In this paper, we propose an approach based on an Alternating Direction Method of Multipliers (ADMM) to unmix hyperspectral data using a recently proposed linear mixing model in which the spectral variability phenomenon is spectrally modeled in a multiplicative manner. This model allows for pixel-wise variation of the endmembers, resulting in different versions of the reference component spectra being considered in each pixel of the image. The proposed ADMM-based unmixing algorithm involves new iterative update rules. The investigation also evaluates the performance of the designed algorithm against some literature ones previously proposed. To this end, experiments using synthetic hyperspectral data are carried out. Overall, the obtained results prove that the proposed algorithm is very attractive for hyperspectral unmixing taking the spectral variability phenomenon into account.

Marine mucilage outbreaks not only pose a threat to the marine ecosystems, but also are a detriment to economy and public-health. The recent marine mucilage outbreak of Spring 2021 in the Sea of Marmara, Türkiye, was one of most serious recorded mucilage outbreaks, lasting over three months and covering more than 1000 square kilometers. Recently, linear spectral unmixing based environmental monitoring of marine mucilage from hyperspectral data have been shown to provide easy to interpret analysis of this complex phenomenon, in terms of endmember signatures and fractional abundances. This paper carries the work forward and proposes nonlinear unmixing for the environmental monitoring of marine mucilage. Hyperspectral data acquired by the PRISMA mission are used in this investigation.

Disasters in marine ecosystems, such as the outbreak of mucilage in the inland Sea of Marmara in the spring of 2021, raise serious environmental, economic and public health concerns. Recently, methods based on fully unsupervised unmixing and dealing with spectral variability have made it possible to analyze marine mucilage, quantify its abundance and thus to map this harmful phenomenon. This work proposes to use an informed (or semi-supervised) unmixing technique, which is based on nonnegative matrix factorization and dealing with spectral variability, using a field-measured spectrum of high-density (or accumulated) mucilage, in order to detect and map, in particular, this marine material. The used technique is evaluated on real hyperspectral PRISMA data, and compared with other unmixing-based techniques.

In these investigations, a nonlinear spectral unmixing-based approach is considered to estimate, more accurately, some physical parameters of a flare, exploiting Visible Infrared Imaging Radiometer Suite (VIIRS) Night Time Lights (NTL) remote sensing data. These calculated parameters are then used to estimate flared gas volumes, through an intercepting zero polynomial regression model that exploits in-situ measurements. Experiments, using VIIRS NTL data, covering the flare, named FIT-M8-101A-1U and located in the Berkine basin (Hassi Messaoud) in Algeria, are performed. Then, the estimated flared gas volumes are compared with in-situ measurements.

In these investigations, a new informed linear mixing model addressing spectral variability with an associated informed penalized hyperspectral unmixing algorithm is first proposed. This algorithm, which optimizes a new cost function with original iterative and multiplicative update rules, is based on nonnegative matrix factorization. Then, this algorithm is used for detecting and mapping several mineral deposits in the Algerian Central Hoggar. This method unmixes the considered PRISMA hyperspectral remote sensing data by exploiting known spectra of some minerals of interest. Obtained abundance fraction maps are then used to establish a classification map of the investigated area, with the considered mineral deposit classes, at a finer spatial resolution.

Hyperspectral data, acquired by air/spaceborne sensors, are generally exposed to intra-class variability, making their unmixing process more complicated, in terms of precise estimations of endmember spectra and their corresponding abundance fractions, by means of the usual linear mixing model that ignores this concern. Accordingly, further advanced linear mixing models, which address this issue, were proposed recently. Several of them consider this intra-class variability in the spectral part of variables, whereas many ones consider the same phenomenon in the spatial part of variables. In this investigation, a novel linear mixing model is proposed to deal with this phenomenon. This model considers this concern both in the spectral and spatial parts of variables. Moreover, an associated penalized hyperspectral unmixing algorithm, based on multiplicative nonnegative matrix factorization, is designed for the proposed model. This algorithm proves to be practical as obviously reported by conducted experiments and obtained results.

In this paper, a novel approach, based on a multistage informed spectral unmixing technique, for detecting and mapping several mineral deposits in the Gara-Djebilet region (Southwestern Algeria), is presented. The considered technique, which is related to linear spectral unmixing methods, uses an iterative informed multiplicative nonnegative matrix factorization algorithm. This technique unmixes the used PRISMA hyperspectral remote sensing data by exploiting known spectra of the considered minerals. More precisely, the originality of this work consists in applying iteratively, in two stages, the considered informed unmixing algorithm. During the first stage, abundant but irrelevant minerals, with known spectra and present in the considered region, are detected, and are then removed from the original hyperspectral data, which thus lead to the creation of new hyperspectral data to which the considered unmixing technique is applied, during the second stage, in order to detect and map other relevant minerals, with known spectra, and possibly together with unknown materials. Experiments are carried out with the considered real hyperspectral data, which cover the studied area, to assess the potential of the proposed approach. The obtained results are mainly confirmed and validated by prior knowledge of the investigated area.

During oil extraction process, natural gases escape from wells, and the procedure for recovering these gases requires heavy investments from oil and gas companies. That is why, most often, they prefer to burn them with flares. This practice, which is very common by oil and gas companies in oil-producing countries, is highly emitting greenhouse gases. Under increasing pressure from the World Bank and environmental defenders, several producer countries are committed to reducing gas flaring. To this end, researchers in the oil and gas industry, academia and governments are working to develop approaches for determining ways to measure gas flaring and its emissions. Among the most widely used approaches, for local and global monitoring of gas flares, are those that exploit remote sensing data, particularly Night time Light (NtL) ones. Indeed, it is possible to extract, from such data, some physical parameters of flames produced by gas flares, and these parameters can be used to estimate annual volumes of flared gas. In this investigation, three spectral analysis-based approaches are tested to estimate flare physical parameters from Visible Infrared Imaging Radiometer Suite (VIIRS) NtL data. Linear and nonlinear spectral unmixing methods applied on NtL VIIRS data, are anticipated to improve, by using a pure spectrum of flames, results provided by the classical one that considers the modeling of the Planck law curve applied on the manipulated observed mixed data. Experiments, based on realistic synthetic VIIRS NtL data, are conducted and obtained results confirm the expected improvements.

Remote sensing hyperspectral images are usually subject to the intra-class variability phenomenon that complicates the precise estimation of endmember spectra and their abundance fractions when using the spectral unmixing process with the typical Linear Mixing Model (LMM), which ignores this concern. Thus, other refined LMMs, which deal with this issue, were developed. Some of them consider this spectral variability on the spectral part of variables, while other ones consider the same phenomenon on the spatial part of variables. In this work, a recent modified Augmented LMM (ALMM) is used to deal with the intra-class variability, considered on the spatial part of variables, by using smaller matrices that also obey the nonnegativity constraint. Furthermore, a projected gradient-based algorithm, based on Nonnegative Matrix Factorization (NMF), is proposed for the used modified ALMM. This Gradient-NMF-based technique proves to be useful as clearly reported by conducted experiments.

Hypersharpening consists in generating an unobservable high-spatial-resolution hyperspectral image by fusing an observed low-spatial-resolution hyperspectral image with an observed high-spatial-resolution panchromatic or multispectral one. The obtained image preserves the high spectral resolution of the first image and the high spatial resolution of the second one. Unlike standard hypersharpening methods that do not consider the spectral variability phenomenon, in this letter, a new approach, which addresses this phenomenon, is proposed for fusing hyperspectral and multispectral remote sensing images. This approach, linked to linear spectral unmixing methods, is based on an extension of nonnegative matrix factorization (NMF), namely the inertia-constrained pixel-by-pixel NMF (IP-NMF) algorithm. The developed fusion algorithm, called hyperspectral and multispectral data fusion based on IP-NMF (HMF-IPNMF), is applied to synthetic and real data sets. Experimental results clearly show that the developed fusion method yields sharpened hyperspectral images with higher spectral and spatial fidelities when compared to those provided by tested state-of-the-art methods that do not take spectral variability into account.

In these investigations, an informed unmixing-based approach is considered for detecting and mapping several mineral deposits in the Algerian Central Hoggar. The considered technique, which is related to linear spectral unmixing methods, uses an informed multiplicative nonnegative matrix factorization algorithm. This technique unmixes the used hyperspectral remote sensing data by exploiting known spectra of the considered minerals. Experiments are carried out, to assess the potential of the used technique, on real hyperspectral PRecursore IperSpettrale della Missione Applicativa (PRISMA) data, which cover the considered study area. Obtained maps are analyzed by using a geological map of the investigated region.

Spectral unmixing methods aim at estimating a collection of pure material spectra and their associated proportions in each pixel of the image. Such methods usually suppose that each pure material is represented by a unique spectrum in all image pixels. However, in many cases this assumption is no more valid for materials that exhibit spectral variability due to varying illumination and atmospheric conditions or material composition. Recently, a novel linear mixing model that handles the spectral variability phenomenon, modeled in a multiplicative form, was proposed with an associated unmixing method. In the present paper, two gradient-based approaches, which use the above mixing model and that are based on pixel-by-pixel nonnegative matrix factorization, are proposed. The first approach uses the projected gradient descent algorithm, whereas the second one employs a Newton update. The proposed algorithms minimize a cost function that takes into account the considered linear mixing model with the spectral variability phenomenon. Experiments, based on realistic synthetic data, are conducted to evaluate the performance of the proposed algorithms. The obtained results are also compared to those of methods from the literature. These test results show that the proposed approaches prove to be very attractive for unmixing hyperspectral remote sensing data with spectral variability.

Remote sensing hyperspectral sensors are often limited in their spatial resolutions, which leads to mixed pixels. The linear spectral unmixing process is frequently used to extract endmember spectra and their abundance fractions. The standard linear mixing model considers that each endmember is represented by the same spectral signature in the entire image. However, such a basic hypothesis is not relevant in most practical situations since the spectral signature of an endmember can spatially vary. This intra-class variability phenomenon can be considered by introducing the concept of classes of endmembers. Recently, a structured additively-tuned linear mixing model, with its constraints, was proposed, with an associated unmixing method, to address this phenomenon. That method, based on Nonnegative Matrix Factorization (NMF), optimizes a cost function with iterative and multiplicative update rules supplemented by additional constraints that control the spectral variability. In the present work, two penalization terms that more efficiently manage the spectral variability are added to the considered cost function, for the same structured mixing model, and new NMF-based iterative and multiplicative update rules are deduced for achieving the unmixing process taking the considered phenomenon into account. The proposed algorithm proves to be very attractive as clearly reported by conducted experiments based on synthetic data.

In this work, a hypersharpening approach, creating fused hyperspectral remote sensing images with high spatial and spectral resolutions, is introduced. This approach, linked to linear spectral unmixing (LSU) methods and based on a multiplicative nonnegative matrix factorization (NMF) technique, extends the Joint-Criterion NMF (JCNMF) algorithm, by addressing the spectral variability phenomenon. This method is designed for combining low spatial resolution hyperspectral and high spatial resolution multispectral data. It optimizes the considered criterion that deals with the spectral variability phenomenon by using a specific structure of involved matrices. The introduced algorithm, which uses multiplicative and iterative update rules, is applied to realistic synthetic data, and its effectiveness, in the spatial and spectral domains, is evaluated by considering commonly used assessment protocol and performance criteria. The obtained results prove that the introduced algorithm yields fused hyperspectral data with good spectral and spatial fidelities. These results also illustrate that the proposed algorithm significantly outperforms two tested literature ones that do not take the spectral variability phenomenon into account.

In a very recent paper, we introduced (i) a specific hyper-spectral mixing model for the sea bottom, based on a detailed physical analysis which includes the adjacency effect, and (ii) an associated unmixing method, which is not blind in the sense that it requires a prior estimation of various parameters of that mixing model. We here proceed much further, by first analytically showing that this model can be seen as a specific member of the general class of mixing models involving spectral variability. Therefore, we then process such data with the IP-NMF and UP-NMF blind unmixing methods that we recently proposed in other works to handle spectral variability. Such a variability especially occurs when sea depth significantly varies over the considered scene, and we show that IP-NMF and UP-NMF then yield significantly better pure spectra estimation than a classical method from the literature which was not designed to handle such a variability.

This paper presents a fusion method generating unobservable sharpened hyperspectral remote sensing data with high spatial and spectral resolutions. This method, related to linear spectral unmixing (LSU) techniques, and based on nonnegative matrix factorization (NMF), introduces Joint-Variables NMF (JVNMF) for fusing observable low spatial resolution hyperspectral and high spatial resolution multispectral data. It optimizes a joint-variables criterion that exploits spatial and spectral degradation models between the two considered images, and therefore considers a reduced number of unknown variables. This approach, called Grd-JVNMF, is a gradient-based method and uses iterative update rules. The proposed method is applied to realistic synthetic and semi-real data, and its effectiveness, in spatial and spectral domains, is evaluated with established performance criteria. Experimental results show that the proposed Grd-JVNMF method yields multi-sharpened hyperspectral data with good spectral and spatial fidelities. These tests also show that the proposed method outperforms tested literature ones.

Hyperspectral remote sensing data offer unique opportunities for the characterization of land surface in urban areas. However, no hyperspectral- unmixing based studies have been conducted to automatically detect photovoltaic panels, which represent one of the important components of energy systems in such areas. In this paper, a hyperspectral-unmixing based method is proposed to detect photovoltaic panels and to estimate their areas. This approach is based on an original multiplicative nonnegative matrix factorization (NMF) algorithm with some known photovoltaic panel spectra. The proposed method can be considered as a partial/informed NMF approach. Experiments are conducted on realistic synthetic and real data to evaluate the performance of the proposed approach. In both cases, obtained results show that the proposed method yields much better overall performance than a method from the literature.

A strong intern dynamic characterizes towns, a very high spatial heterogeneity of their elements, their 3D geometric shapes (horizontal and vertical) inducing shadows, and their large variety of materials. These characteristics make the collection of information of land surface properties and urban descriptors more delicate. Due to the enhancement of spatial to deepen the observation of urban areas. Nevertheless, such a type of sensors would not contribute to the characterization of the urban land surface properties (chemical composition of materials, species of vegetation, quality of soils, etc.). They and show great potentials might consider Hyperspectral imagery capacities as providing useful products but it becomes mandatory to define which type of information these different sensors can deliver. The ANR HYEP project has the purpose to demonstrate the benefit of a second generation of hyperspectral space borne mission characterized by a high spatial resolution (8m GSD) and a high temporal revisit. After a detailed description of the motivation of such a proposal, applications are given focused on urban vegetation, sealed and impervious areas, solar panel area estimation.

Hypersharpening aims at combining an observable low-spatial resolution hyperspectral image with a high-spatial resolution remote sensing image, in particular a multispectral one, to generate an unobservable image with the high spectral resolution of the former and the high spatial resolution of the latter. In this paper, two such new fusion methods are proposed. These methods, related to linear spectral unmixing techniques, and based on nonnegative matrix factorization (NMF), optimize a new joint criterion and extend the recently proposed joint NMF (JNMF) method. The first approach, called gradient-based joint-criterion NMF (Grd-JCNMF), is a gradient-based method. The second one, called multiplicative JCNMF (Mult-JCNMF), uses new designed multiplicative update rules. These two JCNMF approaches are applied to synthetic and semireal data, and their effectiveness, in spatial and spectral domains, is evaluated with commonly used performance criteria. Experimental results show that the proposed JCNMF methods yield sharpened hyperspectral data with good spectral and spatial fidelities. The obtained results are compared with the performance of two NMF-based methods and one approach based on a sparse representation. These results show that the proposed methods significantly outperform the well-known coupled NMF sharpening method for most performance figures. Also, the proposed Mult-JCNMF method provides the results that are similar to those obtained by JNMF, with a lower computational cost. Compared with the tested sparse-representation-based approach, the proposed methods give better results. Moreover, the proposed Grd-JCNMF method considerably surpasses all other tested methods.

In this paper, a new projected-gradient method for bilinear matrix factorization with nonnegativity constraints is proposed for extracting hyperspectral endmember spectra. The proposed method is designed for a bilinear mixing model faced in urban hyperspectral remote sensing images. Experiments based on realistic synthetic data, generated according to the considered bilinear mixing model, are conducted to evaluate the performance of the proposed method and of approaches from the literature. Experimental results show that the proposed method yields much better overall performance than the used literature approaches.

In this paper, we propose a new multi-sharpening approach for improving the spatial resolution of hyperspectral data. This approach, based on the linear-quadratic spectral unmixing concept, uses a linear-quadratic nonnegative matrix factorization multiplicative algorithm. Our method first consists in unmixing the low spatial resolution hyperspectral data and high spatial resolution multispectral data. The obtained high resolution spectral and spatial parts of information are then recombined, according to the linear-quadratic mixing model, in order to obtain unobservable multi-sharpened high spatial resolution hyperspectral data. Experiments, based on realistic synthetic and real data, are carried out to evaluate the performance of the proposed approach and of linear nonnegative matrix factorization-based approaches from the literature. We show that our proposed approach significantly outperforms the used literature methods.