradiation at moderate fluence levels achieved effective contaminant removal with minimal chromatic and chemical alteration of the original paint layers. These findings support the development of tailored, sustainable, and non-contact laser cleaning protocols for the conservation of contemporary urban murals and contribute to the establishment of objective, multi-parameter criteria for evaluating cleaning outcomes in street art conservation. Full article

Early-stage fungal contamination in maize kernels is difficult to identify visually and it can cause severe quality and safety risks during storage and transportation. Short-wave infrared (SWIR) hyperspectral imaging offers a rapid, non-destructive approach by capturing chemical information related to water, proteins, and lipids. This study investigates the early detection and classification of Gibberella zeae contamination in maize kernels using SWIR hyperspectral imaging combined with machine learning. Two maize varieties were artificially inoculated and cultured under controlled conditions, followed by hyperspectral data collection over six contamination stages. Various preprocessing techniques including standard normal variate (SNV), second derivative (SD), multiplicative scatter correction (MSC), and derivatives were evaluated to enhance data quality. Feature wavelength selection was performed using successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE), significantly reducing redundancy and improving classification performance. Multiple models, including linear discriminant analysis (LDA), multilayer perceptron (MLP), support vector machine (SVM), a convolutional neural network (CNN), long short-term memory (LSTM) network, and a hybrid architecture Transformer that integrated a CNN, a LSTM network, and a Transformer (abbreviated as CLT), were constructed for both binary (healthy vs. contaminated) and multiclass classification tasks. Specifically, the multiclass task consisted of six contamination stages corresponding to contamination time from Day 0 to Day 5. The best binary classification task accuracy of 100% was achieved using SNV-preprocessed data with the MLP model. For multiclass classification task, the SD-preprocessed LDA model reached a test accuracy of 92.56%. Combined with appropriate preprocessing, feature selection and modeling, these results demonstrate that hyperspectral imaging is a powerful tool for the non-destructive, early-stage identification of fungal contamination in maize kernels, offering strong support for food safety and quality monitoring. Full article

Precision Agriculture (PA) is increasingly applied to nutraceutical cropping systems, where agronomic productivity must be integrated with the stabilization of phytochemical quality and environmental sustainability. This structured narrative review synthesizes scientific evidence (primarily 2010–2025) on the use of Unmanned Aerial Vehicle (UAV)-based multispectral and thermal sensing, LiDAR-derived canopy characterization, Internet of Things (IoT) monitoring, and artificial intelligence (AI)-driven analytics in medicinal, aromatic, and functional crops. The literature indicates that PA enhances high-resolution monitoring of crop–environment interactions, supporting site-specific irrigation, nutrient management, and stress detection. Under validated conditions, these interventions are associated with improved yield stability, resource-use efficiency, and modulation of secondary metabolite accumulation. However, reported outcomes vary substantially across species, agroecological contexts, and experimental scales, and most studies remain plot-scale or pilot-scale, limiting large-scale generalization. Moringa oleifera Lam. is examined as a model species for Mediterranean and semi-arid systems. Evidence suggests that integrated spectral, structural, and environmental monitoring can support optimized irrigation scheduling, canopy uniformity, and phytochemical consistency. Nonetheless, genotype-specific calibration, multi-season validation, standardized metabolomic benchmarking, and cross-regional transferability remain significant research gaps. Overall, PA represents a scientifically promising but still maturing framework for nutraceutical agriculture. Future progress will require rigorous multi-site validation, improved model robustness, standardized sustainability metrics, and comprehensive economic assessments to ensure scalability and long-term impact. Full article

Rice undergoes rapid growth and exhibits a high demand for nutrients during the tillering and booting stages. SPAD readings, which reflect relative leaf chlorophyll status, and leaf nitrogen content (LNC) are key indicators of plant nutritional status, directly influencing photosynthetic efficiency and biomass accumulation, while plant height (PH) reflects canopy structure and nutrient availability. Establishing quantitative relationships among these traits at key growth stages is essential for stage-specific precision rice management. In this study, Unmanned Aerial Vehicle (UAV) hyperspectral imagery and ground-truth measurements of SPAD, LNC, and PH were collected from rice fields in Qingbaijiang District, Chengdu, China. Twelve vegetation indices (VIs) were calculated, and three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR)—were employed to develop stage-specific retrieval models. A stage-specific modeling framework integrating PH with hyperspectral data was developed to statistically enhance estimation accuracy at the tillering and booting stages. The optimal models for SPAD readings and LNC achieved R² values of 0.916 and 0.936, respectively. The results indicate that integrating canopy structural information with hyperspectral features can improve the estimation accuracy of SPAD-related chlorophyll indicators and nitrogen status in rice. Under the controlled field conditions of this study, the proposed framework provides a plot-scale proof-of-concept demonstration for UAV-based stage-specific nitrogen monitoring. Full article

Vision Transformers (ViTs), owing to their strong capability in modeling global contextual dependencies, have been widely adopted in hyperspectral image unmixing (HU). However, standard ViTs process images by partitioning them into non-overlapping patches, which disrupts spatial continuity at the pixel level and neglects the fine-grained structural relationships among pixels within local regions. Consequently, effectively capturing the detailed spatial–spectral features required for accurate unmixing remains challenging. Furthermore, the high computational complexity of global self-attention and its sensitivity to noise limit the applicability of conventional Transformers to HU. To address these issues, we propose a spatial–spectral similarity guided Transformer-in-Transformer (SSTNT) framework. The proposed network adopts a modified TNT architecture, in which the inner Transformer employs a linear self-attention (LSA) mechanism to efficiently exploit pixel-level local features within sliding windows, while the outer Transformer preserves global attention to aggregate contextual information, thereby forming a cooperative local–global optimization scheme. Furthermore, a lightweight spatial–spectral similarity module is introduced to enhance the modeling of neighborhood structures. Finally, spectral reconstruction is achieved through a trainable endmember decoder and a normalized abundance estimation module. Extensive experiments conducted on both synthetic and real hyperspectral datasets demonstrate the effectiveness and robustness of the proposed method. Full article

Hyperspectral unmixing (HU) is fundamental for conducting quantitative analyses in remote sensing, yet existing methods face a persistent tradeoff between model performance and physical interpretability. Although deep learning models achieve superior performance, even “gray-box” models that incorporate physical constraints still suffer from an intrinsically opaque decision-making process, which hinders their trustworthiness in critical applications. To address this challenge, this paper introduces a theory-guided unmixing framework aimed at enhancing mechanistic interpretability called the sparse and subspace-attentive transformer unmixing network (SSTU-Net). Unlike heuristic architectures, SSTU-Net is rigorously derived from the first principles of sparse rate reduction (SRR) theory. Its core modules—the multi-head subspace self-attention (MSSA) and the iterative shrinkage-thresholding algorithm (ISTA)—directly implement the essential mathematical steps of information compression and sparsification within the SRR theory, respectively. Extensive experiments on both synthetic and real hyperspectral datasets demonstrate that SSTU-Net achieves competitive performance compared to representative state-of-the-art methods—including advanced autoencoder-based networks (e.g., CyCU-Net and DAAN) and recent transformer-based unmixing architectures (e.g., DeepTrans and MAT-Net)—while strictly adhering to theoretically predicted evolutionary trajectories. More importantly, a series of specifically designed structural interpretability validation experiments mechanistically confirm the theoretically predicted behaviors, such as layer-wise information compression, feature sparsification, and subspace orthogonalization. These results reveal the internal working mechanisms of SSTU-Net, validating the feasibility and significant potential of our principled theory-guided framework for developing high-performance and trustworthy intelligent models in remote sensing. Full article

Coloured and variegated leaves are common in shade-tolerant ornamentals. However, it remains unclear whether their photosynthetic performance is determined mainly by pigment abundance or by the organisation of chloroplasts and thylakoids. We tested this in three Epipremnum aureum phenotypes (‘Neon’, ‘Golden’ and ‘Jade’) that share a genetic background but contrast in leaf colour, chloroplast density and thylakoid membrane abundance. Plants were grown in a greenhouse and assessed by hyperspectral and thermal imaging, infrared gas exchange analysis, chlorophyll a fluorescence measurements, and structural, ultrastructural and biochemical analyses. Traits were integrated by principal component analysis, with the quantum yield of CO₂ assimilation per absorbed photon (αCO_2,abs) as the response variable. ‘Neon’ leaves had high specific leaf area and approximately 55% lower maximum Rubisco carboxylation (Vc_MAX) and electron transport capacity (J_MAX) than ‘Jade’, as well as reduced chloroplast and thylakoid abundance and warmer canopies, despite carotenoid enrichment. JIP-test parameters and fluorescence light–response curves showed high absorption and dissipation per PSII reaction centre, elevated excitation pressure, modest non-photochemical quenching (NPQ), low αCO_2,abs, small carbohydrate pools and low intrinsic water-use efficiency. ‘Jade’ leaves developed thick mesophyll with dense chloroplast populations, extensive thylakoid networks, highest NPQ, cool canopies and large carbohydrate reserves, whereas ‘Golden’ leaves combined thin laminae and intermediate chloroplast–thylakoid organisation with early light saturation of CO₂ assimilation and the highest intrinsic water-use efficiency. Principal component analysis revealed a structural axis of chloroplast and thylakoid organisation that better predicted αCO_2,abs, net carbon gain and canopy temperature than pigment abundance. In variegated E. aureum, ‘photon economy’ is therefore governed primarily by chloroplast and thylakoid membrane organisation and abundance rather than by carotenoid accumulation. Full article

Deep learning-based crop mapping from hyperspectral satellite data offers immense potential for capturing subtle phenological differences, yet leveraging sparse time series remains a major methodological challenge. This study evaluates the ability of the EnMAP sensor to identify nine major crop types in the intensive agricultural landscape of Southeastern Hungary. We utilized a limited time series (November, March, August) to benchmark two modeling strategies: a single-date dual-stream spatial–spectral 2D-CNN (DSS-2D) and a multi-temporal 3D-SE-ResNet. Model performance was assessed using parcel-level spatial cross-validation to ensure realistic accuracy estimates and reduce spatial autocorrelation bias. The results demonstrate that the DSS-2D model achieved superior single-date accuracy (OA > 97%), significantly outperforming pixel-based baselines. Furthermore, the multi-temporal 3D-SE-ResNet achieved a robust seasonal accuracy of 92.9%, effectively compensating for temporal sparsity by exploiting the deep spectral information of the SWIR domain. This study confirms that treating hyperspectral data as a 3D volume enables the extraction of phenological traits even from limited observations. These findings provide a strong proof-of-concept for the operational feasibility of future missions such as Copernicus CHIME for continental-scale food security monitoring. Full article

Wetlands, frequently termed the “kidneys of the Earth,” represent one of the most vital global ecosystems. Despite their limited spatial extent, plateau wetlands function as unique ecological units that play a pivotal role in the global carbon cycle, water resource regulation, and biodiversity conservation, while exhibiting acute sensitivity to climate change. Advances in remote sensing technology—characterized by macro-scale cover-age, temporal efficiency, and non-invasive operations—have established it as a corner-stone for the dynamic monitoring and analysis of these environments. This study presents a bibliometric synthesis of 2138 publications (1982–2024) retrieved from the Web of Science Core Collection. We systematically evaluated publication trajectories, international collaborative networks, disciplinary shifts, core journals, and the spatiotemporal evolution of research hotspots. Our findings reveal an exponential growth in scholarly output alongside a marked diversification of research fields. Geographically, research is predominantly clustered around the Tibetan Plateau, flanked by the Alps and the Himalayas, with sparse representation in other regions. Future endeavors should prioritize underrepresented low-latitude and remote regions through strengthened international synergy and the integration of emerging technologies, such as UAVs and hyperspectral sensors. Full article

This paper investigated the feasibility of snapshot multispectral fluorescence imaging for nondestructive identification of cold stress in pepper plants. Fluorescence spectra were obtained by exciting the plant with a 405 nm ultraviolet LED. The plants were grown under three temperature conditions: 17 °C (control), 10 °C (moderate cold stress), and 5 °C (severe cold stress). Raw fluorescence spectra extracted from the demosaiced snapshot images were used as inputs for a deep-learning pipeline consisting of feature extraction, an encoder–decoder GRU, and a multilayer perceptron (MLP), and the results were compared with conventional machine learning classifiers, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and a Gaussian support vector machine (G-SVM). Tukey’s HSD test indicated that the proposed deep-learning model achieved the highest cross-validation accuracy and consistently produced superior classification metrics (accuracy of 85.7%, precision of 85.3%, recall of 85.3%, F1-score of 85.2). The trained model was further applied to hyperspectral cubes to generate classification maps; however, moderate misclassification was observed, consistent with the overall prediction performance. Full article

Nitrogen is one of the essential nutrient elements that affect rice growth, yield, and quality formation. Accurate and timely estimation of rice nitrogen status is fundamental for precision fertilization in agricultural fields. Hyperspectral remote sensing technology provides a promising approach for rapid and accurate acquisition of nitrogen status of rice in the field. However, traditional single-angle hyperspectral observations are easily disturbed by factors such as canopy structure, light direction, and background reflection, limiting their inversion accuracy and stability. This study is based on multi-angle polarimetric hyperspectral data obtained from an unmanned aerial vehicle platform. It extracts features from multi-angle polarimetric spectra based on three algorithms: successive projections algorithm (SPA), competitive adaptive reweighted sampling, and relevant features. The input weight and hidden layer bias of the extreme learning machine (ELM) model were optimized by the whale optimization algorithm (WOA) and caterpillar fungus optimization algorithm (CFO), taking the sensitive band of optimal viewing angle as input. Finally, an inversion model of rice canopy nitrogen content (CNC) based on multi-angle polarization hyperspectral data was established. The results demonstrate that the inversion results of the combination of SPA-(30°) + SPA-(45°) observation angles and feature selection methods are optimal, and multi-angle fusion significantly improves the model’s ability to characterize CNC, with higher stability and accuracy than single-angle modeling. The R² of CFO-ELM on the training set and test set reach 0.8553 and 0.8274, respectively, which is significantly better than the original ELM and WOA-ELM, becoming the optimal CNC inversion model in this study. The rice CNC inversion model based on multi-angle polarimetric hyperspectral data constructed in this study provides a specific reference for the rapid detection of rice CNC. Full article

Deep learning has demonstrated outstanding potential for the spectral super-resolution (S²R) reconstruction of multispectral images (MSIs). However, it is still a challenge to alleviate spectral distortion during S²R reconstruction. Given the superiority of a graph, a graph-regularized double-path interactive S²R network (GDIS²Net) consisting of two parallel branches is proposed to reconstruct hyperspectral images (HSIs) from MSIs. An interactive residual module is carefully schemed as the backbone of the S²R network to facilitate the feature interaction between the two branches, while an enhanced residual module is constructed for further feature fusion. In addition, a new loss function considering the spectral continuity is proposed to optimize the proposed GDIS²Net. Experimental analyses show that the proposed GDIS²Net outperforms state-of-the-art methods on both simulated and real datasets. Full article

To address the technical bottlenecks of strong background radiation interference and weak target signals in mid-wave infrared (MWIR) hyperspectral mineral detection over complex terrain, this paper proposes a “full-link background radiation suppression” methodological framework. A coupled illumination-terrain-atmosphere-sensor radiative transfer model is constructed to systematically quantify how multidimensional parameters—such as observation geometry, surface temperature, elevation, aerosol optical depth, and water vapor content—influence the target background radiation contrast. The findings reveal that daytime observation, lower surface temperature, higher altitude, dry atmosphere, and moderate solar and observation zenith angles are key factors for maximizing the signal-to-noise ratio. Comprehensive optimization analysis demonstrates that observations during midday in autumn and winter achieve optimal performance, with the target background relative contrast potentially enhanced by up to 6.29 times compared to unfavorable conditions such as summer nights. This work elucidates the physical mechanisms governing MWIR hyperspectral detection efficacy in complex scenarios, provides direct parameter-optimization strategies for intelligent mission planning of spaceborne imaging systems, and holds significant value for advancing mineral remote sensing from “passive acquisition” to “cognitive detection”. Full article