Search Results (150)

Hyperspectral imaging (HSI) provides rich spatial and spectral information for remote sensing, mineral exploration, and biomedical analysis, but its limited spatial resolution and sensor imperfections lead to blurred, noisy, and mixed-pixel observations. Addressing these degradations jointly—rather than sequentially—has been shown to improve physical interpretability, yet existing joint deblurring–unmixing methods rely primarily on hand-crafted regularizers that do not fully exploit spatial–spectral structure. Meanwhile, recent plug-and-play (PnP) approaches applied to HSI leverage deep priors but focus solely on either deconvolution or unmixing in isolation. To bridge this gap, we formulate the joint inverse problem of hyperspectral deblurring and spectral unmixing and propose, to our knowledge, the first plug-and-play framework tailored for this coupled task using the Alternating Direction Method of Multipliers (ADMM) and a pretrained deep denoiser (DnCNN) as an implicit PnP prior. Our method uses the natural splitting properties of ADMM to separate a physics-driven subproblem that enforces fidelity to the hyperspectral forward model, which includes linear mixing and blur under a linear, space-invariant convolution approximation, from the data-driven prior step. This synergy of model-based fidelity and learned spatial prior enables more accurate abundance estimates than those obtained with approaches relying solely on analytical regularizers. Experimental results on real hyperspectral datasets demonstrate that the proposed Plug-and-Play Joint Deconvolution and Unmixing (PnP-JDU) method outperforms conventional unmixing baselines, stand-alone PnP unmixing methods, and the Deblurring and Sparse Unmixing via the Alternating Direction Method with Total Variation (DSUnADM-TV) baseline in reconstruction and abundance accuracy metrics. Across the tested datasets and imaging conditions, PnP-JDU achieves lower RMSE, higher PSNR, lower reconstruction and abundance errors, and lower SAD values, while preserving fine spatial details and producing physically meaningful abundance maps. Full article

The spatial distribution of flammable tree species directly influences forest fuel structure and fire risk patterns. However, mixed pixels limit the ability of conventional classification methods to characterize continuous within-pixel variation in species composition, thereby constraining fine-scale forest mapping. To address this issue, this study developed a subpixel mapping framework for flammable tree species in Yajiang County, Sichuan Province, by integrating Sentinel-2 time-series data with a spectral mixing–unmixing strategy. Using 2019 Sentinel-2 time-series data and National Forest Inventory (NFI) data, temporal mixed samples with known abundance fractions were generated using a linear spectral mixing model. An XGBoost-based collaborative multi-regression framework was then applied to estimate the proportions of different tree-species endmembers within complex forest pixels. Quantitative evaluation using synthetic mixed samples showed that the model achieved stable unmixing performance across different random mixing scenarios. The best performance was obtained under the Mixed 2 scenario with a sample size of 250 K, reaching an R² of 0.821. The resulting maps revealed continuous spatial variation in the abundance and composition of flammable tree species. Mountain pine was the most widespread and dominant species, followed by spruce and mountain oak, whereas birch and fir mainly exhibited localized patchy distributions. An additional NFI-based categorical evaluation assessed the consistency of the final maps with real forest inventory records. The identification accuracies were 93.95% for pure stands and 91.22% for mixed stands, while the species classification accuracies were 87.28% for pure stands and 84.41% for dominant species in mixed stands. The proposed framework provides useful spatial information for regional forest fuel assessment and fire risk management. Full article

Hyperspectral unmixing is an important technique for mineral mapping because natural geological scenes commonly contain mixed pixels composed of multiple spectrally overlapping materials. In mineral environments, these mixtures are often intimate rather than purely areal, and nonlinear scattering effects may weaken the validity of linear mixing assumptions. Although autoencoder-based hyperspectral unmixing methods can jointly estimate endmembers and abundances in an unsupervised manner, they often suffer from insufficient physical constraints, unstable endmember learning, and limited geological interpretability. To address these issues, this study proposes a physically constrained and library-guided convolutional autoencoder for mineral hyperspectral unmixing. The method retains an interpretable linear reconstruction backbone while introducing a Hapke-consistency regularization term to incorporate physically motivated nonlinear scattering behavior during endmember optimization. In addition, a library-aware endmember anchor module is designed to improve initialization quality, reduce endmember drift, and guide optimization toward spectrally meaningful solutions. The proposed method was evaluated on both simulated hyperspectral datasets and real airborne SASI data. On the simulated datasets, the method achieved improved endmember spectral fidelity and lower abundance estimation error than several representative autoencoder-based baselines, with the advantage being more evident under nonlinear mixing conditions. Ablation experiments further showed that the Hapke-consistency term mainly improved physical plausibility, whereas the anchor module enhanced optimization stability and spectral consistency. On the real airborne dataset, the proposed method produced endmember spectra that were more consistent with field and laboratory mineral references and generated spatially more coherent abundance maps. These results indicate that incorporating physically motivated constraints and mineral-library priors into deep autoencoder frameworks can improve the robustness and interpretability of mineral hyperspectral unmixing. The proposed framework provides a practical direction for hyperspectral mineral mapping in mixed and spectrally complex geological environments. Full article

Hyperspectral remote sensing is increasingly utilized due to its high spectral resolution and broad observational capabilities, and hyperspectral unmixing aims to decompose mixed pixels into their constituent endmembers with corresponding classes. The core research directions in this area include how to construct a proprietary spectral library and how to optimize the corresponding abundance maps. However, due to the influence of complex terrain and variable illumination conditions, hyperspectral images (HSI) exhibit significant spectral variability (SV), which undermines the performance of traditional unmixing methods. In the paper, we propose an SV and class-constrained diffusion model (SVCDM) for unsupervised hyperspectral unmixing that integrates endmember extraction and abundance optimization. Specifically, a Dirichlet-based variational autoencoder is employed to construct a spectral library from the original HSI with a class constraint and prior distribution, which subsequently guide a conditional diffusion model to learn the distribution. During the reverse process, the endmembers are iteratively updated at each time step, enhancing diversity while ensuring class consistency. Subsequently, the endmember matrix is synthesized with the original HSI to optimize the abundance maps under the linear mixing assumption. The proposed SVCDM effectively mitigates the impact of SV induced by imaging characteristics. Experimental results demonstrate that the SVCDM achieves a root mean square error (RMSE) of 0.0371 for abundance maps on a synthetic dataset and a spectral angle mapper (SAM) for endmembers of 0.0309 on the Samson dataset, outperforming existing state-of-the-art hyperspectral unmixing methods on both synthetic and real datasets. Full article

The performance of remote sensing-based mineral alteration extraction is significantly restricted in the vegetation area. Spectral unmixing is one of the effective methods to address the vegetation problem during mineral alteration extraction. However, the spectral curves of different tree species vary a lot; if multiple tree species are regarded as a whole during the spectral unmixing stage, the proportions of vegetation would be estimated with more errors. The purpose of this study was to verify the effects of dominant tree species classification on spectral unmixing and reconstruction, and to apply the proposed method to the mineral alteration extraction practice. To accomplish this, the Shabaosi gold deposit region in Mohe City, China, with an area of 650 km², was selected as the study area. Firstly, reference spectral curves, GaoFen-1/6 (GF-1/6) satellite imageries, ZiYuan-1F (ZY-1F) satellite imageries, Sentinel-1B satellite synthetic aperture radar (SAR) data, the ALOS digital elevation model (DEM), and sub-compartment dominant tree species data were collected; subsequently, simulated mixed-pixel reflectance images of ZY-1F, reflectance images of GF-1/6, ZY-1F, backscattering data of Sentinel-1B, slope, aspect, and 5484 tree species samples were derived from the collected data. Secondly, to verify the effect of dominant tree species classification on mineral alteration extraction, the reference spectra of pine, oak, goethite, and kaolinite were used to construct a simulated ZY-1F mixed-pixel image, and spectral unmixing and reconstruction experiments were conducted. Thirdly, fourteen independent variables were selected from the derived data, five dominant tree species classification models were trained and tested using tree species samples via the ResNet50 algorithm, and the pine- and birch-dominated parts were segmented from the ZY-1F images. Fourthly, minimum noise fraction (MNF), pixel purity index (PPI), n-dimensional visualizer auto-clustering, and spectral angle mapper (SAM) methods were separately applied to the pine- and birch-dominated parts of ZY-1F images to extract and identify endmembers; subsequently, the fully constrained least squares (FCLS) and linear spectral unmixing (LSU) methods were separately applied to the pine- and birch-dominated parts to estimate endmember proportions and generate spectrally reconstructed ZY-1F images. Fifthly, the pine- and birch-dominated parts of spectrally reconstructed ZY-1F images were mosaiced, and the SAM was utilized to extract mineral alteration in the study area. The result showed that in the spectral unmixing and reconstruction experiment, the spectral reconstruction error declined from 0.0594 (simulated ZY-1F image without segmentation) to 0.0292 and 0.0388 (simulated ZY-1F image that was segmented by pine- and oak-dominated parts), suggesting that dominant tree species classification could improve the accuracy of spectral unmixing and reconstruction and help obtain a more reliable mineral alteration extraction result. In the study area, the tested overall accuracies (OA) and Kappa coefficients of the five dominant tree species classification models were 0.75 ± 0.03 and 0.50 ± 0.05, respectively, suggesting that conducting dominant tree species classification was feasible in dense vegetation areas and could facilitate mineral alteration extraction. After segmenting the ZY-1F image by pine- and birch-dominated parts and spectral reconstruction, eight main types of alteration, including kaolinite, vesuvianite, montmorillonite, rutile, limonite, mica, sphalerite, and quartz, were identified, and nine mineral alteration areas (MA) were delineated accordingly. Full article

Accurate mapping of lunar mineral distributions is essential for understanding the Moon’s origin and evolution and for enabling future in situ resource utilization (ISRU). Yet mineralogical inversion from orbital hyperspectral observations remains challenging due to limited spatial resolution, complex photometric conditions, and sparse returned samples. We present PGU-Net, a Hapke physics-guided deep autoencoder for nonlinear blind unmixing of lunar hyperspectral data. The encoder adopts a dual-attention design to enhance discriminative spectral features. The decoder performs linear mixing in the SSA domain and then reconstructs reflectance through a lightweight nonlinear module, while physics-consistent losses encourage radiative-transfer plausibility. Experiments on a synthetic lunar regolith dataset demonstrate that PGU-Net achieves consistently lower endmember SAD and abundance aRMSE than representative baselines across multiple noise levels. Additional validations on the terrestrial AVIRIS Cuprite benchmark and on Moon Mineralogy Mapper (${\mathrm{M}}^3$) observations near the Chang’e-5 (CE-5) and Chang’e-6 (CE-6) landing regions yield physically plausible mineral distributions. The ${\mathrm{M}}^3$ maps are broadly consistent with Kaguya MI mineral products and returned-sample constraints, supporting the practicality of PGU-Net for lunar mineralogical mapping. Full article

This work presents a portable polarimetric multispectral imaging (PMSI) system operating in the visible to shortwave infrared range (VNIR–SWIR: 400–1700 nm) and its application to target detection, discrimination from aquatic backgrounds, and polymer identification. The instrument integrates two synchronized cameras with motorized bandpass filters and piezoelectric polarization control, enabling the acquisition of 48 wavelength–polarization measurements per capture. This configuration allows the extraction of both intensity-based and polarimetric features, including the degree of linear polarization (DoLP). A complete radiometric and polarimetric calibration framework is implemented, encompassing system response characterization, polarization-dependent gain correction, and reflectance normalization under variable illumination. Experiments conducted on a representative set of 16 polymer materials show that polarimetric information consistently improves class separability compared to intensity-only features, with a mean gain of 6.9 (95% CI: 6.35–8.47). Although the correlation between intensity- and DoLP-based separability is moderate (r = 0.44), the results indicate complementary identification capability. Material recoverability was further evaluated using spectral unmixing techniques (VCA, N-FINDR, and PPI), with VCA offering the best accuracy–complexity trade-off on the calibrated Stokes reflectance dataset. Despite these gains, identification among chemically similar polyethylene variants remains challenging due to limited spectral and polarimetric contrast. An underwater detectability study under natural illumination reveals strong wavelength-dependent constraints: SWIR penetration is limited to 4 cm, whereas VNIR bands (430–550 nm) preserve detectability up to 20 cm, with DoLP enhancing edge visibility. These results motivate future validation in more complex aquatic conditions and with increased spectral dimensionality. 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

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

Soil heavy metal contamination in mining areas poses a serious environmental challenge, requiring monitoring approaches with both wide coverage and high accuracy. Hyperspectral remote sensing provides an effective solution, yet its performance in complex mining environments is often limited by mixed-pixel effects and nonlinear spectral responses. To address these issues, this study proposes a Physically-Constrained Collaborative Endmember Extraction (PCCEE) framework that integrates spectral unmixing with machine learning for multi-element inversion. Using Gaofen-5 hyperspectral imagery, a collaborative workflow combining Pixel Purity Index (PPI), Vertex Component Analysis (VCA), and prior-spectral-constrained Spectral Angle Mapper (SAM) was developed to improve endmember purity and physical interpretability. Among three unmixing models (LMM, NMF, and SVR), the Linear Mixing Model achieved the best balance between accuracy and efficiency. Random Forest regression using retrieved abundances enabled high-accuracy inversion of eight heavy metals (mean R² = 0.85). Spatial analysis revealed significant co-enrichment of Pb, Cd, and Zn related to sulfide weathering, while PCA distinguished compound and independent pollution sources. The proposed PCCEE framework effectively mitigates mixed-pixel interference and provides a transferable approach for heavy metal monitoring and risk assessment in complex mining environments. Full article

The mineral-mapping capability of three spaceborne sensors with different spatial and spectral resolutions, the Environmental Mapping and Analysis Program (EnMap), Sentinel-2, and World View-3 (WV3), is assessed regarding bauxite mining wastes in Amphissa, Greece, with validation based on ground samples. We applied the well-established Linear Spectral Unmixing (LSU) and Spectral Angle Mapping (SAM) classification techniques utilizing endmembers of two established spectral libraries and incorporated ground data through geochemical and mineralogical analyses, X-ray fluorescence (XRF), Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS), and X-ray Diffraction (XRD), to assess classification performance. The main lithologies in this area are bauxites and limestones; therefore, aluminum oxyhydroxides, calcite, and iron oxide minerals were the dominant phases as indicated by the XRF/XRD results. Almost all target minerals were mapped with the three sensors and both methods. The performance of EnMap is affected by its coarser spatial resolution despite its higher spectral resolution using these methods. Sentinel-2 is most effective for mapping iron-bearing minerals, particularly hematite, due to its higher spatial resolution and the presence of diagnostic iron oxide absorption features in the VNIR. World View 3 Shortwave Infrared (WV3-SWIR) performs better when mapping calcite, benefiting from its eight SWIR spectral bands and very high spatial resolution (3.7 m). Hematite and calcite yield the highest accuracy, especially with SAM, indicating 0.80 for Sentinel-2 (10 m) for hematite and 0.87 for WV3-SWIR (3.7 m) for calcite. AlOOH shows higher accuracy with SAM, ranging from 0.57 to 0.80 across the sensors, while LSU shows lower accuracy, ranging from 0.20 to 0.73 across the sensors. This study showcases each sensor’s ability to map minerals while also demonstrating that spectral coverage and the spatial and spectral resolution, as well as the characteristics of the selected endmembers, exert a critical influence on the accuracy of mineral mapping in mine waste. Full article

The advancement of hyperspectral remote sensing technology has enhanced the ability to assess and characterize land cover in complex ecosystems. In this study, a linear spectral unmixing algorithm was applied to NEON hyperspectral imagery in 2018 and 2022 to quantify the fractional abundance of dominant land cover classes, namely herbaceous vegetation, mixed forbs, and bare soil, across the Marvin Klemme Experimental Rangeland in Oklahoma. UAV imagery acquired during the 2023 field campaign provided high resolution reference data for model training. The LSU results revealed a decline in herbaceous cover from 16.02 ha to 11.56 ha and an expansion of bare soil from 3.37 ha to 6.39 ha, while mixed forb cover remained relatively stable (12.38 ha to 13.82 ha). Accuracy assessment using the UAV-derived validation points yielded overall accuracy of 84% and 60% at fractional thresholds of 50% and 75%, respectively. Although statistical tests indicated no significant change in mean fractional abundance (p > 0.05), slope-based trend maps captured localized vegetation loss and regrowth patterns. These findings demonstrate the effectiveness of integrating LSU with UAV data for detecting subtle yet ecologically meaningful shifts in semi-arid grassland composition. Full article

Accurate assessment of a crop’s water requirement is essential for optimising irrigation scheduling and increasing the sustainability of water use. The crop coefficient (K_c) is a dimensionless factor that converts reference evapotranspiration (ET₀) into actual crop evapotranspiration (ET_c) and is widely used for irrigation scheduling. The K_c reflects canopy cover, phenology, and crop type/variety, but is difficult to measure directly in heterogeneous perennial systems, such as vineyards. Remote sensing (RS) products, especially open-source satellite imagery, offer a cost-effective solution at moderate spatial and temporal scales, although their application in vineyards has been relatively limited due to the large pixel size (~100 m²) relative to vine canopy size (~2 m²). This study aimed to improve grapevine K_c predictions using vegetation indices derived from harmonised Sentinel-2 imagery in combination with spectral unmixing, with ground data obtained from canopy light interception measurements in three winegrape cultivars (Shiraz, Cabernet Sauvignon, and Chardonnay) in the Barossa and Eden Valleys, South Australia. A linear spectral mixture analysis approach was taken, which required estimation of vine canopy cover through beta regression models to improve the accuracy of vegetation indices that were used to build the K_c prediction models. Unmixing improved the prediction of seasonal K_c values in Shiraz (R² of 0.625, RMSE = 0.078, MAE = 0.063), Cabernet Sauvignon (R² = 0.686, RMSE = 0.072, MAE = 0.055) and Chardonnay (R² = 0.814, RMSE = 0.075, MAE = 0.059) compared to unmixed pixels. Furthermore, unmixing improved predictions during the early and late canopy growth stages when pixel variability was greater. Our findings demonstrate that integrating open-source satellite data with machine learning models and spectral unmixing can accurately reproduce the temporal dynamics of K_c values in vineyards. This approach was also shown to be transferable across cultivars and regions, providing a practical tool for crop monitoring and irrigation management in support of sustainable viticulture. Full article

Hyperspectral images (HSIs), with their rich spectral information, are widely used in remote sensing; yet the inherent trade-off between spectral and spatial resolution in imaging systems often limits spatial details. Single-image hyperspectral super-resolution (HSI-SR) seeks to recover high-resolution HSIs from a single low-resolution input, but the high dimensionality and spectral redundancy of HSIs make this task challenging. In HSIs, spectral signatures and spatial textures often exhibit intrinsic symmetries, and preserving these symmetries provides additional physical constraints that enhance reconstruction fidelity and robustness. To address these challenges, we propose the Dynamic Spectral Collaborative Super-Resolution Network (DSCSRN), an end-to-end framework that integrates physical modeling with deep learning and explicitly embeds spatial–spectral symmetry priors into the network architecture. DSCSRN processes low-resolution HSIs with a Cascaded Residual Spectral Decomposition Network (CRSDN) to compress redundant channels while preserving spatial structures, generating accurate abundance maps. These maps are refined by two Synergistic Progressive Feature Refinement Modules (SPFRMs), which progressively enhance spatial textures and spectral details via a multi-scale dual-domain collaborative attention mechanism. The Dynamic Endmember Adjustment Module (DEAM) then adaptively updates spectral endmembers according to scene context, overcoming the limitations of fixed-endmember assumptions. Grounded in the Linear Mixture Model (LMM), this unmixing–recovery–reconstruction pipeline restores subtle spectral variations alongside improved spatial resolution. Experiments on the Chikusei, Pavia Center, and CAVE datasets show that DSCSRN outperforms state-of-the-art methods in both perceptual quality and quantitative performance, achieving an average PSNR of 43.42 and a SAM of 1.75 (×4 scale) on Chikusei. The integration of symmetry principles offers a unifying perspective aligned with the intrinsic structure of HSIs, producing reconstructions that are both accurate and structurally consistent. Full article

In recent years, remarkable advancements have been achieved in hyperspectral unmixing (HU). Sparse unmixing, in which models mix pixels as linear combinations of endmembers and their corresponding fractional abundances, has become a dominant paradigm in hyperspectral image analysis. To address the inherent limitations of spectral-only approaches, spatial contextual information has been integrated into unmixing. In this article, a superpixel collaborative sparse unmixing algorithm with graph differential operator (SCSU–GDO), is proposed, which effectively integrates superpixel-based local collaboration with graph differential spatial regularization. The proposed algorithm contains three key steps. First, superpixel segmentation partitions the hyperspectral image into homogeneous regions, leveraging boundary information to preserve structural coherence. Subsequently, a local collaborative weighted sparse regression model is formulated to jointly enforce data fidelity and sparsity constraints on abundance estimation. Finally, to enhance spatial consistency, the Laplacian matrix derived from graph learning is decomposed into a graph differential operator, adaptively capturing local smoothness and structural discontinuities within the image. Comprehensive experiments on three datasets prove the accuracy, robustness, and practical efficacy of the proposed method. Full article