Search Results (168)

Hyperspectral sparse unmixing (SU) networks have recently received considerable attention due to their model hyperspectral images (HSIs) with a priori spectral libraries and to capture nonlinear features through deep networks. This method effectively avoids errors associated with endmember extraction, and enhances the unmixing performance via nonlinear modeling. However, two major challenges remain: the use of large spectral libraries with high coherence leads to computational redundancy and performance degradation; moreover, certain feature extraction models, such as Transformer, while exhibiting strong representational capabilities, suffer from high computational complexity. To address these limitations, this paper proposes a hyperspectral unmixing dual-branch network integrating an adaptive hop-aware GCN and neighborhood offset Mamba that is termed DGMNet. Specifically, DGMNet consists of two parallel branches. The first branch employs the adaptive hop-neighborhood-aware GCN (AHNAGC) module to model global spatial features. The second branch utilizes the neighborhood spatial offset Mamba (NSOM) module to capture fine-grained local spatial structures. Subsequently, the designed Mamba-enhanced dual-stream feature fusion (MEDFF) module fuses the global and local spatial features extracted from the two branches and performs spectral feature learning through a spectral attention mechanism. Moreover, DGMNet innovatively incorporates a spectral-library-pruning mechanism into the SU network and designs a new pruning strategy that accounts for the contribution of small-target endmembers, thereby enabling the dynamic selection of valid endmembers and reducing the computational redundancy. Finally, an improved ESS-Loss is proposed, which combines an enhanced total variation (ETV) with an $l_{1/2}$ sparsity constraint to effectively refine the model performance. The experimental results on two synthetic and five real datasets demonstrate the effectiveness and superiority of the proposed method compared with the state-of-the-art methods. Notably, experiments on the Shahu dataset from the Gaofen-5 satellite further demonstrated DGMNet’s robustness and generalization. Full article

In recent years, deep learning (DL) has been demonstrated remarkable capabilities in hyperspectral unmixing (HU) due to its powerful feature representation ability. Convolutional neural networks (CNNs) are effective in capturing local spatial information, but limited in modeling long-range dependencies. In contrast, transformer architectures extract global contextual features via multi-head self-attention (MHSA) mechanisms. However, most existing transformer-based HU methods focus only on spatial or spectral modeling at a single scale, lacking a unified mechanism to jointly explore spatial and channel-wise dependencies. This limitation is particularly critical for multiscale contextual representation in complex scenes. To address these issues, this article proposes a novel Spatial-Channel Multiscale Transformer Network (SCMT-Net) for HU. Specifically, a compact feature projection (CFP) module is first used to extract shallow discriminative features. Then, a spatial multiscale transformer (SMT) and a channel multiscale transformer (CMT) are sequentially applied to model contextual relations across spatial dimensions and long-range dependencies among spectral channels. In addition, a multiscale multi-head self-attention (MMSA) module is designed to extract rich multiscale global contextual and channel information, enabling a balance between accuracy and efficiency. An efficient feed-forward network (E-FFN) is further introduced to enhance inter-channel information flow and fusion. Experiments conducted on three real hyperspectral datasets (Samson, Jasper and Apex) and one synthetic dataset showed that SCMT-Net consistently outperformed existing approaches in both abundance estimation and endmember extraction, demonstrating superior accuracy and robustness. Full article

Electrospinning is a versatile technique for producing porous nanofibers with a high specific surface area, making them ideal for several tissue engineering applications. Although Raman spectroscopy has been widely employed to characterize electrospun materials, but most studies report bulk-averaged properties without addressing the spatial heterogeneity of their chemical composition. Raman imaging has emerged as a promising tool to overcome this limitation; however, challenges remain, including limited sensitivity for detecting minor components, reliance on distinctive high-intensity bands, and the frequent use of commercial software. In this study, we present a methodology based on Raman hyperspectral image processing using open-source software (Python), capable of identifying components present at concentrations as low as 2% and 5%, even in the absence of exclusive bands of high or medium intensity, respectively. The proposed approach integrates spectral segmentation, end member extraction via the N-FINDR algorithm, and analysis of average spectra to map and characterize the chemical heterogeneity within electrospun fibers. Finally, its performance is compared with the traditional approach based on band intensities, highlighting improvements in sensitivity and the detection of weak signals. Full article

A timely and accurate distribution of crop types and cropping patterns provides a crucial reference for the management of agriculture and food security. However, accurately mapping crop types and cropping patterns in subtropical hilly and mountainous areas often face challenges such as mixed pixels resulted from fragmented patches and difficulty in obtaining optical satellites due to a frequently cloudy and rainy climate. Here we propose a crop type and cropping pattern mapping framework in subtropical hilly and mountainous areas, considering multiple sources of satellites (i.e., Landsat 8/9, Sentinel-2, and Sentinel-1 images and GF 1/2/7). To develop this framework, six types of variables from multi-sources data were applied in a random forest classifier to map major summer crop types (singe-cropped rice and double-cropped rice) and winter crop types (rapeseed). Multi-scale segmentation methods were applied to improve the boundaries of the classified results. The results show the following: (1) Each type of satellite data has at least one variable selected as an important feature for both winter and summer crop type classification. Apart from the endmember variables, the other five extracted variable types are selected by the RF classifier for both winter and summer crop classifications. (2) SAR data can capture the key information of summer crops when optical data is limited, and the addition of SAR data can significantly improve the accuracy as to summer crop types. (3) The overall accuracy (OA) of both summer and winter crop type mapping exceeded 95%, with clear and relatively accurate cropland boundaries. Area evaluation showed a small bias in terms of the classified area of rapeseed, single-cropped rice, and double-cropped rice from statistical records. (4) Further visual examination of the spatial distribution showed a better performance of the classified crop types compared to three existing products. The results suggest that the proposed method has great potential in accurately mapping crop types in a complex subtropical planting environment. Full article

AetherGeo is a standalone piece of software (current version 1.0) that aims to enable the user to analyze raster data, with a special focus on processing multi- and hyperspectral images. Being developed in Python 3.12.4, this application is a free, open-source alternative for spectral analysis, something considered beneficial for researchers, allowing for a flexible approach to start working on the topic without acquiring proprietary software licenses. It provides the user with a set of tools for spectral data analysis through classical approaches, such as band ratios and RGB combinations, but also more elaborate techniques, such as endmember extraction and unsupervised image classification with partial spectral unmixing techniques. While it has been tested on visible and near-infrared (VNIR), short-wave infrared (SWIR), and VNIR-SWIR datasets, the functions implemented have the potential to be applied to other spectral ranges. On top of this, all results can be visualized within the software, and some tools allow for the inspection and comparison of spectra and spectral libraries. Providing software with these capabilities in a unified platform has the potential to positively impact research and education, as students and educators usually have limited access to proprietary software. Full article

This paper proposes an experimental procedure based on hyperspectral imaging (HSI) combined with statistical classification for assessing archaeo-paleoanthropological stratigraphic deposits at the Gran Dolina site (TD10 unit), located in the Sierra de Atapuerca (Burgos, Spain). Representative spectral reflectance signatures were determined and analyzed using HSI measurements and statistical classification methods in natural light conditions across various capture distances. This study aims to characterize and quantify cave sediments by defining spectral models for feature classification and spectral similarity analysis, evaluating the strengths and limitations of spectral captures at this specific site. HSI technology enhances the analysis and identification of materials at an internationally recognized reference site for human evolution studies. Hyperspectral imaging assessment of archaeo-paleoanthropological stratigraphic deposits emerges as an innovative digital tool, revolutionizing the sustainable management of cultural heritage and environmental sciences by enabling advanced material identification and stratigraphic analysis. Full article

Biodentine, a tricalcium silicate cement, has emerged as a retrograde root-end filling material to promote periapical lesion (PL) healing after apicoectomy. However, its underlying mechanisms remain unclear. This study tested the hypothesis that Biodentine stimulates the osteogenic differentiation of mesenchymal stromal cells (MSCs) derived from PLs. The Biodentine extract (B-Ex) was prepared by incubating polymerized Biodentine in RPMI medium (0.2 g/mL) for three days at 37 °C. B-Ex, containing both released microparticles and soluble components, was incubated with PL-MSCs cultured in either a basal MSC medium or suboptimal osteogenic medium. Osteogenic differentiation was assessed by Alizarin Red staining and the expression of 20 osteoblastogenesis-related genes. Non-cytotoxic concentrations of B-Ex stimulated the proliferation of PL-MSCs and induced their osteogenic differentiation in a dose-dependent manner, with a significantly enhanced effect in suboptimal osteogenic medium. B-Ex upregulated most early and late osteoblastic genes. However, the differentiation process was prolonged, as indicated by the delayed expression of wingless-type MMTV integration site family member 2 (WNT2), bone gamma-carboxyglutamate protein (BGLAP), bone morphogenic protein-2 (BMP-2), growth hormone receptor (GHR), and FOS-like 2, AP-1 transcription factor subunit (FOSL2), compared with their expression under optimal osteogenic conditions. The stimulatory effect of B-Ex was primarily calcium dependent, as it was reduced by 85% when B-Ex was treated with the calcium-chelating agent EGTA. In conclusion, Biodentine promotes the osteogenic differentiation of PL-MSCs in a calcium-dependent manner, supporting its stimulatory role in periapical healing. Full article

Hyperspectral unmixing plays a fundamental role in mining meaningful information from hyperspectral data. It promotes advancements in various scientific, environmental, and industrial applications by extracting meaningful information from hyperspectral data. However, it is still hindered by several challenges, including accurately identifying the number of endmembers in a hyperspectral image, extracting the endmembers, and estimating their abundance fractions. This research addresses these challenges by employing a convolutional-neural-network-based autoencoder that leverages both the spatial and spectral information present in the hyperspectral image. Additionally, a self-learning module utilizing a fuzzy clustering algorithm is designed to determine the number of endmembers. A novel approach is also introduced that estimates the abundances of the endmembers from the autoencoder and the clustering output. Real datasets and relevant performance metrics were used to validate and evaluate the performance of the proposed method. The results demonstrate that our approach outperforms related methods, achieving improvements of 47% in Spectral Angle Distance (SAD) and 42% in root-mean-square error (RMSE). Full article

In satellite remote sensing, mixed pixels commonly arise in medium- and low-resolution imagery, where surface reflectance is a combination of various land cover types. The widely adopted linear mixing model enables the decomposition of mixed pixels into constituent endmembers, effectively bridging spectral resolution gaps by retrieving the spectral properties of individual land cover types. This study introduces a method to enhance multispectral surface reflectance data by reconstructing additional spectral information, particularly in the visible spectral range, using the TROPOMI BRDF product generated by the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm. Employing non-negative matrix factorization (NMF), the approach extracts spectral basis vectors from reference spectral libraries and reconstructs key spectral features using a limited number of wavelength bands. The comprehensive test results show that this method is particularly effective in supplementing surface reflectance information for specific wavelengths where gas absorption is strong or atmospheric correction errors are significant, demonstrating its applicability not only within the 400–800 nm range but also across the broader spectral range of 400–2400 nm. While not a substitute for hyperspectral observations, this approach provides a cost-effective means to address spectral resolution gaps in multispectral datasets, facilitating improved surface characterization and environmental monitoring. Future research will focus on refining spectral libraries, improving reconstruction accuracy, and expanding the spectral range to enhance the applicability and robustness of the method for diverse remote sensing applications. Full article

Transformers have performed favorably in recent hyperspectral unmixing studies in which the self-attention mechanism possesses the ability to retain spectral information and spatial details. However, the lack of reliable prior information for correction guidance has resulted in an inadequate accuracy and robustness of the network. To benefit from the advantages of the Transformer architecture and to improve the interpretability and robustness of the network, a dual-branch network with prior information correction, incorporating a Transformer network (PICT-Net), is proposed. The upper branch utilizes pre-extracted endmembers to provide pure pixel prior information. The lower branch employs a Transformer structure for feature extraction and unmixing processing. A weight-sharing strategy is employed between the two branches to facilitate information sharing. The deep integration of prior knowledge into the Transformer architecture effectively reduces endmember variability in hyperspectral unmixing and enhances the model’s generalization capability and accuracy across diverse scenarios. Experimental results from experiments conducted on four real datasets demonstrate the effectiveness and superiority of the proposed model. Full article

Deep learning has emerged as a prevalent approach for hyperspectral unmixing. However, most existing unmixing methods employ a single network, resulting in moderate estimation errors and less meaningful endmembers and abundances. To address this imitation, this paper proposes a novel double autoencoders-based unmixing method, consisting of an endmember extraction network and an abundance estimation network. In the endmember network, to improve the spectral discrimination, a logarithm spectral angle distance (SAD), integrated with anomaly-guided weight, is developed as the loss function. Specifically, the logarithm function is used to boost the reliability of a pixel based on its high SAD similarity to other pixels. Moreover, the anomaly-guided weight mitigates the influence of outliers. As for the abundance network, a spectral convolutional autoencoder combined with the channel attention module is employed to exploit the spectral features. Additionally, the decoder weight is shared between the two networks to reduce computational complexity. Extensive comparative experiments with state-of-the-art unmixing methods demonstrate that the proposed method achieves superior performance in both endmember extraction and abundance estimation. Full article

We explore a new approach for the parsimonious, generalizable, efficient, and potentially automatable characterization of spectral diversity of sparse targets in spectroscopic imagery. The approach focuses on pixels which are not well modeled by linear subpixel mixing of the Substrate, Vegetation and Dark (S, V, and D) endmember spectra which dominate spectral variance for most of Earth’s land surface. We illustrate the approach using AVIRIS-3 imagery of anthropogenic surfaces (primarily hydrocarbon extraction infrastructure) embedded in a background of Arctic tundra near Prudhoe Bay, Alaska. Computational experiments further explore sensitivity to spatial and spectral resolution. Analysis involves two stages: first, computing the mixture residual of a generalized linear spectral mixture model; and second, nonlinear dimensionality reduction via manifold learning. Anthropogenic targets and lakeshore sediments are successfully isolated from the Arctic tundra background. Dependence on spatial resolution is observed, with substantial degradation of manifold topology as images are blurred from 5 m native ground sampling distance to simulated 30 m ground projected instantaneous field of view of a hypothetical spaceborne sensor. Degrading spectral resolution to mimicking the Sentinel-2A MultiSpectral Imager (MSI) also results in loss of information but is less severe than spatial blurring. These results inform spectroscopic characterization of sparse targets using spectroscopic images of varying spatial and spectral resolution. Full article

A two-eyed seeing approach considered Indigenous knowledge and Western science towards eco–health, reconciliation and land back with Fort William First Nation (FWFN) in Ontario, Canada. To map traditional land use, occupancy, and ecological knowledge, we interviewed 49 FWFN members about their hunting, fishing, trapping, plant harvesting, cultural sites, and sacred gatherings on their ancestral land. Their traditional land use and occupancy includes more than 7.5 million ha of their ancestral land. The FWFN members reported many industrial impacts on their reserve and ancestral land. We analyzed the normalized difference vegetation index (NDVI) change over time on FWFN’s ancestral land and the Thunder Bay Pulp and Paper Mill (TBPP)’s National Pollutant Release Inventory data to investigate the FWFN members’ ecohealth concerns. The NDVI analysis revealed large tracts of degraded FWFN’s ancestral land due to logging areas, mining claims, settlements, and paper mills. Mining claims and greenstone belts occupy a quarter of the FWFN members’ ancestral land. The TBPP mill dumped pollution into the Kaministiquia River upstream and upwind of the FWFN community, exposing FWFN members to kilotons of cancerous and other toxic chemicals each year for over a century. Resource extraction and pollution in Northwestern Ontario negatively impacted the human health and ecosystem integrity of FWFN, requiring reconciliation by restoring damaged land and preventing pollution as the starting point for land back. The first step to land back is ending the environmental racism of the TBPP’s pollution directed downstream and downwind of FWFN and protecting ancestral land against logging, mining, and other extractive industries. Full article

We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM). The model represents endmember mixing using a latent space of points sampled within a $(n-1)$-simplex corresponding to n unique sources. Barycentric coordinates within this simplex are naturally interpreted as relative endmember abundances satisfying both the abundance sum-to-one and abundance non-negativity constraints. Points in this latent space are mapped to reflectance spectra via a flexible function combining linear and non-linear mixing. Due to the probabilistic formulation of the GSM, spectral variability is also estimated by a precision parameter describing the distribution of observed spectra. Model parameters are determined using a generalized expectation-maximization algorithm, which guarantees non-negativity for extracted endmembers. We first compare the GSM against three varieties of non-negative matrix factorization (NMF) on a synthetic data set of linearly mixed spectra from the USGS spectral database. Here, the GSM performed favorably for both endmember accuracy and abundance estimation with all non-linear contributions driven to zero by the fitting procedure. In a second experiment, we apply the GTM to model non-linear mixing in real hyperspectral imagery captured over a pond in North Texas. The model accurately identified spectral signatures corresponding to near-shore algae, water, and rhodamine tracer dye introduced into the pond to simulate water contamination by a localized source. Abundance maps generated using the GSM accurately track the evolution of the dye plume as it mixes into the surrounding water. Full article

Surface water fraction mapping is an essential preprocessing step for the subpixel mapping (SPM) of surface water, providing valuable prior knowledge about surface water distribution at the subpixel level. In recent years, spectral mixture analysis (SMA) has been extensively applied to estimate surface water fractions in multispectral images by decomposing each mixed pixel into endmembers and their corresponding fractions using linear or nonlinear spectral mixture models. However, challenges emerge when introducing existing surface water fraction mapping methods to hyperspectral images (HSIs) due to insufficient exploration of spectral information. Additionally, inaccurate extraction of endmembers can result in unsatisfactory water fraction estimations. To address these issues, this paper proposes an adaptive unmixing method based on iterative multi-objective optimization for surface water fraction mapping (IMOSWFM) using Zhuhai-1 HSIs. In IMOSWFM, a modified normalized difference water fraction index (MNDWFI) was developed to fully exploit the spectral information. Furthermore, an iterative unmixing framework was adopted to dynamically extract high-quality endmembers and estimate their corresponding water fractions. Experimental results on the Zhuhai-1 HSIs from three test sites around Nanyi Lake indicate that water fraction maps obtained by IMOSWFM are closest to the reference maps compared with the other three SMA-based surface water fraction estimation methods, with the highest overall accuracy (OA) of 91.74%, 93.12%, and 89.73% in terms of pure water extraction and the lowest root-mean-square errors (RMSE) of 0.2506, 0.2403, and 0.2265 in terms of water fraction estimation. This research provides a reference for adapting existing surface water fraction mapping methods to HSIs. Full article