Salah Eddine Brezini

In this paper, a novel method for fusing hyperspectral and multispectral images to generate a high-spatial-resolution hyperspectral image, that has both a high spatial and a high spectral resolution, is presented. The introduced method employs an extended version of the Linear Mixing Model (LMM) addressing spectral variability (SV). The proposed method consists of four main stages: (1) constructing a spectral library through the generalized linear mixing model (GLMM), (2) applying a spectral library pruning strategy, (3) extracting spatial information from the multispectral image using a sparse regression technique and (4) generating the fused hyperspectral product. The method is evaluated on synthetic datasets and the results clearly demonstrate its effectiveness, yielding fusion products with superior spectral and spatial fidelity.

The aim of fusing hyperspectral and multispectral images is to overcome the limitation of remote sensing hyperspectral sensors by improving their spatial resolutions. This process, also known as hypersharpening, generates an unobserved high-spatial-resolution hyperspectral image. To this end, several hypersharpening methods have been developed, however most of them do not consider the spectral variability phenomenon; therefore, neglecting this phenomenon may cause errors, which leads to reducing the spatial and spectral quality of the sharpened products. Recently, new approaches have been proposed to tackle this problem, particularly those based on spectral unmixing and using parametric models. Nevertheless, the reported methods need a large number of parameters to address spectral variability, which inevitably yields a higher computation time compared to the standard hypersharpening methods. In this paper, a new hypersharpening method addressing spectral variability by considering the spectra bundles-based method, namely the Automated Extraction of Endmember Bundles (AEEB), and the sparsity-based method called Sparse Unmixing by Variable Splitting and Augmented Lagrangian (SUnSAL), is introduced. This new method called Hyperspectral Super-resolution with Spectra Bundles dealing with Spectral Variability (HSB-SV) was tested on both synthetic and real data. Experimental results showed that HSB-SV provides sharpened products with higher spectral and spatial reconstruction fidelities with a very low computational complexity compared to other methods dealing with spectral variability, which are the main contributions of the designed method.

Hypersharpening results in creating unobservable hyperspectral remote sensing data with high spectral and spatial resolutions, by combining a hyperspectral image, characterized by a high spectral resolution, with a high spatial resolution multispectral one. In this paper, a new hypersharpening method, based on the linear spectral unmixing concept, is proposed. This one, based on a multiplicative Nonnegative Matrix Factorization (NMF) technique, extends the Coupled NMF (CNMF) algorithm by addressing the spectral variability phenomenon. The introduced algorithm that uses multiplicative and iterative update rules minimizes a cost function that takes into account the specific structure of involved matrices, in order to deal with the spectral variability phenomenon. The proposed method is applied to realistic synthetic data, and its effectiveness is evaluated with established performance criteria. The obtained results show that the developed extended CNMF algorithm yields sharpened hyperspectral data with fine spectral and spatial fidelities. Also, these results demonstrate that the proposed technique outperforms the tested hypersharpening literature approaches.

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.

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.

Remote sensing hyperspectral sensors are often limited in their spatial resolutions, which results in mixed pixels. The mixture is, usually, assumed to be linear and linear spectral unmixing techniques are employed to unmix observed pixel spectra. Most of these techniques consider that each endmember is represented by the same spectrum in the whole image. Nevertheless, in various situations, each endmember needs to be represented by slightly different spectra in all observed pixels. This spectral variability phenomenon must be tackled by introducing the concept of classes of endmembers. In this paper, a structured additively-tuned linear mixing model, without physical considerations, is first introduced to address this phenomenon. Then, an algorithm, based on nonnegative matrix factorization, is proposed for unmixing the considered data. This algorithm, which minimizes a cost function by using multiplicative update rules supplemented by additional constraints, derives, for each class of endmembers, slightly different estimated spectra in all pixels. The designed update rules are obtained by considering the structured variables introduced in the used mixing model. To assess the performance of the designed algorithm, experiments, based on realistic synthetic data, are conducted and obtained results are compared to those of approaches from the literature. This comparison shows that the proposed approach outperforms all other tested methods.

Remote sensing hyperspectral sensors, with high spectral resolution, allow precise classification of endmembers present in imaged areas. These sensors have a limited spatial resolution, which results in mixed pixels. The mixture is usually assumed to be linear and blind linear spectral unmixing (LSU) methods are used to unmix all observed pixel spectra. Most blind LSU approaches assume that each endmember is represented by a unique spectrum in all image pixels. But, in many practical applications, this assumption is not valid and more complex models are needed to describe other phenomena, e.g. when each endmember needs to be represented by slightly different spectra in all image pixels. This spectral variability must be handled by replacing the concept of endmembers by classes of endmembers, to avoid errors when processing the considered data. In this paper, a new linear mixing model is firstly introduced in order to handle the spectral variability. In the proposed model, the endmember spectra are additively tuned. Then, an algorithm, based on pixel-by-pixel nonnegative matrix factorization, is proposed for unmixing the considered data. That algorithm, which derives, for each class of endmembers, slightly different estimated spectra in all pixels, optimizes a cost function and uses additional constraints that are related to the introduced linear mixing model. Experiments, based on realistic synthetic data, are conducted to evaluate the performance of the proposed algorithm. The obtained results are compared to those of three approaches from the literature. These test results show that the proposed approach outperforms all other tested methods.