nils-herrmann/clean_scirepeval huggingface dataset:
Uncertainty propagation from atmospheric parameters to sparse hyperspectral unmixing

Sparse hyperspectral unmixing is a widely used technique in remote sensing data characterization. It aims at inferring, from a large spectral library, the pure spectral signatures (endmembers) present in each pixel of a hyperspectral image, jointly with their corresponding abundances. The input to sparse unmixing is represented, thus, by a hyperspectral image acquired from a platform flying at high altitude and a spectral library compiled using laboratory measurements. The reflectance datacube results from a complex ensemble of algorithms which translate the digital numbers stored by the sensor to meaningful ground reflectance, including the removal of atmospheric influence. A recurrent question in the research community does not have an answer yet: how does the atmospheric composition at the time of the flight influence the fractional abundances retrieved via sparse unmixing? This is a fundamental question, as the atmospheric parameters are subject to uncertainties, being very difficult to know them in all pixels. In this paper, we investigate how the uncertainty in two atmospheric parameters: water vapor content and visibility range, propagates to the final abundance maps via atmospheric correction of the sensed image. Our experiments reveal that sparse unmixing is more robust to uncertainty in those parameters and performs better in terms of accuracy than unmixing with image-based endmembers.
Supervised hyperspectral image segmentation: A convex formulation using hidden fields

Image segmentation is fundamentally a discrete problem. It consists of finding a partition of the image domain such that the pixels in each element of the partition exhibit some kind of similarity. The optimization is obtained via integer optimization which is NP-hard, apart from few exceptions. We sidestep from the discrete nature of image segmentation by formulating the problem in the Bayesian framework and introducing a hidden set of real-valued random fields determining the probability of a given partition. Armed with this model, the original discrete optimization is converted into a convex program. To infer the hidden fields, we introduce the Segmentation via the Constrained Split Augmented Lagrangian Shrinkage Algorithm (SegSALSA). The effectiveness of the proposed methodology is illustrated with hyperspectral image segmentation.
Hyperspectral image denoising based on global and non-local low-rank factorizations

The ever increasing spectral resolution of the hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise of the measurements, thus calling for effective denoising techniques. HSIs from the real world live in low dimensional subspaces and are self-similar. The low dimensionality stems from the high correlation existing among the reflectance vectors and the self-similarity is common to images of the real world. In this paper, we exploit the above two properties. The low dimensionality is a global property, which enables the denoising to be formulated just with respect to the subspace representation coefficients, thus greatly improving the denoising performance and reducing the processing computational complexity. The self-similarity is exploited via low-rank tensor factorization of non-local similar 3D-patches. The proposed factorization hinges on optimal shrinkage/thresholding of SVD singular value of low-rank tensor unfoldings. As a result, the proposed method has no parameters, apart from the noise variance. Its effectiveness is illustrated in a comparison with state-of-the-art competitors.
A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors

The development of multisensor systems in recent years has led to great increase in the amount of available remote sensing data. Image fusion techniques aim at inferring high quality images of a given area from degraded versions of the same area obtained by multiple sensors. This paper focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: 1) a low spatial resolution multispectral image and 2) a high spatial resolution panchromatic image. We introduce a new variational model based on spatial and spectral sparsity priors for the fusion. In the spectral domain, we encourage low-rank structure, whereas in the spatial domain, we promote sparsity on the local differences. Given the fact that both panchromatic and multispectral images are integrations of the underlying continuous spectra using different channel responses, we propose to exploit appropriate regularizations based on both spatial and spectral links between panchromatic and fused multispectral images. A weighted version of the vector total variation norm of the data matrix is employed to align the spatial information of the fused image with that of the panchromatic image. With regard to spectral information, two different types of regularization are proposed to promote a soft constraint on the linear dependence between the panchromatic and fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low-resolution multispectral images by linear regression while the second one employs the principal component pursuit to obtain a robust recovery of the underlying low-rank structure. We also show that the two regularizers are strongly related. The basic idea of both regularizers is that the fused image should have low-rank and preserve edge locations. We use a variation of the recently proposed split augmented Lagrangian shrinkage algorithm to effectively solve the proposed variational formulations. Experimental results on simulated and real remote sensing images show the effectiveness of the proposed pansharpening method compared with the state-of-the-art.
Hyperspectral image reconstruction from random projections on GPU

Hyperspectral data compression and dimensionality reduction has received considerable interest in recent years due to the high spectral resolution of these images. Contrarily to the conventional dimensionality reduction schemes, the spectral compressive acquisition method (SpeCA) performs dimensionality reduction based on random projections. The SpeCA methodology has applications in Hyperspectral Compressive Sensing and also in dimensionality reduction. Due to the extremely large volumes of data collected by imaging spectrometers, high performance computing architectures are needed for data compression of high dimensional hyperspectral data under real-time constrained applications. In this paper a parallel implementation of SpeCA on Graphics Processing Units (GPUs) using the compute unified device architecture (CUDA) is proposed. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore, achieving high GPU occupancy. The experimental results obtained for simulated and real hyperspectral data sets reveal speedups up to 21 times, which demonstrates that the GPU implementation can significantly accelerate the methods execution over big datasets while maintaining the methods accuracy.
Maximum-a-posteriori estimation with unknown regularisation parameters

This paper presents two hierarchical Bayesian methods for performing maximum-a-posteriori inference when the value of the regularisation parameter is unknown. The methods are useful for models with homogenous regularisers (i.e., prior sufficient statistics), including all norms, composite norms and compositions of norms with linear operators. A key contribution of this paper is to show that for these models the normalisation factor of the prior has a closed-form analytic expression. This then enables the development of Bayesian inference techniques to either estimate regularisation parameters from the observed data or, alternatively, to remove them from the model by marginalisation followed by inference with the marginalised model. The effectiveness of the proposed methodologies is illustrated on applications to compressive sensing using an l1-wavelet analysis prior, where they outperform a state-of-the-art SURE-based technique, both in terms of estimation accuracy and computing time.
Sparse Distributed Multitemporal Hyperspectral Unmixing

Blind hyperspectral unmixing jointly estimates spectral signatures and abundances in hyperspectral ima-ges (HSIs). Hyperspectral unmixing is a powerful tool for analyzing hyperspectral data. However, the usual huge size of HSIs may raise difficulties for classical unmixing algorithms, namely, due to limitations of the hardware used. Therefore, some researchers have considered distributed algorithms. In this paper, we develop a distributed hyperspectral unmixing algorithm that uses the alternating direction method of multipliers and  ${\ell _{1}}$  sparse regularization. The hyperspectral unmixing problem is split into a number of smaller subproblems that are individually solved, and then the solutions are combined. A key feature of the proposed algorithm is that each subproblem does not need to have access to the whole HSI. The algorithm may also be applied to multitemporal HSIs with due adaptations accounting for variability that often appears in multitemporal images. The effectiveness of the proposed algorithm is evaluated using both simulated data and real HSIs.
Unmixing sparse hyperspectral mixtures

Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or atoms), is a hard combinatorial problem which, as in other areas, has been increasingly addressed. This paper studies the efficiency of the sparse regression techniques in the spectral unmixing problem by conducting a comparison between four different approaches: Moore-Penrose Pseudoinverse, Orthogonal Matching Pursuit (OMP), Iterative Spectral Mixture Analysis (ISMA) and l2 - l1 sparse regression techniques, which are of widespread use in compressed sensing. We conclude that the l2-l1 sparse regression techniques, implemented here by Iterative Shrinkage/Thresholding (TwIST) algorithm, yield the state-of-the-art in the hyperspectral unmixing area.
Semi-supervised hyperspectral image segmentation

This paper presents a new semi-supervised segmentation algorithm, suited to high dimensional data, of which hyperspectral images are an example. The algorithm implements two main steps: (a) semisupervised learning, used to infer the class distributions, followed by (b) segmentation, by inferring the labels from a posterior density built on the learned class distributions and on a Markov random field. The class distributions are modeled with a multinomial logistic regression, where the regressors are learned using both labeled and, through a graph-based technique, unlabeled samples. The prior on the labels is a multi-level logistic model. The maximum a posterior segmentation is computed by the α-Expansion min-cut based integer optimization algorithm. We give experimental evidence that the spatial prior greatly improves the segmentation performance, with respect to that of a semi-supervised classifier. The effectiveness of the proposed method is demonstrated with simulated and real data.
Does independent component analysis play a role in unmixing hyperspectral data?

Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2)sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signal-to-noise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.
Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing

Convex optimization problems are common in hyperspectral unmixing. Examples are the constrained least squares (CLS) problem used to compute the fractional abundances in a linear mixture of known spectra, the constrained basis pursuit (CBP) to find sparse (i.e., with a small number of terms) linear mixtures of spectra, selected from large libraries, and the constrained basis pursuit denoising (CBPDN), which is a generalization of BP to admit modeling errors. In this paper, we introduce two new algorithms to efficiently solve these optimization problems, based on the alternating direction method of multipliers, a method from the augmented Lagrangian family. The algorithms are termed SUnSAL (sparse unmixing by variable splitting and augmented Lagrangian) and C-SUnSAL (constrained SUnSAL). C-SUnSAL solves the CBP and CBPDN problems, while SUnSAL solves CLS as well as a more general version thereof, called constrained sparse regression (CSR). C-SUnSAL and SUnSAL are shown to outperform off-the-shelf methods in terms of speed and accuracy.
Majorization–Minimization Algorithms for Wavelet-Based Image Restoration

Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to very high-dimensional optimization problems involving the following difficulties: the non-Gaussian (heavy-tailed) wavelet priors lead to objective functions which are nonquadratic, usually nondifferentiable, and sometimes even nonconvex; the presence of the convolution operator destroys the separability which underlies the simplicity of wavelet-based denoising. This paper presents a unified view of several recently proposed algorithms for handling this class of optimization problems, placing them in a common majorization-minimization (MM) framework. One of the classes of algorithms considered (when using quadratic bounds on nondifferentiable log-priors) shares the infamous ¿singularity issue¿ (SI) of ¿iteratively re weighted least squares¿ (IRLS) algorithms: the possibility of having to handle infinite weights, which may cause both numerical and convergence issues. In this paper, we prove several new results which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms. Exploiting the unified MM perspective, we introduce a new algorithm, resulting from using bounds for nonconvex regularizers; the experiments confirm the superior performance of this method, when compared to the one based on quadratic majorization. Finally, an experimental comparison of the several algorithms, reveals their relative merits for different standard types of scenarios.
Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning

This paper presents a new semisupervised segmentation algorithm, suited to high-dimensional data, of which remotely sensed hyperspectral image data sets are an example. The algorithm implements two main steps: 1) semisupervised learning of the posterior class distributions followed by 2) segmentation, which infers an image of class labels from a posterior distribution built on the learned class distributions and on a Markov random field. The posterior class distributions are modeled using multinomial logistic regression, where the regressors are learned using both labeled and, through a graph-based technique, unlabeled samples. Such unlabeled samples are actively selected based on the entropy of the corresponding class label. The prior on the image of labels is a multilevel logistic model, which enforces segmentation results in which neighboring labels belong to the same class. The maximum a posteriori segmentation is computed by the α-expansion min-cut-based integer optimization algorithm. Our experimental results, conducted using synthetic and real hyperspectral image data sets collected by the Airborne Visible/Infrared Imaging Spectrometer system of the National Aeronautics and Space Administration Jet Propulsion Laboratory over the regions of Indian Pines, IN, and Salinas Valley, CA, reveal that the proposed approach can provide classification accuracies that are similar or higher than those achieved by other supervised methods for the considered scenes. Our results also indicate that the use of a spatial prior can greatly improve the final results with respect to a case in which only the learned class densities are considered, confirming the importance of jointly considering spatial and spectral information in hyperspectral image segmentation.
An analysis of collaborative representation schemes for the classification of hyperspectral images

Collaborative-based representation classifiers have widely spread in the latest years achieving remarkable results in signal and image processing tasks. In this paper, we consider these approaches for the hyperspectral image classification. Specifically, we focus on collaborative and sparse representation classifiers and we perform an investigation on the role of the different regularizations and constraints that can be considered with respect to the classification performance. In addition, we propose to consider the Nearest Subspace Classifier with regularization which, from the experiments, has proven to be a competitive classification technique. Experimental results have been conducted considering both spectral and spatial features of a real hyperspectral image.
Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation

This paper presents a variational-based approach for fusing hyperspectral and multispectral images. The fusion problem is formulated as an inverse problem whose solution is the target image assumed to live in a lower dimensional subspace. A sparse regularization term is carefully designed, relying on a decomposition of the scene on a set of dictionaries. The dictionary atoms and the supports of the corresponding active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved via alternating optimization with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients. Simulation results demonstrate the efficiency of the proposed algorithm when compared with state-of-the-art fusion methods.
Signal and Image Processing in Hyperspectral Remote Sensing [From the Guest Editors]

In recent years, it has become clear that hyperspectral imaging has formed a core area within the geoscience and remote sensing community. Armed with advanced optical sensing technology, hyperspectral imaging offers high spectral resolution-a hyperspectral image can contain more than 200 spectral channels (rather than a few channels as in multispectral images), covering visible and near-infrared wavelengths at a resolution of about 10 nm. The result, on one hand, is significant expansion in data sizes. A captured scene can easily take 100 MB, or more. On the other hand, the vastly increased spectral information content available in hyperspectral images (or large spectral degrees of freedom in signal processing languages) creates a unique opportunity that may have previously been seen as impossible in multispectral remote sensing. We can detect difficult targets, for example, those appearing at a subpixel level. We can perform image classification with greatly improved accuracy. We can also identify underlying materials in a captured scene without prior information of the materials to be encountered, by carrying out blind unmixing.
The ZπM algorithm: a method for interferometric image reconstruction in SAR/SAS

This paper presents an effective algorithm for absolute phase (not simply modulo-2-pi) estimation from incomplete, noisy and modulo-2pi observations in interferometric aperture radar and sonar (InSAR/InSAS). The adopted framework is also representative of other applications such as optical interferometry, magnetic resonance imaging and diffraction tomography. The Bayesian viewpoint is adopted; the observation density is 2-pi-periodic and accounts for the interferometric pair decorrelation and system noise; the a priori probability of the absolute phase is modeled by a compound Gauss-Markov random field (CGMRF) tailored to piecewise smooth absolute phase images. We propose an iterative scheme for the computation of the maximum a posteriori probability (MAP) absolute phase estimate. Each iteration embodies a discrete optimization step (Z-step), implemented by network programming techniques and an iterative conditional modes (ICM) step (pi-step). Accordingly, the algorithm is termed ZpiM, where the letter M stands for maximization. An important contribution of the paper is the simultaneous implementation of phase unwrapping (inference of the 2pi-multiples) and smoothing (denoising of the observations). This improves considerably the accuracy of the absolute phase estimates compared to methods in which the data is low-pass filtered prior to unwrapping. A set of experimental results, comparing the proposed algorithm with alternative methods, illustrates the effectiveness of our approach.
GPU implementation of a hyperspectral coded aperture algorithm for compressive sensing

This paper presents a new parallel implementation of a previously hyperspectral coded aperture (HYCA) algorithm for compressive sensing on graphics processing units (GPUs). HYCA method combines the ideas of spectral unmixing and compressive sensing exploiting the high spatial correlation that can be observed in the data and the generally low number of endmembers needed in order to explain the data. The proposed implementation exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs using shared memory and coalesced accesses to memory. The proposed algorithm is evaluated not only in terms of reconstruction error but also in terms of computational performance using two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN. Experimental results using real data reveals signficant speedups up with regards to serial implementation.
Maximum likelihood estimation of spectral moments at low signal to noise ratios

A maximum likelihood (ML) estimator of spectral moments of a zero-mean complex Gaussian vector process, immersed in independent additive Gaussian white noise is proposed. The covariance function is assumed known in advance, apart from a vector of parameters which are related with the spectral moments. Since the maximization of the log-likelihood function yields a highly cumbersome algorithm, a more manageable objective function is considered. This objective function provides estimates consistent with probability one, and that are asymptotically efficient, which are the asymptotic properties of the ML estimator. For finite sample sizes, and signal to noise ratio (SNR) tending to zero, the results are similar to the ML results. Statistical characterization and simulation examples are presented.<<ETX>>