Unmixing Approach Research Articles

Spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints

Hyperspectral sparse unmixing, an image processing technique, leverages a spectral library enriched with endmember spectral information as a prerequisite. It decomposes the hyperspectral data to ascertain the abundance corresponding to each endmember in the spectral library. Currently, the majority of sparse unmixing methods are inadequate in high-noise environments due to their failure to comprehensively account for hyperspectral characteristics. Addressing this challenge, this paper introduces an innovative sparse unmixing approach for hyperspectral images named spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints (SWSU-ATVLR). Initially, the sparse unmixing algorithm is introduced in detail. Subsequently, we present the our method. This method seamlessly integrates the low-rank, adaptive TV and spectral weighting characteristics of hyperspectral data. While preserving the low-rank attributes and sparsity of abundance, the adaptively adjusted abundance matrix exhibits a regularized horizontal and vertical difference ratio across various structures and fully utilizes spectral information, enhancing denoising efficiency. Subsequently, the ADMM algorithm is employed to solve the new model. To validate our proposed algorithm, SWSU-ATVLR method is compared and analysed in detailed experiments with several current state-of-the-art methods through simulated and real data experiments. Experimental results prove that our proposed method is superior to these state-of-the-art methods.

Multiobjective sparse unmixing based hyperspectral change detection

Hyperspectral unmixing can provide the composition of ground objects, while change detection can identify the changes of the same region over time. Therefore, unmixing based hyperspectral change detection can investigate changes in a subpixel-level. It can provide not only whether the change happens or not but also how to change. This paper will gradually establish an unconstrained sparse unmixing based change detection model and design a multiobjective optimization sparse unmixing approach (termed MoSU-CD) to solve it for more subpixel-level change details. Compared with the existing sparse unmixing based change detection approaches, MoSU-CD directly unmixes the difference map of multi-temporal images rather than multi-temporal images themselves respectively. Then it explores the change details of one substance in each pixel based on the unmixing results, i.e., endmembers and abundances. In addition, in order to improve the change detection accuracy, an ensemble decision strategy is designed for the Pareto optimal solutions obtained by multiobjective optimization method. Based on this strategy, the subpixel-level changes can be aggregated to generate an aggressive pixel-level change map. The experimental results on synthetic and real data sets demonstrate that the proposed MoSU-CD not only outperforms the benchmark and state-of-art approaches in terms of pixel-level change detection but also provides additional subpixel-level change information.

High-resolution greenspace dynamic data cube from Sentinel-2 satellites over 1028 global major cities

Greenspace, offering multifaceted ecological and socioeconomic benefits to the nature system and human society, is integral to the 11th Sustainable Development Goal pertaining to cities and communities. Spatially and temporally explicit information on greenspace is a premise to gauge the balance between its supply and demand. However, existing efforts on urban greenspace mapping primarily focus on specific time points or baseline years without well considering seasonal fluctuations, which obscures our knowledge of greenspace’s spatiotemporal dynamics in urban settings. Here, we combined spectral unmixing approach, time-series phenology modeling, and Sentinel-2 satellite images with a 10-m resolution and nearly 5-day revisit cycle to generate a four-year (2019–2022) 10-m and 10-day resolution greenspace dynamic data cube over 1028 global major cities (with an urbanized area >100 km2). This data cube can effectively capture greenspace seasonal dynamics across greenspace types, cities, and climate zones. It also can reflect the spatiotemporal dynamics of the cooling effect of greenspace with Landsat land surface temperature data. The developed data cube provides informative data support to investigate the spatiotemporal interactions between greenspace and human society.

Navigating challenges and solutions in quantitative photoacoustic imaging

Photoacoustic imaging, an emerging modality that seamlessly combines advantages of optical absorption contrast and ultrasound resolution, holds great promise for noninvasive imaging of biological tissues. Its applications span across diverse fields, such as dermatology, oncology, cardiology, and neurology. However, achieving accurate image reconstruction and physiological parameters quantification from raw photoacoustic signals presents a significant challenge. This challenge primarily arises from the inherent heterogeneity of tissues, encompassing variations in optical fluence and acoustic properties. In addition, incomplete information acquired from a limited view also leads to artifacts, image distortions, and reduced spatial resolution. Furthermore, robust spectral unmixing approach is another key step to restore the initial biochemical components' distribution with complex or unknown background absorption. To overcome these hurdles, researchers have proposed numerous state-of-the-art techniques, aiming to improve the accuracy and reliability of quantitative photoacoustic imaging (qPAI) in heterogeneous tissue. This review aims to comprehensively overview recent developments over the past decade, for addressing four main challenges frequently encountered in qPAI: limited-view reconstruction, acoustic heterogeneity, optical fluence fluctuations, and robust spectral unmixing, which serves as a reference for readers seeking to understand the specific challenges and corresponding solutions in this field.

Assessment of soil erosion in the Upper Citarum watershed for sustainability of the Saguling reservoir: unmixing model approach.

The Citarum watershed and the Saguling reservoir are vital natural resources in Indonesia, affecting the livelihood of West Java and the DKI Jakarta population. This study aimed to assess the soil erosion in the Upper Citarum watershed and identify its source. The study used the fallout radionuclide technique, geochemical tracers, and an unmixing model to measure soil erosion and the contribution of suspended sediment sources due to erosion. Soil bulk transects and surface soil were sampled using a coring tool on the Ciwidey and Cisangkuy sub-watersheds. Riverbank and suspended sediment samples were collected from tributaries and rivers. With 137Cs, 40% of the samples had values below the minimum detectable activity, and vice versa for 210Pbex, all samples are detectable. For mitigation, bare land needs to be recovered due to its erosion (25.6 t ha-1year-1) exceeding the tolerance erosion value (17 t ha-1year-1). Statistically, Mg and Na were the most appropriate composite tracers for suspended sediment contribution. The unmixing model predicted the sediment contributors from bare land (58%), the riverbank (32.7%), and plantation land (9.3%). Proper land conservation could reduce sediment supply by almost 14.7% and extend the reservoir's life. This is the first study to report the feasibility of the unmixing model in Indonesia.

Probabilistic Mixture Model-Based Spectral Unmixing

Spectral unmixing attempts to decompose a spectral ensemble into the constituent pure spectral signatures (called endmembers) along with the proportion of each endmember. This is essential for techniques like hyperspectral imaging (HSI) used in environment monitoring, geological exploration, etc. Several spectral unmixing approaches have been proposed, many of which are connected to hyperspectral imaging. However, most extant approaches assume highly diverse collections of mixtures and extremely low-loss spectroscopic measurements. Additionally, current non-Bayesian frameworks do not incorporate the uncertainty inherent in unmixing. We propose a probabilistic inference algorithm that explicitly incorporates noise and uncertainty, enabling us to unmix endmembers in collections of mixtures with limited diversity. We use a Bayesian mixture model to jointly extract endmember spectra and mixing parameters while explicitly modeling observation noise and the resulting inference uncertainties. We obtain approximate distributions over endmember coordinates for each set of observed spectra while remaining robust to inference biases from the lack of pure observations and the presence of non-isotropic Gaussian noise. As a direct impact of our methodology, access to reliable uncertainties on the unmixing solutions would enable robust solutions to noise, as well as informed decision-making for HSI applications and other unmixing problems.

PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

Remote sensing satellite imagery consistently provides valuable and frequent information, enabling the exploration of mineral resources across immense, remote and harsh domains. Recent developments in spaceborne hyperspectral remote sensing have opened avenues to support diverse remote sensing applications, particularly in the realm of mineral exploration. This study evaluates the capabilities of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral satellite data for mapping alteration minerals using the Matched Filtering Unmixing (MFU) approach in the Sar-e-châh-e-shur, Birjand, Iran. Minerals such as richterite, augite, psilomelane, ilmenite, kaolinite, smectite, mirabilite, muscovite, and chlorite were identified using the vertex component analysis (VCA) technique. Subsequently, alteration mineral maps of the study area were generated using a matched filtering technique. Additionally, through the integration of X-ray diffraction (XRD) analysis, thin section examination, geochemical study of stream sediments, and interpretation of geological maps, potential alteration mineralization zones were delineated in the study area. Ultimately, the validation process, which included comparing the maps with the findings derived from the PRISMA remote sensing study, was conducted using the normal score equation. Thus, our results yielded a normalized score of 3.42 out of 4, signifying an 85.71% agreement with the regional geological characteristics of the study area. The results of this investigation highlight the substantial potential of the PRISMA dataset for systematic alteration mineral mapping and consequent exploration of ore minerals, specifically in challenging and inaccessible terrains.

Assessment of unmixing approaches for the quantitation of SERS nanoparticles in highly multiplexed spectral images

AbstractSurface‐enhanced Raman scattering nanoparticles (SERS NPs) offer powerful optical contrast features for imaging assays. Their gold core enhances the inelastic scattering cross section, allowing highly sensitive and rapid detection, and their characteristic sets of narrow spectral bands give them unsurpassed multiplexing capabilities. Multiplexed hyperspectral images are commonly unmixed using a compensation matrix of reference spectra to produce quantitative image channels illustrating the distribution of each material. It is these unmixed channels that are fit for interpretation from assays utilizing SERS NP contrast agents. Some factors that may impact SERS NP quantitative and dynamic range capabilities may include endogenous background heterogeneity, the ability of unmixing algorithms to account for signal variances, and linear system conditioning imposed by contrast agent signals. We report on hyperspectral Raman imaging of mixtures of SERS NPs from an expanded library of contrast agents. We study increasing plexity and varying degrees of system conditioning as inputs to a diverse set of classical, non‐negatively constrained, and regularized regression algorithms to investigate which signal features and unmixing methods deliver the most promising quantitation performance with the least error. Raman imaging of SERS NP mixtures is performed on controlled substrates and representative biological specimens, and experimental results are compared against ground truth data. We evaluate spectral fitting fidelity, quantitation, and specificity correlations with system conditioning. Spectral unmixing with a regularized hybrid of least squares regression with principal component analysis (HLP) algorithm approximated spectra with 3.5× better fitting fidelity and 3× better quantitation robustness with tissue background compared with simpler unmixing routines.

A hyperspectral unmixing approach for ink mismatch detection in unbalanced clusters

Detecting ink mismatch is a significant challenge in verifying the authenticity of documents, especially when dealing with uneven ink distribution. Conventional imaging methods frequently fail to distinguish visually similar inks. Our study presents a novel hyperspectral unmixing approach to detect ink mismatches in unbalanced clusters. The proposed method identifies unique spectral characteristics of different inks employing k-means clustering and Gaussian mixture models (GMMs) to perform color segmentation on different ink types and utilizes elbow estimation and silhouette coefficient to evaluate the number of inks estimation precisely. For a more accurate estimation of quantity, which is generally not an attribute of clustering methods, we employed entropy calculations in the red, green, and blue depth channels for precise abundance estimation of ink. This unique combination of basic techniques in conjunction exhibits better efficacy in performing ink unmixing and provides a real-world document forensic solution compared to current methods that rely on assumptions like prior knowledge of the inks used in a document and deep learning-based methods that rely heavily on abundant training datasets. We evaluate our approach on the iVision handwritten hyperspectral images dataset (iVision HHID), which is a comprehensive and rich dataset that surpasses the commonly-used UWA writing inks hyperspectral images (WIHSI) database in size and diversity. This study has accomplished the unmixing task with three main challenges: unmixing of diverse ink spectral signatures (149 spectral bands instead of 33 bands in the previous dataset), without using prior knowledge and assumptions about the number of inks used in the questioned document, and not requiring large training data for performing unmixing. Furthermore, the security of the proposed document authentication methodology to address the likelihood of forgeries or manipulations in questioned documents is enhanced as compared to previous works relying on known inks and known spectrum. Randomization techniques and anomaly detection mechanisms are used in our methodology which increases the difficulty for adversaries to predict and manipulate specific aspects of the input data in questioned documents, thereby enhancing the robustness of our method. The code for conducting this research can be accessed at GitHub repository.

Multidecadal grassland fractional cover time series retrieval for Germany from the Landsat and Sentinel-2 archives

Time series data provided by the Sentinel-2 and Landsat satellite missions offer manifold opportunities for grassland monitoring. The high intra-annual observation density of Sentinel-2 combined with the continuous long-term data record of Landsat enable analyses at seasonal, annual, and decadal scales. Fractional cover estimates of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil provide essential information to describe grassland conditions and processes. Yet, retrieving consistent grassland fractional cover time series from Landsat and Sentinel-2 imagery represents a major challenge. In this study, we implemented a multisensor spectral unmixing approach for retrieving multidecadal (i.e., 1984 to 2021) fractional cover time series of PV, NPV, and soil for Germany's permanent grasslands from the Landsat and Sentinel-2 archives. The spectral consistency of Landsat 5/7/8 and Sentinel-2A/B imagery as well as the coherency of a Sentinel-2-based spectral library to be used across Landsat and Sentinel-2 sensors served as the foundation for implementing the unmixing approach. We then employed regression-based unmixing using synthetic training data from spectral libraries for developing spatially and temporally generalized models. Applying these models to the Landsat and Sentinel-2 data facilitated multidecadal fractional cover mapping at a national-scale. We evaluated the quality of our multidecadal grassland fractional cover time series using statistical validation and linear correspondence analysis. The statistical validation was based on a multitemporal reference dataset spanning 2017 to 2021, derived from very high-resolution (VHR) imagery. Landsat 7/8- and Sentinel-2A/B-derived fractions showed similar Mean Absolute Errors (MAEs), i.e., 0.067 and 0.08 for PV, 0.149 and 0.15 for NPV, and 0.135 and 0.129 for soil. Linear correspondence analysis confirmed consistent PV and NPV fractional cover estimates among Landsat and Sentinel-2 sensors, suggesting similar errors beyond the statistical validation period. However, higher errors and weaker linear correspondence pointed to remaining uncertainties in soil fractional cover estimates. We further showed that the differences in spatial and spectral resolutions, i.e., the pixel size and the number of spectral bands, between Landsat and Sentinel-2 had a minor effect and were well mitigated by the spectral unmixing approach. We finally illustrated the value of the dense time series available for more recent years for describing seasonal trajectories of grassland conditions and land use intensities, as well as the use of the entire time series for analyzing long-term grassland dynamics based on annual fraction anomalies. Our study emphasizes the efficacy of generalized multisensor spectral unmixing approaches for retrieving consistent PV, NPV, and soil cover fractions across space, time, and sensors, providing a valuable means for grassland monitoring.

Structured low-rank representation learning for hyperspectral sparse unmixing

ABSTRACT Sparse unmixing methods have been widely used to estimate the abundance of each material component from hyperspectral images. However, conventional sparse unmixing approaches only consider matrix factorization without limited explorations on the high-dimensional structures of the third-order tensors. To address this issue, we propose SUnSLRR, a novel approach to sparse unmixing of hyperspectral data based on structured low-rank tensor modelling. In contrast to traditional methods, SUnSLRR leverages the low-rank property underlying the abundance tensor to exploit structural details from multiple modes. By incorporating sparsity regularization and a low-rank constraint, SUnSLRR can effectively extract the intrinsic features of hyperspectral data. We apply the alternating direction method of multipliers framework to solve the optimization problem induced by SUnSLRR, and experiments conducted on simulated and real hyperspectral images demonstrate the superior effectiveness of our proposed method compared to traditional methods in terms of both accuracy and efficiency.

Effects of skin tone on photoacoustic imaging and oximetry.

Photoacoustic imaging (PAI) provides contrast based on the concentration of optical absorbers in tissue, enabling the assessment of functional physiological parameters such as blood oxygen saturation (). Recent evidence suggests that variation in melanin levels in the epidermis leads to measurement biases in optical technologies, which could potentially limit the application of these biomarkers in diverse populations. To examine the effects of skin melanin pigmentation on PAI and oximetry. We evaluated the effects of skin tone in PAI using a computational skin model, two-layer melanin-containing tissue-mimicking phantoms, and mice of a consistent genetic background with varying pigmentations. The computational skin model was validated by simulating the diffuse reflectance spectrum using the adding-doubling method, allowing us to assign our simulation parameters to approximate Fitzpatrick skin types. Monte Carlo simulations and acoustic simulations were run to obtain idealized photoacoustic images of our skin model. Photoacoustic images of the phantoms and mice were acquired using a commercial instrument. Reconstructed images were processed with linear spectral unmixing to estimate blood oxygenation. Linear unmixing results were compared with a learned unmixing approach based on gradient-boosted regression. Our computational skin model was consistent with representative literature for in vivo skin reflectance measurements. We observed consistent spectral coloring effects across all model systems, with an overestimation of and more image artifacts observed with increasing melanin concentration. The learned unmixing approach reduced the measurement bias, but predictions made at lower blood still suffered from a skin tone-dependent effect. PAI demonstrates measurement bias, including an overestimation of blood , in higher Fitzpatrick skin types. Future research should aim to characterize this effect in humans to ensure equitable application of the technology.

Marine plastic detection using PRISMA hyperspectral satellite imagery in a controlled environment

ABSTRACT The amount of plastics on seawater is causing ecosystem damage to both land areas and water bodies. It is proven that once plastic particles reach the sea, they will be degraded. As their detection is not easy with in situ sampling, remote sensing techniques could help detect and evaluate their impact on ecosystems. To understand the main limitations of detecting floating plastic material through remote images, an a pilot experiment was carried out on an artificial floating plastic target deployed at the Aegean Sea (Greece) within the Plastic Litter Project 2021. Hyperspectral PRISMA, multispectral PlanetScope and visible Unmanned Aerial Systems (UAS) acquisitions were analysed. A nonlinear unmixing technique was applied to derive the spectral signature of the plastic target; finally, the linear unmixing approach allowed for determining the plastic percentage occupation at the pixel level. Different band combinations of the PRISMA data were selected to evaluate which provided the best result for the detection; one of these band combinations was retrieved via the principal component analysis. Only slight differences were achieved using the different PRISMA band-sets. As expected, since the artificial plastic target had a diameter comparable with the PRISMA spatial resolution (~30 m), its detection was a challenging task caused by the water influence in the pixel (mixed pixels). The detection was realized by benefitting from the high amount of available spectral bands, as confirmed by the comparative test with a PlanetScope image used at its original spatial resolution (3 m) and after degrading it at the PRISMA level. Results demonstrated that an optimal target detection was possible with few spectral bands, only taking advantage of the high spatial resolution (compared with the target dimension). Indeed, unreliable plastic fractions were derived at the PRISMA spatial resolution with a limited number of spectral bands.

A blind convolutional deep autoencoder for spectral unmixing of hyperspectral images over waterbodies

Harmful algal blooms have dangerous repercussions for biodiversity, the ecosystem, and public health. Automatic identification based on remote sensing hyperspectral image analysis provides a valuable mechanism for extracting the spectral signatures of harmful algal blooms and their respective percentage in a region of interest. This paper proposes a new model called a non-symmetrical autoencoder for spectral unmixing to perform endmember extraction and fractional abundance estimation. The model is assessed in benchmark datasets, such as Jasper Ridge and Samson. Additionally, a case study of the HSI2 image acquired by NASA over Lake Erie in 2017 is conducted for extracting optical water types. The results using the proposed model for the benchmark datasets improve unmixing performance, as indicated by the spectral angle distance compared to five baseline algorithms. Improved results were obtained for various metrics. In the Samson dataset, the proposed model outperformed other methods for water (0.060) and soil (0.025) endmember extraction. Moreover, the proposed method exhibited superior performance in terms of mean spectral angle distance compared to the other five baseline algorithms. The non-symmetrical autoencoder for the spectral unmixing approach achieved better results for abundance map estimation, with a root mean square error of 0.091 for water and 0.187 for soil, compared to the ground truth. For the Jasper Ridge dataset, the non-symmetrical autoencoder for the spectral unmixing model excelled in the tree (0.039) and road (0.068) endmember extraction and also demonstrated improved results for water abundance maps (0.1121). The proposed model can identify the presence of chlorophyll-a in waterbodies. Chlorophyll-a is an essential indicator of the presence of the different concentrations of macrophytes and cyanobacteria. The non-symmetrical autoencoder for spectral unmixing achieves a value of 0.307 for the spectral angle distance metric compared to a reference ground truth spectral signature of chlorophyll-a. The source code for the proposed model, as implemented in this manuscript, can be found at https://github.com/EstefaniaAlfaro/autoencoder_owt_spectral.git.

Hyperspectral image analysis for the evaluation of chilling injury in avocado fruit during cold storage

Many vegetables and fruit are sensitive to storage at lower temperatures and experience chilling injury that can result in internal disorder, leading to postharvest waste and economic loss. Most tropical and subtropical fruit, such as avocado and mango, are sensitive to cold storage and can develop chilling injury with symptoms such as abnormal ripening, skin pitting, and darkening of the mesocarp. The severity of chilling injury symptoms is determined by various factors, including the cultivar, the storage temperature, and the length of cold exposure. The object of this study was to develop a method for estimation of internal chilling injury in avocado using hyperspectral imaging (HSI). Hyperspectral images of the avocado surface were obtained with a hyperspectral camera over the wavelength range of 400 nm to 1000 nm with 2 nm resolution. Using estimated functions of multiple instances (eFUMI) and hyperspectral unmixing approaches, we identified the spectra that correlated with areas of internal injured. By quantifying injured areas and fruit firmness, we showed the association between the internal and the external changes as detected by HSI, which implied that HSI has the potential to be used to monitor internal disorder of avocado. HSI technology opens the possibility of non-destructive determination of chilling injury and other internal quality attributes in horticultural and food products.

Mapping forest fire severity using bi-temporal unmixing of Sentinel-2 data - Towards a quantitative understanding of fire impacts

Precise quantification of forest fire impacts is critical for management strategies in support of post-fire mitigation. In this regard, optical remote sensing imagery in combination with spectral unmixing has been widely used to measure fire severity by means of fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), charcoal (CH) and further ground components such as ash, bare soil and rocks. However, most unmixing analyses have made use of a single post-fire image without accounting for the pre-fire state. We aim to assess fire severity from Sentinel-2 data using a bi-temporal spectral unmixing analysis that provides a quantitative fire impact description and is oriented towards the process of change by including pre-fire and post-fire information. Unmixing was based on Random Forest Regression (RFR) modeling using synthetic training data from a bi-temporal spectral library. We describe fire severity as changes associated with the combustion of photosynthetic vegetation (PV–CH fraction) and dieback of photosynthetic vegetation (PV-NPV fraction). Unburned forest was mapped as stable photosynthetic vegetation (PV-PV fraction). We evaluated our approach on a forest fire that burned in a temperate forest region in eastern Germany in 2018. Independent validation was carried out based on reference fractions obtained from very high-resolution (VHR) imagery such as Plante Scope, SPOT6, orthophotos, aerial photos, and Google Earth. The results underline the effectiveness of our unmixing approach, with Root Mean Squared Errors (RMSE) of 0.072 for PV-CH, 0.09 for PV-NPV, and 0.08 for PV-PV fractions. Most of the errors were caused by spectral similarity between charcoal and shadow effects caused by trees, and the coloring of foliage and NPV in the late phenological season of the post-fire Sentinel-2 image. Based on the two-dimensional feature space of PV-CH and PV-NPV fractions, we calculated two metrics to characterize fire impacts: distance, an indicator of disturbance severity (sum of combustion and dieback), and angle, a measure of disturbance composition (gradient between combustion and dieback). Furthermore, we compared the fraction-based metrics with the difference Normalized Burn Ratio (dNBR). Since the dNBR is most sensitive to combustion and presence of charcoal, it does not fully characterize fire-related vegetation loss associated with dieback. The bi-temporal fraction-based indices provide more ecologically meaningful information on fire severity, particularly for regions that are less prone to severe wildfires such as Central Europe.

A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines

Condition monitoring of induction machines (IMs) with the aim of increasing the machine’s lifetime, improving the efficiency and reducing the maintenance cost is necessary and inevitable. Among different types of methods presented for mechanical and electrical fault tracing in induction machines, stator current signature analysis has attracted great attention in recent decades. This popularity is mainly due to the non-invasive nature of this technique. A non-recursive method named variational mode decomposition (VMD) is used for the decomposition of any signal into several intrinsic mode functions (IMFs). This technique can be employed for detection of faulty components in a current signature. However, mode mixing of extracted IMFs makes the mechanical and electrical fault detection of IMs complicated, especially in the case where fault indices emerge close to the supply frequency. To achieve this, we rectify the signal of stator current prior to applying VMD. The main advantage of the presented approach is allowing the fault indices to be properly demodulated from the main frequency to avoid mode mixing phenomenon. The method shows that the dominant frequencies of the current signal can be isolated in each IMFs, appropriately. The proposed strategy is validated to detect the rotor asymmetric fault (RAF) in a wound rotor induction machine (WRIM), in both transient and steady-state conditions.

Modelling Spectral Unmixing of Geological Mixtures: An Experimental Study Using Rock Samples

Spectral unmixing of geological mixtures, such as rocks, is a challenging inversion problem because of nonlinear interactions of light with the intimately mixed minerals at a microscopic scale. The fine-scale mixing of minerals in rocks limits the sensor’s ability to identify pure mineral endmembers and spectrally resolve these constituents within a given spatial resolution. In this study, we attempt to model the spectral unmixing of two rocks, namely, serpentinite and granite, by acquiring their hyperspectral images in a controlled environment, having uniform illumination, using a laboratory-based imaging spectroradiometer. The endmember spectra of each rock were identified by comparing a limited set of pure hyperspectral image pixels with the constituent minerals of the rocks based on their diagnostic spectral features. A series of spectral unmixing paradigms for explaining geological mixtures, including those ranging from simple physics-based light interaction models (linear, bilinear, and polynomial models) to classification-based models (support vector machines (SVMs) and half Siamese network (HSN)), were tested to estimate the fractional abundances of the endmembers at each pixel position of the image. The analysis of the results of the spectral unmixing algorithms using the ground truth abundance maps and actual mineralogical composition of the rock samples (estimated using X-ray diffraction (XRD) analysis) indicate a better performance of the pure pixel-guided HSN model in comparison to the linear, bilinear, polynomial, and SVM-based unmixing approaches. The HSN-based approach yielded reduced errors of abundance estimation, image reconstruction, and mineralogical composition for serpentinite and granite. With its ability to train using limited pure pixels, the half-Siamese network model has a scope for spectrally unmixing rock samples of varying mineralogical composition and grain sizes. Hence, HSN-based approaches effectively address the modelling of nonlinear mixing in geological mixtures.

Endmember variability based abundance estimation of red and black soil over sparsely vegetated area using AVIRIS-NG hyperspectral image

Visible-near-and-short-wave-infrared hyperspectral images (HSI) have proven helpful for mapping the soil types over bare soils pixels. However, the accuracy of the traditional pixel-based classification methods decreases due to the spectral mixing between the significant agricultural features such as vegetation, soil and crop residue. In this context, spectral unmixing algorithms are handy for estimating the sub-pixel abundances of soil over sparsely vegetated areas. Most of the spectral unmixing methods focus on analyzing the HSI by considering the pixels as independent entities. However, in the HSIs of agricultural fields, the endmembers are spatially and temporally variable. It is also known that there exists a strong spatial correlation among neighbourhood pixels over agricultural fields. Therefore, both endmember variability and the neighbourhood spatial-spectral information are critical to estimate soil abundance accurately. Here we address the issue of estimating the abundances of two major soil types with spectral variability. The proposed approach combines the endmember bundle extraction with the spectral-spatial weighted unmixing approach (SSWU-SV). Experimental analysis has been carried out on the airborne HSI acquired by airborne-visible-and-infrared-imaging- spectrometer-next-generation (AVIRIS-NG) sensor. The quantitative analysis reveals that the proposed method consistently achieves a better unmixing performance than the traditional linear mixing model in terms of spectral angle mapper (SAM), and root-mean-square error (RMSE). Our results also indicate the saturation of normalized difference vegetation index (NDVI) at high vegetative fractions obtained from SSWU-SV.

Estimation and Validation of Sub-Pixel Needleleaf Cover Fraction in the Boreal Forest of Alaska to Aid Fire Management

Wildfires, which are a natural part of the boreal ecosystem in Alaska, have recently increased in frequency and size. Environmental conditions (high temperature, low precipitation, and frequent lightning events) are becoming favorable for severe fire events. Fire releases greenhouse gasses such as carbon dioxide into the environment, creating a positive feedback loop for warming. Needleleaf species are the dominant vegetation in boreal Alaska and are highly flammable. They burn much faster due to the presence of resin, and their low-lying canopy structure facilitates the spread of fire from the ground to the canopy. Knowing the needleleaf vegetation distribution is crucial for better forest and wildfire management practices. Our study focuses on needleleaf fraction mapping using a well-documented spectral unmixing approach: multiple endmember spectral mixture analysis (MESMA). We used an AVIRIS-NG image (5 m), upscaled it to 10 m and 30 m spatial resolutions, and applied MESMA to all three images to assess the impact of spatial resolution on sub-pixel needleleaf fraction estimates. We tested a novel method to validate the fraction maps using field data and a high-resolution classified hyperspectral image. Our validation method produced needleleaf cover fraction estimates with accuracies of 73%, 79%, and 78% for 5 m, 10 m, and 30 m image data, respectively. To determine whether these accuracies varied significantly across different spatial scales, we used the McNemar statistical test and found no significant differences between the accuracies. The findings of this study enhance the toolset available to fire managers to manage wildfire and for understanding changes in forest demography in the boreal region of Alaska across the high-to-moderate resolution scale.