nils-herrmann/clean_scirepeval huggingface dataset:
Markov random fields for joint unmixing and segmentation of hyperspectral images

This paper studies a new Bayesian algorithm for the unmixing of hyperspectral images. The proposed Bayesian algorithm is based on the well-known linear mixing model (LMM). Spatial correlations between pixels are introduced using hidden variables, or labels, and modeled via a Potts-Markov random field. We assume that the pure materials (or endmembers) contained in the image are known a priori or have been extracted by using an endmember extraction algorithm. The mixture coefficients (referred to as abundances) of the whole hyperspectral image are then estimated by using a hierarchical Bayesian algorithm. A reparametrization of the abundances is considered to handle the physical constraints associated to these parameters. Appropriate prior distributions are assigned to the other parameters and hyperparameters associated to the proposed model. To alleviate the complexity of the resulting joint distribution, a hybrid Gibbs algorithm is developed, allowing one to generate samples that are asymptotically distributed according to the full posterior distribution of interest. The generated samples are finally used to estimate the unknown model parameters. Simulations on synthetic data illustrate the performance of the proposed method.
Joint Segmentation of Piecewise Constant Autoregressive Processes by Using a Hierarchical Model and a Bayesian Sampling Approach

We propose a joint segmentation algorithm for piecewise constant autoregressive (AR) processes recorded by several independent sensors. The algorithm is based on a hierarchical Bayesian model. Appropriate priors allow us to introduce correlations between the change locations of the observed signals. Numerical problems inherent to Bayesian inference are solved by a Gibbs sampling strategy. The proposed joint segmentation methodology yields improved segmentation results when compared with parallel and independent individual signal segmentations. The initial algorithm is derived for piecewise constant AR processes whose orders are fixed on each segment. However, an extension to models with unknown model orders is also discussed. Theoretical results are illustrated by many simulations conducted with synthetic signals and real arc-tracking and speech signals
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery

This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for nonnegativity and full-additivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images.
Change detection for optical and radar images using a Bayesian nonparametric model coupled with a Markov random field

This paper introduces a Bayesian non parametric (BNP) model associated with a Markov random field (MRF) for detecting changes between remote sensing images acquired by homogeneous or heterogeneous sensors. The proposed model is built for an analysis window which takes advantage of the spatial information via an MRF. The model does not require any a priori knowledge about the number of objects contained in the window thanks to the BNP framework. The change detection strategy can be divided into two steps. First, the segmentation of the two images is performed using a region based approach. Second, the joint statistical properties of the objects in the two images allows an appropriate manifold to be defined. This manifold describes the relationships between the different sensor responses to the observed scene and can be learnt from a training unchanged area. It allows us to build a similarity measure between the images that can be used in many applications such as change detection or image registration. Simulation results conducted on synthetic and real optical and synthetic aperture radar (SAR) images show the efficiency of the proposed method for change detection.
A robust test for nonlinear mixture detection in hyperspectral images

This paper studies a pixel by pixel nonlinearity detector for hyperspectral image analysis. The reflectances of linearly mixed pixels are assumed to be a linear combination of known pure spectral components (endmembers) contaminated by additive white Gaussian noise. Nonlinear mixing, however, is not restricted to any prescribed nonlinear mixing model. The mixing coefficients (abundances) satisfy the physically motivated sum-to-one and positivity constraints. The proposed detection strategy considers the distance between an observed pixel and the hyperplane spanned by the endmembers to decide whether that pixel satisfies the linear mixing model (null hypothesis) or results from a more general nonlinear mixture (alternative hypothesis). The distribution of this distance is derived under the two hypotheses. Closed-form expressions are then obtained for the probabilities of false alarm and detection as functions of the test threshold. The proposed detector is compared to another nonlinearity detector recently investigated in the literature through simulations using synthetic data. It is also applied to a real hyperspectral image.
Comparison of ship detection performance based on the degree of polarization in hybrid/compact and linear dual-pol SAR imagery

Synthetic Aperture Radar (SAR) is a powerful tool strongly employed in maritime monitoring and surveillance. Among different polarimetric SAR modes, dual-pol SAR data are widely used for monitoring large ocean and coastal areas. The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field. The performance of the DoP is studied for ship detection under different polarizations in hybrid/compact and linear dual-pol SAR imagery. Experiments are performed on C-band polarimetric data acquired by RADARSAT-2 over San Francisco Bay and the Strait of Gibraltar.
Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm

This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are post-nonlinear functions of unknown pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated using second-order polynomials leading to a polynomial post-nonlinear mixing model. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding an unsupervised nonlinear unmixing algorithm. Due to the large number of parameters to be estimated, an efficient Hamiltonian Monte Carlo algorithm is investigated. The classical leapfrog steps of this algorithm are modified to handle the parameter constraints. The performance of the unmixing strategy, including convergence and parameter tuning, is first evaluated on synthetic data. Simulations conducted with real data finally show the accuracy of the proposed unmixing strategy for the analysis of hyperspectral images.
Detection of spectrally equivalent parametric processes using higher order statistics

The paper addresses the problem of detecting two spectrally equivalent parametric processes (SEPP): the noisy AR process and the ARMA process. Higher-order statistics (HOS) are shown to be effective for detection. Two HOS based detectors are derived and compared. The fist detector studies the singularity of a HOS-based Yule-Walker matrix. The second detector filters the data by an AR filter estimated from the data; the residual HOS are then shown to be effective for the SEPP detection problem.
Optimal linear prediction of rain attenuation using the Maseng-Bakken model

The Maseng-Bakken model has shown interesting properties to model rain attenuation for Ka and Q/V broadband satellite systems. This paper derives the optimal rain attenuation predictor based on the Maseng-Bakken model. The optimal predictor is obtained by minimizing the mean square error between the rain attenuation and its estimate. We show that this predictor reduces to a bank of filters whose parameters depend on the rain attenuation power spectral density. Simulation results allow us to appreciate the performance of the resulting rain attenuation prediction that is compared with more traditional strategies.
Identification of bilinear systems using Bayesian inference

A large class of nonlinear phenomena can be described using bilinear systems. Such systems are very attractive since they usually require few parameters, to approximate most nonlinearities (compared to other systems). This paper addresses the problems of bilinear system identicalness using Bayesian inference. The Gibbs sampler is used to estimate the bilinear system parameters, from measurements of the system input and output signals.
Hierarchical Bayesian classification of chirp signals

This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning combined with Markov Chain Monte Carlo (MCMC) methods. Bayesian learning consists of estimating the distribution of observed data conditional upon each class from a set of training samples. Unfortunately, this estimation often requires to evaluate intractable multidimensional integrals. This paper studies an original implementation of hierarchical Bayesian learning which estimates the class conditional probability densities using MCMC methods. The performance of this implementation is compared to other existing approaches for the classification of chirp signals.
Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares

This paper studies a linear radial basis function network (RBFN) for unmixing hyperspectral images. The proposed RBFN assumes that the observed pixel reflectances are nonlinear mixtures of known end-members (extracted from a spectral library or estimated with an end-member extraction algorithm), with unknown proportions (usually referred to as abundances). We propose to estimate the model abundances using a linear combination of radial basis functions whose weights are estimated using training samples. The main contribution of this paper is to study an orthogonal least squares algorithm which allows the number of RBFN centers involved in the abundance estimation to be significantly reduced. The resulting abundance estimator is combined with a fully constrained estimation procedure ensuring positivity and sum-to-one constraints for the abundances. The performance of the nonlinear unmixing strategy is evaluated with simulations conducted on synthetic and real data.
A Fixed-Lag Particle Filter for the Joint Detection/Compensation of Interference Effects in GPS Navigation

Interference are among the most penalizing error sources in global positioning system (GPS) navigation. So far, many effort has been devoted to developing GPS receivers more robust to the radio-frequency environment. Contrary to previous approaches, this paper does not aim at improving the estimation of the GPS pseudoranges between the mobile and the GPS satellites in the presence of interference. As an alternative, we propose to model interference effects as variance jumps affecting the GPS measurements which can be directly detected and compensated at the level of the navigation algorithm. Since the joint detection/estimation of the interference errors and motion parameters is a highly non linear problem, a particle filtering technique is used. An original particle filter is developed to improve the detection performance while ensuring a good accuracy of the positioning solution.
Performance of TOA and FOA-based localization for Cospas-Sarsat search and rescue signals

This work studies the performance of position estimation for distress beacons using time of arrival and frequency of arrival measurements. The analysis is conducted for emergency signals modeled as pulses with sigmoidal transitions. This model has shown interesting properties for Cospas-Sarsat search and rescue signals. The modified Cramér-Rao bounds of the symbol width, time of arrival, frequency of arrival, and position of this model are presented. Simulations conducted with realistic signals indicate good agreement between these bounds and the mean square errors of the estimated parameters.
Blind Deconvolution of Sparse Pulse Sequences Under a Minimum Distance Constraint: A Partially Collapsed Gibbs Sampler Method

For blind deconvolution of an unknown sparse sequence convolved with an unknown pulse, a powerful Bayesian method employs the Gibbs sampler in combination with a Bernoulli-Gaussian prior modeling sparsity. In this paper, we extend this method by introducing a minimum distance constraint for the pulses in the sequence. This is physically relevant in applications including layer detection, medical imaging, seismology, and multipath parameter estimation. We propose a Bayesian method for blind deconvolution that is based on a modified Bernoulli-Gaussian prior including a minimum distance constraint factor. The core of our method is a partially collapsed Gibbs sampler (PCGS) that tolerates and even exploits the strong local dependencies introduced by the minimum distance constraint. Simulation results demonstrate significant performance gains compared to a recently proposed PCGS. The main advantages of the minimum distance constraint are a substantial reduction of computational complexity and of the number of spurious components in the deconvolution result.
A Survey of Stochastic Simulation and Optimization Methods in Signal Processing

Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, areas of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.
Missing data reconstruction and anomaly detection in crop development using agronomic indicators derived from multispectral satellite images

This paper studies a new three-step procedure for detecting anomalies in crop development using temporal indicators derived from multispectral satellite images. These anomalies may result from seeding problems, heterogeneity, deficiency and stress. The first step estimates different biophysical and statistical parameters associated with these parameters from the observed images. In a second step, missing data that arise from the existence of clouds or limited coverage in the satellite image are reconstructed. Finally, the mean shift algorithm is used as an unsupervised classifier to detect anomalies in these reconstructed data. The proposed procedure is evaluated using agronomic indicators estimated from SPOT 5 Take 5 satellite images from the Beauce area in France.
Subspace-based Bayesian blind source separation for hyperspectral imagery

In this paper, a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery is introduced. Following the linear mixing model, each pixel spectrum of the hyperspectral image is decomposed as a linear combination of pure endmember spectra. The estimation of the unknown endmember spectra and the corresponding abundances is conducted in a unified manner by generating the posterior distribution of the unknown parameters under a hierarchical Bayesian model. The proposed model accounts for non-negativity and full-additivity constraints, and exploits the fact that the endmember spectra lie on a lower dimensional space. A Gibbs algorithm is proposed to generate samples distributed according to the posterior of interest. Simulation results illustrate the accuracy of the proposed joint Bayesian estimator.
Joint phase-recovery and demodulation-decoding of AIS signals received by satellite

This paper presents a demodulation algorithm for automatic identification system (AIS) signals received by a satellite. The main contribution of this work is to consider the phase recovery problem for an unknown modulation index, coupled with a time-varying phase shift. The proposed method is based on a demodulator introduced in a previous paper based on a Viterbi-type algorithm applied to an extended trellis. The states of this extended trellis are composed of a trellis-code state and of a cyclic redundancy check state. The bit stuffing mechanism is taken into account by defining special conditional transitions in the extended trellis. This algorithm estimates and tracks the phase shift by modifying the Euclidean distance used in the trellis. Simulation results obtained with and without phase tracking are presented and compared in the context of the AIS system.
Detection and Correction of Glitches in a Multiplexed Multichannel Data Stream—Application to the MADRAS Instrument

This paper presents a new strategy to correct the Earth data corrupted by spurious samples that are randomly included in the multiplexed data stream provided by the MADRAS instrument. The proposed strategy relies on the construction of a trellis associated with each scan of the multichannel image, modeling the possible occurrences of these erroneous data. A specific weight that promotes the smooth behavior of the signals recorded in each channel is assigned to each transition between trellis states. The joint detection and correction of the erroneous data are conducted using a dynamic programming algorithm for minimizing the overall cost function throughout the trellis. Simulation results obtained on synthetic and real MADRAS data demonstrate the effectiveness of the proposed solution.
Statistical modeling and classification of reflectance confocal microscopy images

This paper deals with the characterization and classification of reflectance confocal microscopy images of human skin. A special attention will be given to the identification and characterization of the lentigo, a phenomenon that originates at the dermo-epidermic junction of the skin. Confocal images are acquired at different skin depths with a high resolution. For each depth, the histograms of pixel intensities are determined, and well statistically modelled with a generalized gamma distribution (GGD). The scale, shape and translation parameters associated with the GGD are estimated using a new natural gradient descent algorithm showing fast convergence properties when compared to state-of-the-art estimation methods. Results show that the estimated parameters can be used to classify clinical images of lentigo and healthy patients. They also show that the scale and shape parameters are good features to identify and characterize the presence of lentigo in skin tissues.
A fast algorithm based on a sylvester-like equation for LS regression with GMRF prior

This paper presents a fast approach for penalized least squares (LS) regression problems using a 2D Gaussian Markov random field (GMRF) prior. More precisely, the computation of the proximity operator of the LS criterion regularized by different GMRF potentials is formulated as solving a Sylvester-like matrix equation. By exploiting the structural properties of GMRFs, this matrix equation is solved column-wise in an analytical way. The proposed algorithm can be embedded into a wide range of proximal algorithms to solve LS regression problems including a convex penalty. Experiments carried out in the case of a constrained LS regression problem arising in a multichannel image processing application, provide evidence that an alternating direction method of multipliers performs quite efficiently in this context.
Detecting nonlinear mixtures in hyperspectral images

This paper presents a new detector for identifying nonlinear mixtures in hyperspectral images. The proposed detector relies on a nonlinear mixing model that approximates the pixel reflectance as a nonlinear combination of pure spectral components contaminated by an additive white Gaussian noise. The parameters involved in the resulting model are estimated using subgradient-based least squares method. A generalized likelihood ratio test is then proposed to decide whether a given pixel results from the commonly used linear mixing model or from a more general nonlinear mixture. The performance of the detection strategy is evaluated thanks to simulations conducted on synthetic data.
Ship and Oil-Spill Detection Using the Degree of Polarization in Linear and Hybrid/Compact Dual-Pol SAR

Monitoring and detection of ships and oil spills using synthetic aperture radar (SAR) have received a considerable attention over the past few years, notably due to the wide area coverage and day and night all-weather capabilities of SAR systems. Among different polarimetric SAR modes, dual-pol SAR data are widely used for monitoring large ocean and coastal areas. The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field, with significantly less computational complexity, readily adaptable for on-board implementation, compared with other well-known polarimetric discriminators. The performance of the DoP is studied for joint ship and oil-spill detection under different polarizations in hybrid/compact and linear dual-pol SAR imagery. Experiments are performed on RADARSAT-2 C-band polarimetric data sets, over San Francisco Bay, and L -band NASA/JPL UAVSAR data, covering the Deepwater Horizon oil spill in the Gulf of Mexico.
Logistic regression for detecting changes between databases and remote sensing images

This paper studies database updating using optical and synthetic aperture radar images. Logistic regression is used to model the conditional probability of presence/absence of buildings given features extracted from the images. The logistic regression parameters are estimated using the maximum likelihood method. Binary hypothesis tests are then constructed from these estimates to detect changes between the optical/radar images and the existing database. The estimation and detection algorithms are evaluated using simulated and real data sets.