nils-herrmann/clean_scirepeval huggingface dataset:
Generative Supervised Classification Using Dirichlet Process Priors

Choosing the appropriate parameter prior distributions associated to a given Bayesian model is a challenging problem. Conjugate priors can be selected for simplicity motivations. However, conjugate priors can be too restrictive to accurately model the available prior information. This paper studies a new generative supervised classifier which assumes that the parameter prior distributions conditioned on each class are mixtures of Dirichlet processes. The motivations for using mixtures of Dirichlet processes is their known ability to model accurately a large class of probability distributions. A Monte Carlo method allowing one to sample according to the resulting class-conditional posterior distributions is then studied. The parameters appearing in the class-conditional densities can then be estimated using these generated samples (following Bayesian learning). The proposed supervised classifier is applied to the classification of altimetric waveforms backscattered from different surfaces (oceans, ices, forests, and deserts). This classification is a first step before developing tools allowing for the extraction of useful geophysical information from altimetric waveforms backscattered from nonoceanic surfaces.
Ship detection using SAR and AIS raw data for maritime surveillance

This paper studies a maritime vessel detection method based on the fusion of data obtained from two different sensors, namely a synthetic aperture radar (SAR) and an automatic identification system (AIS) embedded in a satellite. Contrary to most methods widely used in the literature, the present work proposes to jointly exploit information from SAR and AIS raw data in order to detect the absence or presence of a ship using a binary hypothesis testing problem. This detection problem is handled by a generalized likelihood ratio detector whose test statistics has a simple closed form expression. The distribution of the test statistics is derived under both hypotheses, allowing the corresponding receiver operational characteristics (ROCs) to be computed. The ROCs are then used to compare the detection performance obtained with different sensors showing the interest of combining information from AIS and radar.
Similarity measure between vector data bases and optical images for change detection

This paper addresses the problem of defining a similarity measure betweenan observed Gaussian image and a binary image constructed from a cartographic database. The main idea is to assume that the binary image has been obtained by thresholding an unobserved Gaussian image correlated with the observed image. The proposed statistical model is then used to estimate its unknown parameters using the maximum likelihood method. The paper discusses a possible application to change detection between a cartographic vector data base and an optical image.
Bayesian reconstruction of hyperspectral images by using compressed sensing measurements and a local structured prior

This paper introduces a hierarchical Bayesian model for the reconstruction of hyperspectral images using compressed sensing measurements. This model exploits known properties of natural images, promoting the recovered image to be sparse on a selected basis and smooth in the image domain. The posterior distribution of this model is too complex to derive closed form expressions for the estimators of its parameters. Therefore, an MCMC method is investigated to sample this posterior distribution. The resulting samples are used to estimate the unknown model parameters and hyperparameters in an unsupervised framework. The results obtained on real data illustrate the improvement in reconstruction quality when compared to some existing techniques.
Changepoint detection in multivariate Poisson distributions

The paper addresses the problem of detecting changes in the parameters of multivariate Poisson sequences. The Neyman Pearson Detector (NPD) and the, Generalized Likelihood Ratio Detector (GLRD) are easily derived for iid Poisson sequences. Unfortunately, the problem is more' complicated for correlated Poisson sequences. This paper studies the performance of the detectors obtained under the iid assumption, when the observed data are distributed according to multivariate correlated Poisson sequences. Theoretical expressions of the receiver operating characteristics are derived when the change location is known. These curves provide a reference to which other detectors can be compared. The more realistic situation of an unknown change location is finally considered.
New asymptotic properties for the robust ANMF

The purpose of this paper is to derive new asymptotic properties of the robust adaptive normalized matched filter (ANMF). More precisely, the ANMF built with Tyler estimator (TyE-ANMF) is analyzed under the framework of complex elliptically symmetric (CES) distributions. We show that the distribution of TyE-ANMF can be accurately approximated by the well-known distribution of the ANMF built with the sample covariance matrix (SCM-ANMF) under the Gaussian assumption. To that end, the asymptotic properties of the difference between both ANMF detectors are derived. By comparison with the state of the art, the asymptotic properties of the TyE-ANMF are shown to be better approximated by the SCM-ANMF rather than using the NMF (test built with the true CM). Some Monte-Carlo simulations support that claim and demonstrate the interest of this theoretical result.
Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery

This paper studies a hierarchical Bayesian model for nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are polynomial functions of linear mixtures of pure spectral components contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy constraints that are naturally expressed within a Bayesian framework. A Gibbs sampler allows one to sample the unknown abundances and nonlinearity parameters according to the joint posterior of interest. The performance of the resulting unmixing strategy is evaluated thanks to simulations conducted on synthetic and real data.
Singular random signals

Singular random signals are characterized by the fact that their values at each time are singular random variables, which means that their distribution functions are continuous but with a derivative almost everywhere equal to zero. Such random variables are usually considered as without interest in engineering or signal processing problems. The purpose of this paper is to show that very simple signals can be singular. This is especially the case for autoregressive moving average (ARMA) signals defined by white noise taking only discrete values and filters with poles located in a circle of singularity introduced in this paper. After giving the origin of singularity and analyzing its relationships with fractal properties, various simulations highlighting this structure will be presented.
Bayesian parameter estimation for asymmetric power distributions

This paper proposes a hierarchical Bayesian model for estimating the parameters of asymmetric power distributions (APDs). These distributions are defined by shape, scale and asymmetry parameters which make them very flexible for approximating empirical distributions. A hybrid Markov chain Monte Carlo method is then studied to sample the unknown parameters of APDs. The generated samples can be used to compute the Bayesian estimators of the unknown APD parameters. Numerical experiments show the good performance of the proposed estimation method. An application to an image segmentation problem is finally investigated.
Joint spectral classification and unmixing using adaptative pixel neighborhoods

A new spatial unmixing algorithm for hyperspectral images is studied. This algorithm is based on the well-known linear mixing model. The spectral signatures (or endmembers) are assumed to be known while the mixture coefficients (or abundances) are estimated by a Bayesian algorithm. As a pre-processing step, an area filter is employed to partition the image into multiple spectrally consistent connected components or adaptative neighborhoods. Then, spatial correlations are introduced by assigning to the pixels of a given neighbourhood the same hidden labels. More precisely, these pixels are modeled using a new prior distribution taking into account spectral similarity between the neighbors. Abundances are reparametrized by using logistic coefficients to handle the associated physical constraints. Other parameters and hyperparameters are assigned appropriate prior distributions. After computing the joint posterior distribution, a hybrid Gibbs algorithm is employed to generate samples that are asymptotically distributed according to this posterior distribution. The generated samples are finally used to estimate the unknown model parameters. Simulations on synthetic data illustrate the performance of the proposed method.
A Hamiltonian Monte Carlo Method for Non-Smooth Energy Sampling

Efficient sampling from high-dimensional distributions is a challenging issue that is encountered in many large data recovery problems. In this context, sampling using Hamiltonian dynamics is one of the recent techniques that have been proposed to exploit the target distribution geometry. Such schemes have clearly been shown to be efficient for multidimensional sampling but, rather, are adapted to distributions from the exponential family with smooth energy functions. In this paper, we address the problem of using Hamiltonian dynamics to sample from probability distributions having non-differentiable energy functions such as those based on the l1 norm. Such distributions are being used intensively in sparse signal and image recovery applications. The technique studied in this paper uses a modified leapfrog transform involving a proximal step. The resulting nonsmooth Hamiltonian Monte Carlo method is tested and validated on a number of experiments. Results show its ability to accurately sample according to various multivariate target distributions. The proposed technique is illustrated on synthetic examples and is applied to an image denoising problem.
Spectral image fusion from compressive measurements using spectral unmixing

This work aims at reconstructing a high-spatial high-spectral image from the complementary information provided by sensors that allow us to acquire compressive measurements of different spectral ranges and different spatial resolutions, such as hyperspectral (HS) and multi-spectral (MS) compressed images. To solve this inverse problem, we investigate a new optimization algorithm based on the linear spectral unmixing model and using a block coordinate descent strategy. The non-negative and sum to one constraints resulting from the intrinsic physical properties of abundance and a total variation penalization are used to regularize this ill-posed inverse problem. Simulations results conducted on realistic compressive hyperspectral and multispectral images show that the proposed algorithm can provide fusion and unmixing results that are very close to those obtained when using uncompressed images, with the advantage of using a significant reduced number of measurements.
Joint detection/estimation of multipath effects for the Global Positioning System

Multipaths cause major impairments to navigation with the Global Positioning System (GPS). Indeed, non-line-of-sight (NLOS) propagation is well known to bias GPS position estimates. A recent methodology has been proposed to overcome this limitation by estimating simultaneously the kinematic states and the multipath biases all along the observation interval. However, multipaths clearly occur relatively infrequently during time intervals of fixed duration. The paper studies a particle filtering algorithm for joint detection and estimation of multipath biases. A Rao-Blackwellized approach allows estimation of the kinematic states by extended Kalman filters, whereas multipath detection is achieved by an appropriate fixed lag particle filter.
Non-linear time-scale contrast for abrupt change detection in multiplicative noise

Multiplicative abrupt changes (AC) have been considered in many applications. These applications include speckle noise in radar images and random communication models. This work applies the continuous wavelet transform (CWT) and possibly nonlinear preprocessing to AC detection in multiplicative noise. CWT translation invariance allows an AC signature to be defined. Then, the problem is signature detection in the time-scale domain. A second-order contrast criterion is defined as a measure of detector performance. The influence of nonlinear pre-processing on time-scale contrast is studied. The performance of two suboptimal signature-based detectors are clearly improved by a nonlinear pre-processing under appropriate conditions regarding noise and signal parameters.
Nonlinear Spectral Unmixing of Hyperspectral Images Using Gaussian Processes

This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and the nonlinear function are unknown. The first step of the proposed method estimates the abundance vectors for all the image pixels using a Bayesian approach an a Gaussian process latent variable model for the nonlinear function (relating the abundance vectors to the observations). The endmembers are subsequently estimated using Gaussian process regression. The performance of the unmixing strategy is first evaluated on synthetic data. The proposed method provides accurate abundance and endmember estimations when compared to other linear and nonlinear unmixing strategies. An interesting property is its robustness to the absence of pure pixels in the image. The analysis of a real hyperspectral image shows results that are in good agreement with state of the art unmixing strategies and with a recent classification method.
Unmixing multitemporal hyperspectral images accounting for smooth and abrupt variations

A classical problem in hyperspectral imaging, referred to as hyperspectral unmixing, consists in estimating spectra associated with each material present in an image and their proportions in each pixel. In practice, illumination variations (e.g., due to declivity or complex interactions with the observed materials) and the possible presence of outliers can result in significant changes in both the shape and the amplitude of the measurements, thus modifying the extracted signatures. In this context, sequences of hyperspectral images are expected to be simultaneously affected by such phenomena when acquired on the same area at different time instants. Thus, we propose a hierarchical Bayesian model to simultaneously account for smooth and abrupt spectral variations affecting a set of multitemporal hyperspectral images to be jointly unmixed. This model assumes that smooth variations can be interpreted as the result of endmember variability, whereas abrupt variations are due to significant changes in the imaged scene (e.g., presence of outliers, additional endmembers, etc.). The parameters of this Bayesian model are estimated using samples generated by a Gibbs sampler according to its posterior. Performance assessment is conducted on synthetic data in comparison with state-of-the-art unmixing methods.
Spectral estimation of migrating targets in wideband radar

Low pulse repetition frequency radars aimed at detecting moving targets are faced with the problem of blind velocities, since Doppler ambiguous targets have to compete with clutter and possibly the sidelobes of strong targets. On the other hand, wideband radars offer high range resolution, and hence fast moving targets, in contrast to clutter or non moving targets, are likely to migrate during the coherent processing interval. Moreover, range migration is different between two Doppler ambiguous targets. These properties can be used to remove Doppler ambiguities and detect targets which fall in the clutter or targets sidelobes. In this paper, we derive new spectral estimators (and detectors accordingly) that can take into account range migration to improve detectability of Doppler ambiguous targets. The new techniques are based on an extension of Capon method to the wideband case, and take into account the non-stationarity of the covariance matrix of the targets. Numerical simulations show that the new estimators significantly reduce clutter and target sidelobes compared to previous proposed schemes.
Adaptive Markov Random Fields for Joint Unmixing and Segmentation of Hyperspectral Images

Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using an inversion step. Recent works have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to model these spatial correlations and partition the image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites using similarity regions. These regions are built using a self-complementary area filter that stems from the morphological theory. This kind of filter divides the original image into flat zones where the underlying pixels have the same spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the corresponding posterior distribution of the unknown parameters and hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of the algorithm.
A Semi-Analytical Model for Delay/Doppler Altimetry and Its Estimation Algorithm

The concept of delay/Doppler (DD) altimetry (DDA) has been under study since the mid-1990s, aiming at reducing the measurement noise and increasing the along-track resolution in comparison with the conventional pulse-limited altimetry. This paper introduces a new model for the mean backscattered power waveform acquired by a radar altimeter operating in synthetic aperture radar mode, as well as an associated least squares (LS) estimation algorithm. As in conventional altimetry (CA), the mean power can be expressed as the convolution of three terms: the flat surface impulse response (FSIR), the probability density function of the heights of the specular scatterers, and the time/frequency point target response of the radar. An important contribution of this paper is to derive an analytical formula for the FSIR associated with DDA. This analytical formula is obtained for a circular antenna pattern, no mispointing, no vertical speed effect, and a uniform scattering. The double convolution defining the mean echo power can then be computed numerically, resulting in a 2-D semi-analytical model called the DD map (DDM). This DDM depends on three altimetric parameters: the epoch, the sea surface wave height, and the amplitude. A multi-look model is obtained by summing all the reflected echoes from the same along-track surface location of interest after applying appropriate delay compensation (range migration) to align the DDM on the same reference. The second contribution of this paper concerns the estimation of the parameters associated with the multi-look semi-analytical model. An LS approach is investigated by means of the Levenberg-Marquardt algorithm. Simulations conducted on simulated altimetric waveforms allow the performance of the proposed estimation algorithm to be appreciated. The analysis of Cryosat-2 waveforms shows an improvement in parameter estimation when compared to the CA.
Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes

This paper describes a Gaussian process based method for nonlinear hyperspectral image unmixing. The proposed model assumes a nonlinear mapping from the abundance vectors to the pixel reflectances contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy physical constraints that are naturally expressed within a Bayesian framework. The proposed abundance estimation procedure is applied simultaneously to all pixels of the image by maximizing an appropriate posterior distribution which does not depend on the endmembers. After determining the abundances of all image pixels, the endmembers contained in the image are estimated by using Gaussian process regression. The performance of the resulting unsupervised unmixing strategy is evaluated through simulations conducted on synthetic data.
The noncentral wishart distribution: properties and application to speckle imaging

Whereas central Wishart distributions have been extensively studied in the literature, the related noncentral distributions have received less attention. This paper studies interesting properties of these noncentral Wishart distributions such as closed form expressions for the moments. This work is motivated by the fact that this distribution plays a central part in the modeling of images corrupted by speckle. More precisely, the multivariate distribution of the intensity arising from a nonzero mean Gaussian wavefront amplitude is the diagonal of a noncentral Wishart distribution. Two examples which are interesting for synthetic aperture radar imaging and optical imaging through turbulence are discussed
Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images

Mixing phenomena in hyperspectral images depend on a variety of factors, such as the resolution of observation devices, the properties of materials, and how these materials interact with incident light in the scene. Different parametric and nonparametric models have been considered to address hyperspectral unmixing problems. The simplest one is the linear mixing model. Nevertheless, it has been recognized that the mixing phenomena can also be nonlinear. The corresponding nonlinear analysis techniques are necessarily more challenging and complex than those employed for linear unmixing. Within this context, it makes sense to detect the nonlinearly mixed pixels in an image prior to its analysis, and then employ the simplest possible unmixing technique to analyze each pixel. In this paper, we propose a technique for detecting nonlinearly mixed pixels. The detection approach is based on the comparison of the reconstruction errors using both a Gaussian process regression model and a linear regression model. The two errors are combined into a detection statistics for which a probability density function can be reasonably approximated. We also propose an iterative endmember extraction algorithm to be employed in combination with the detection algorithm. The proposed detect-then-unmix strategy, which consists of extracting endmembers, detecting nonlinearly mixed pixels and unmixing, is tested with synthetic and real images.