nils-herrmann/clean_scirepeval huggingface dataset:
Nonlinear hyperspectral unmixing accounting for spatial illumination variability

This paper presents a new supervised algorithm for nonlinear hyperspectral unmixing. Based on the residual component analysis model, the proposed model assumes the linear model to be corrupted by an additive term that accounts for bilinear interactions between the endmembers. The proposed formulation considers also the effect of the spatial illumination variability. The parameters of the proposed model are estimated using a Bayesian strategy. This approach introduces prior distributions on the parameters of interest to take into account their known constraints. The resulting posterior distribution is optimized using a coordinate descent algorithm which allows us to approximate the maximum a posteriori estimator of the unknown model parameters. The proposed model and estimation algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inferences and the computational complexity when compared to the state-of-the-art algorithms.
Exploiting spatial information in semi-supervised hyperspectral image segmentation

We present a new semi-supervised segmentation algorithm suited to hyperspectral images, which takes full advantage of the spectral and spatial information available in the scenes. We mainly focus on problems involving very few labeled samples and a larger set of unlabeled samples. A multinomial logistic regression (MLR) is used to model the posterior class probability distributions, whereas a multilevel logistic level (MLL) prior is adopted to model the spatial information present in class label images. The multinomial logistic regressors are learnt using an expectation maximization (EM) type algorithm, where the class labels of the unlabeled samples are dealt with as unobserved random variables. The expectation step of the EM algorithm is computed using belief propagation (BP). In the maximization step of the EM algorithm, we compute the maximum a posterioi estimate (MAP) estimate of the multinomial logistic regressors. For the segmentation, we compute both the MAP solution and the maxi-mizer of the posterior marginal (MPM) provided by the belief propagation algorithm. We show, using the well-known AVIRIS Indian Pines data, that both solutions exhibit state-of-the-art performance.
Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data

This paper presents a new method of minimum volume class for hyperspectral unmixing, termed minimum volume simplex analysis (MVSA). The underlying mixing model is linear; i.e., the mixed hyperspectral vectors are modeled by a linear mixture of the end-member signatures weighted by the correspondent abundance fractions. MVSA approaches hyperspectral unmixing by fitting a minimum volume simplex to the hyperspectral data, constraining the abundance fractions to belong to the probability simplex. The resulting optimization problem is solved by implementing a sequence of quadratically constrained subproblems. In a final step, the hard constraint on the abundance fractions is replaced with a hinge type loss function to account for outliers and noise. We illustrate the state-of-the-art performance of the MVSA algorithm in unmixing simulated data sets. We are mainly concerned with the realistic scenario in which the pure pixel assumption (i.e., there exists at least one pure pixel per end member) is not fulfilled. In these conditions, the MVSA yields much better performance than the pure pixel based algorithms.
Multiple moving target detection and trajectory estimation using a single SAR sensor

A novel methodology is presented for determining the velocity and location of multiple moving targets using a single strip-map synthetic aperture radar (SAR) sensor. The so-called azimuth position uncertainty problem is therefore solved. The method exploits the structure of the amplitude and phase modulations of the returned echo from a moving target in the Fourier domain. A crucial step in the whole processing scheme is a matched filtering, depending on the moving target parameters, that simultaneously accounts for range migration and compresses two-dimensional signatures into one-dimensional ones without losing moving target information. A generalized likelihood ratio test approach is adopted to detect moving targets and derive their trajectory parameters. The effectiveness of the method is illustrated with synthetic and real data covering a wide range of targets velocities and signal-to-clutter ratios (SCRs). Even in the case of parallel to platform moving target motion, the most unfavorable scenario, the proposed method yields good results for, roughly, SCR > 10 dB.
Convex formulation for hyperspectral image classification with superpixels

The superpixels provided by an unsupervised segmentation algorithm are sets of neighboring pixels homogeneous in some sense. Therefore it is very likely that, in a classification problem, most pixels in a superpixel belong to the same class, namely if the homogeneity criterion is compatible with the class statistics. Superpixels are, therefore, a powerful device to express spatial contextual information. However, the exploitation of superpixels in a principled way is not straightforward. Recent efforts attack this problem under a discrete optimization framework, by including regularization terms promoting consistence of the labels in the superpixels and computing approximate labelings with graph-cut algorithms. The well known hardness of integer optimization problems is a major limitation of this line of attack. In this paper, we introduce a new strategy, based on convex relaxation, to include the spatial information provided by superpixels in classification problems. The convex relaxation of an integer optimization problem opens a door to include extra information, such as spatial partitioning information given by over-segmented superpixels. The convex optimization problem thus obtained is solved by using SALSA algorithm. Experimental results with the ROSIS Pavia University dataset illustrate the effectiveness of the proposed framework.
Generalized Composite Kernel Framework for Hyperspectral Image Classification

This paper presents a new framework for the development of generalized composite kernel machines for hyperspectral image classification. We construct a new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the hyperspectral data, without any weight parameters. The classifier adopted in this work is the multinomial logistic regression, and the spatial information is modeled from extended multiattribute profiles. In order to illustrate the good performance of the proposed framework, support vector machines are also used for evaluation purposes. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed framework leads to state-of-the-art classification performance in complex analysis scenarios.
High-energy electron beams produced by a Laser Wakefield accelerator

A Laser-Wakefiled acceleration (LWFA) experiment, aiming the production of high-quality electron relativistic beams, was performed at Astra-Gemini Laser Facility. The interaction was used sub-50 fs laser pulses with energies in the range 4 – 10 J and structured gas cells with lengths of 2 – 4 cm filled with hydrogen as target. Reliable and reproducible electron beams with energies close to 1 GeV were produced in a single stage. The use of plasma channels to enhance the acceleration process was also tested resulting in a significant energy gain signature.
Improving point cloud to surface reconstruction with generalized Tikhonov regularization

Point cloud rendering has a vital role in the user Quality of Experience for applications adopting point cloud based representations. While this is not a new area, it has recently become more relevant with the recent interest on point cloud coding by major standardization groups, notably JPEG and MPEG. The screened Poisson surface reconstruction is a state-of-the-art technique for generating a watertight surface mesh from the point cloud samples. While its screening component allows the surface to better fit the cloud points, this fitting may lead to undesired artifacts in the surface, notably when the point cloud is noisy. This paper proposes to improve this reconstruction method by making it more robust to noise by adopting a generalized Tikhonov regularization term. The proposed regularization approach smooths regions that should be flat while keeping the important details in the edges, thus creating more pleasant surface reconstructions.
Efficient computation of tr{TR-1} for Toeplitz matrices

An efficient algorithm for the computation of tr{TR/sup -1/}, where T and R are Toeplitz matrices and R is also symmetric positive definite, is presented. The method exploits the fact that the trace of TR/sup -1/ depends only on the sum of the diagonals of R/sup -1/, and not on the whole matrix R/sup -1/. To obtain this sum, a fast efficient technique, built upon the Trench (1964) algorithm for computing the inverse of a Toeplitz matrix, is developed. The complexity of the algorithm depends on the generation function of matrix R and is O(N ln N) for generic functions and O(p ln p) for AR(p) functions.
Fast hyperspectral unmixing in presence of sparse multiple scattering nonlinearities

This paper presents a novel nonlinear hyperspectral mixture model and its associated supervised unmixing algorithm. The model assumes a linear mixing model corrupted by an additive term which accounts for multiple scattering nonlinearities (NL). The proposed model generalizes bilinear models by taking into account higher order interaction terms. The inference of the abundances and nonlinearity coefficients of this model is formulated as a convex optimization problem suitable for fast estimation algorithms. This formulation accounts for constraints such as the sum-to-one and nonnegativity of the abundances, the non-negativity of the nonlinearity coefficients, and the spatial sparseness of the residuals. The resulting convex problem is solved using the alternating direction method of multipliers (ADMM) whose convergence is ensured theoretically. The proposed mixture model and its unmixing algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inference and the computational complexity when compared to the state-of-the-art algorithms.
Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors

Image deconvolution is formulated in the wavelet domain under the Bayesian framework. The well-known sparsity of the wavelet coefficients of real-world images is modeled by heavy-tailed priors belonging to the Gaussian scale mixture (GSM) class; i.e., priors given by a linear (finite of infinite) combination of Gaussian densities. This class includes, among others, the generalized Gaussian, the Jeffreys , and the Gaussian mixture priors. Necessary and sufficient conditions are stated under which the prior induced by a thresholding/shrinking denoising rule is a GSM. This result is then used to show that the prior induced by the "nonnegative garrote" thresholding/shrinking rule, herein termed the garrote prior, is a GSM. To compute the maximum a posteriori estimate, we propose a new generalized expectation maximization (GEM) algorithm, where the missing variables are the scale factors of the GSM densities. The maximization step of the underlying expectation maximization algorithm is replaced with a linear stationary second-order iterative method. The result is a GEM algorithm of O(NlogN) computational complexity. In a series of benchmark tests, the proposed approach outperforms or performs similarly to state-of-the art methods, demanding comparable (in some cases, much less) computational complexity.
Algorithms for imaging inverse problems under sparsity regularization

This paper reviews our recent work on the application of a class of techniques known as ADMM (alternating direction method of multipliers, which belongs to the family of augmented Lagrangian methods) to several imaging inverse problems under sparsity-inducing regularization. After reviewing ADMM, a formulation that allows handling problems with more than two terms is introduced; this formulation is then applied to a variety of problems, namely: standard image restoration/reconstruction from linear observations (e.g., compressive sensing, deconvolution, inpainting) with Gaussian or Poisson noise, using either analysis or synthesis regularization, and unconstrained or constrained optimization. We also show how the proposed framework can be used to address hybrid analysis-synthesis regularization. In all these cases, the proposed approach inherits the theoretic convergence guarantees of ADMM and achieve state-of-the-art speed.
Frame-based deconvolution of Poissonian images using alternating direction optimization

Restoration of Poissonian images is a class of inverse problem arising in fields such medical and astronomical imaging. Regularization criteria that combine the Poisson log-likelihood with a non-smooth convex regularizer lead to optimization problems with several difficulties: the log-likelihood does not have a Lipschitzian gradient; the regularizer is non-smooth; there is a non-negativity constraint. Using convex analysis tools, we give sufficient conditions for existence and uniqueness of solutions of these optimization problems for (frame-based) analysis and synthesis formulations. Then, we attack these problems with an adapted version of the alternating direction method of multipliers and show that sufficient conditions for convergence are met. The algorithm is shown to be competitive, often outperform, state-of-the-art methods.
Collaborative nonnegative matrix factorization for remotely sensed hyperspectral unmixing

In this paper, we develop a new algorithm for hyperspectral unmixing which can provide suitable endmembers (and their corresponding abundances) in a single step. Hence, the algorithm does not require a previous subspace identification step to estimate the number of endmembers as it can cope with the two most likely scenarios in practice (i.e., the number of endmembers is correctly determined or overestimated a priori). The proposed approach, termed collaborative NMF (CoNMF), uses a collaborative regularization prior which forces the abundances corresponding to the overestimated endmembers to zero, such that it is guaranteed that only the true endmembers have fractional abundance contributions and the estimation of the number of endmembers is not required in advance. The obtained experimental results demonstrate that the proposed method exhibits very good performance in case the number of endmember is not available a priori.
Phase unwrapping: a new max-flow/min-cut based approach

The paper presents a new max-flow/min-cut approach for recovering the absolute phase from modulo-2/spl pi/ phase, the so-called phase unwrapping (PU) problem. The adopted criterion is the minimization of the L/sup p/ norm of phase differences, leading to computationally demanding integer optimization problems. The unwrapped phase is computed iteratively through a sequence of binary optimizations, each one mapped onto a max-flow/min-cut problem on a certain graph. Accordingly, we name this new algorithm PUMF (for phase unwrapping max-flow). A set of experimental results illustrates the effectiveness of PUMF approach, namely for 0 < p < 1, where most competitors fail.
Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches

The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing in remote sensing. By using an available spectral library as a dictionary, the SR approach identifies the underlying materials in a given hyperspectral image by selecting a small subset of spectral samples in the dictionary to represent the whole image. A drawback with the current SR developments is that an actual spectral signature in the scene is often assumed to have zero mismatch with its corresponding dictionary sample, and such an assumption is considered too ideal in practice. In this paper, we tackle the spectral signature mismatch problem by proposing a dictionary-adjusted nonconvex sparsity-encouraging regression (DANSER) framework. The main idea is to incorporate dictionary-correcting variables in an SR formulation. A simple and low per-iteration complexity algorithm is tailor-designed for practical realization of DANSER. Using the same dictionary-correcting idea, we also propose a robust subspace solution for dictionary pruning. Extensive simulations and real-data experiments show that the proposed method is effective in mitigating the undesirable spectral signature mismatch effects.
A variable splitting augmented Lagrangian approach to linear spectral unmixing

This paper presents a new linear hyperspectral unmixing method of the minimum volume class, termed simplex identification via split augmented Lagrangian (SISAL). Following Craig's seminal ideas, hyperspectral linear unmixing amounts to finding the minimum volume simplex containing the hyperspectral vectors. This is a nonconvex optimization problem with convex constraints. In the proposed approach, the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The obtained problem is solved by a sequence of augmented Lagrangian optimizations. The resulting algorithm is very fast and able so solve problems far beyond the reach of the current state-of-the art algorithms. The effectiveness of SISAL is illustrated with simulated data.
Source Separation and Clustering of Phase-Locked Subspaces

It has been proven that there are synchrony (or phase-locking) phenomena present in multiple oscillating systems such as electrical circuits, lasers, chemical reactions, and human neurons. If the measurements of these systems cannot detect the individual oscillators but rather a superposition of them, as in brain electrophysiological signals (electo- and magneoencephalogram), spurious phase locking will be detected. Current source-extraction techniques attempt to undo this superposition by assuming properties on the data, which are not valid when underlying sources are phase-locked. Statistical independence of the sources is one such invalid assumption, as phase-locked sources are dependent. In this paper, we introduce methods for source separation and clustering which make adequate assumptions for data where synchrony is present, and show with simulated data that they perform well even in cases where independent component analysis and other well-known source-separation methods fail. The results in this paper provide a proof of concept that synchrony-based techniques are useful for low-noise applications.
Pansharpening Based on Semiblind Deconvolution

Many powerful pansharpening approaches exploit the functional relation between the fusion of PANchromatic (PAN) and MultiSpectral (MS) images. To this purpose, the modulation transfer function of the MS sensor is typically used, being easily approximated as a Gaussian filter whose analytic expression is fully specified by the sensor gain at the Nyquist frequency. However, this characterization is often inadequate in practice. In this paper, we develop an algorithm for estimating the relation between PAN and MS images directly from the available data through an efficient optimization procedure. The effectiveness of the approach is validated both on a reduced scale data set generated by degrading images acquired by the IKONOS sensor and on full-scale data consisting of images collected by the QuickBird sensor. In the first case, the proposed method achieves performances very similar to that of the algorithm that relies upon the full knowledge of the degrading filter. In the second, it is shown to outperform several very credited state-of-the-art approaches for the extraction of the details used in the current literature.
R-FUSE: Robust Fast Fusion of Multiband Images Based on Solving a Sylvester Equation

This letter proposes a robust fast multiband image fusion method to merge a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. Following the method recently developed by Wei et al., the generalized Sylvester matrix equation associated with the multiband image fusion problem is solved in a more robust and efficient way by exploiting the Woodbury formula, avoiding any permutation operation in the frequency domain as well as the blurring kernel invertibility assumption required in their method. Thanks to this improvement, the proposed algorithm requires fewer computational operations and is also more robust with respect to the blurring kernel compared with the one developed by Wei et al. The proposed new algorithm is tested with different priors considered by Wei et al. Our conclusion is that the proposed fusion algorithm is more robust than the one by Wei et al. with a reduced computational cost.
Semi-supervised hyperspectral image classification using a new (soft) sparse multinomial logistic regression model

In this work, we propose a new semi-supervised classification algorithm for remotely sensed hyperspectral images. The main contribution of this work is the development of new soft sparse multinomial logistic regression (S2MLR) model which exploits both hard and soft labels. In our terminology, these labels respectively correspond to labeled and unlabeled training samples. In order to obtain the soft labels, we use a recently proposed subspace-based MLR algorithm (MLRsub). The proposed semi-supervised algorithm represents an innovative contribution with regards to conventional semi-supervised learning algorithms that only assign hard labels to unlabeled samples. The effectiveness of our proposed method is evaluated via experiments with a widely used hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region in Indiana. Our results indicate that the proposed method provides state-of-the-art performance when compared to other methods.
Supervised Hyperspectral Image Classification With Rejection

Hyperspectral image classification is a challenging problem as obtaining complete and representative training sets is costly, pixels can belong to unknown classes, and it is generally an ill-posed problem. The need to achieve high classification accuracy may surpass the need to classify the entire image. To account for this scenario, we use classification with rejection by providing the classifier with an option not to classify a pixel and consequently reject it. We present and analyze two approaches for supervised hyperspectral image classification that combine the use of contextual priors with classification with rejection: 1) by jointly computing context and rejection and 2) by sequentially computing context and rejection. In the joint approach, rejection is introduced as an extra class that models the probability of classifier failure. In the sequential approach, rejection results from the hidden field associated with a marginal maximum a posteriori classification of the image. We validate both approaches on real hyperspectral data.