nils-herrmann/clean_scirepeval huggingface dataset:
Introduction to the Issue on Advances in Hyperspectral Data Processing and Analysis

The articles in this special issue represent advances in hyperspectral image-processing research. Specifically, it focuses on novel algorithmic approaches related to the representation and analysis of hyperspectral images, as well as their application to real-world image-analysis needs. It also delves into theoretical insights, performance limits, and trade-offs on the class of emerging image-processing approaches tailored to hyperspectral images. This issue covers a gamut of broad topics, from representation and sensing (image coding, compressive sensing), feature extraction and channel selection, to image analysis (spectral unmixing, classification, and detection.
A new framework for hyperspectral image classification using multiple spectral and spatial features

This paper presents a new multiple feature learning approach for accurate spectral-spatial classification of hyperspec-tral images. The proposed method integrates multiple features based on the logarithmic opinion pool. We consider subspace multinomial logistic regression for classification as it exhibits a flexible structure for the combination of multiple features through the posterior probability. At the same time, it is able to cope with highly mixed hyperspectral data and with the presence of limited training samples. In this work, we considered lowpass filtering and morphological attribute profiles for spatial feature extraction. Our experimental results with a real hyperspectral images collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) indicate that the proposed method exhibits state-of-the-art classification performance.
A new semi-supervised approach for hyperspectral image classification with different active learning strategies

Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semi-supervised learning (SSL) techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new framework for SSL which exploits active learning (AL) for unlabeled sample selection. Specifically, we use AL to select the most informative unlabeled training samples and further evaluate two different strategies for active sample selection. In this work, the proposed approach is illustrated with the sparse multinomial logistic regression (SMLR) classifier learned with the MLR via variable splitting and augmented Lagrangian (LORSAL) algorithm. Our experimental results with a real hyperspectral image collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) indicate that the use of AL for unlabeled sample selection represents an effective and promising strategy in the context of semi-supervised hyperspectral data classification.
Nonparametric estimation of mean Doppler and spectral width

This paper proposes a new nonparametric method for estimation of spectral moments of a zero-mean Gaussian process immersed in additive white Gaussian noise. Although the technique is valid for any order moment, particular attention is given to the mean Doppler (first moment) and to the spectral width (square root of the centered second spectral moment). By assuming that the power spectral density (PSD) of the underlying process is bandlimited, the maximum-likelihood estimates of its spectral moments are derived. A suboptimal estimate based on the sample covariance is also studied. Both methods are robust in the sense that they do not rely on any assumption concerning the PSD (besides being bandlimited). Under weak conditions, the set of estimates based on sample covariance is unbiased and strongly consistent. Compared with the classical pulse pair and the periodogram-based estimators, the proposed methods exhibit better statistical properties for asymmetric spectra and/or spectra with large spectral widths, while involving a computational burden of the same order.
Performance analysis of model-based localization of high-frequency acoustic sources in 3D

A robust model-based source localization (MBSL) scheme for 3D positioning of high-frequency underwater acoustic (UWA) transmitters, as well as performance assessment of 3D speed vector estimation of the acoustic source, deploying two linear receiver arrays, is developed and evaluated. In addition, this paper presents the sensitivity evaluation of MBSL to uncertainties involved in environmental parameters, including the effects of sound speed profile and bathymetry mismatches. The results presented here pertain to the general framework of inferring side information concerning an UWA link from the environmental signature imprinted on waveforms by the propagation medium. The presented algorithms directly operate on estimated linear time-varying UWA channel responses, represented as 3-dimensional delay-Doppler-depth functions (DDDF). Estimation of DDDF coefficients is achieved jointly for all receivers deployed at different depths using Basis-Pursuit (BP) tools, which can efficiently handle sparse unconstrained ℓ2 - ℓ1 minimization, and directly operate on the complex signals of baseband models. To extract delay/Doppler information related to each path from the DDDF representation, an efficient energy-based scheme is developed to detect and label wavefronts using clustering techniques inspired by image processing.
Spectral–Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization

Remotely sensed hyperspectral image classification is a very challenging task. This is due to many different aspects, such as the presence of mixed pixels in the data or the limited information available a priori. This has fostered the need to develop techniques able to exploit the rich spatial and spectral information present in the scenes while, at the same time, dealing with mixed pixels and limited training samples. In this paper, we present a new spectral-spatial classifier for hyperspectral data that specifically addresses the issue of mixed pixel characterization. In our presented approach, the spectral information is characterized both locally and globally, which represents an innovation with regard to previous approaches for probabilistic classification of hyperspectral data. Specifically, we use a subspace-based multinomial logistic regression method for learning the posterior probabilities and a pixel-based probabilistic support vector machine classifier as an indicator to locally determine the number of mixed components that participate in each pixel. The information provided by local and global probabilities is then fused and interpreted in order to characterize mixed pixels. Finally, spatial information is characterized by including a Markov random field (MRF) regularizer. Our experimental results, conducted using both synthetic and real hyperspectral images, indicate that the proposed classifier leads to state-of-the-art performance when compared with other approaches, particularly in scenarios in which very limited training samples are available.
Classification with reject option using contextual information

We propose a new algorithm for classification that merges classification with reject option with classification using contextual information. A reject option is desired in many image-classification applications requiring a robust classifier and when the need for high classification accuracy surpasses the need to classify the entire image. Moreover, our algorithm improves the classifier performance by including local and nonlocal contextual information, at the expense of rejecting a fraction of the samples. As a probabilistic model, we adopt a multinomial logistic regression. We use a discriminative random model for the description of the problem; we introduce reject option into the classification problem through association potential, and contextual information through interaction potential. We validate the method on the images of H&E-stained teratoma tissues and show the increase in the classifier performance when rejecting part of the assigned class labels.
Vertex component analysis: a fast algorithm to unmix hyperspectral data

Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
Hyperspectral image inpainting based on low-rank representation: A case study on Tiangong-1 data

Hyperspectral images (HSIs) cover hundreds of narrow spectral bands, thus yielding high spectral resolution, enabling precise identification of different materials. However, the existence of dead pixels in the light sensors produces a number of irrelevant measurements, which may compromise the usefulness of HSIs. In this paper, a new hyperspectral inpainting method, named HyInpaint, is proposed. The original HSI is represented on a low dimensional subspace and its estimation is formalized with respect to the subspace representation coefficients on a given basis. The coefficients are estimated by minimizing an objective function which, in addition to the data term, contains a regularizer based on the Criminisi's inpainting method. The optimization is carried out by an instance of the alternating direction method of multipliers (ADMM), adopting the plug-and-play methodology. The effectiveness of the proposed HyInpaint approach is illustrated on Tiangong-1 hyperspectral visible near infrared (VNIR) wavebands data.
Image restoration with locally selected class-adapted models

State-of-the-art algorithms for imaging inverse problems (namely deblurring and reconstruction) are typically iterative, involving a de-noising operation as one of its steps. Using a state-of-the-art denoising method in this context is not trivial, and is the focus of current work. Recently, we have proposed to use a class-adapted denoiser (patch-based using Gaussian mixture models) in a so-called plug- and-play scheme, wherein a state-of-the-art denoiser is plugged into an iterative algorithm, leading to results that outperform the best general-purpose algorithms, when applied to an image of a known class (e.g. faces, text, brain MRI). In this paper, we extend that approach to handle situations where the image being processed is from one of a collection of possible classes or, more importantly, contains regions of different classes. More specifically, we propose a method to locally select one of a set of class-adapted Gaussian mixture patch priors, previously estimated from clean images of those classes. Our approach may be seen as simultaneously performing segmentation and restoration, thus contributing to bridging the gap between image restoration/reconstruction and analysis.
Bayesian image segmentation using hidden fields: Supervised, unsupervised, and semi-supervised formulations

Segmentation is one of the central problems in image analysis, where the goal is to partition the image domain into regions exhibiting some sort of homogeneity. Most often, the partition is obtained by solving a combinatorial optimization problem, which is, in general, NP-hard. In this paper, we follow an alternative approach, using a Bayesian formulation based on a set of hidden real-valued random fields, which condition the partition. This formulation yields a continuous optimization problem, rather than a combinatorial one. In the supervised case, this problem is convex, and we tackle it with an instance of the alternating direction method of multipliers (ADMM). In the unsupervised and semi-supervised cases, the optimization problem is nonconvex, and we address it using an expectation-maximization (EM) algorithm, where the M-step is implemented via ADMM. The effectiveness and flexibility of the proposed approach is illustrated with experiments on simulated and real data.
Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization

Multiplicative noise (also known as speckle noise) models are central to the study of coherent imaging systems, such as synthetic aperture radar and sonar, and ultrasound and laser imaging. These models introduce two additional layers of difficulties with respect to the standard Gaussian additive noise scenario: (1) the noise is multiplied by (rather than added to) the original image; (2) the noise is not Gaussian, with Rayleigh and Gamma being commonly used densities. These two features of multiplicative noise models preclude the direct application of most state-of-the-art algorithms, which are designed for solving unconstrained optimization problems where the objective has two terms: a quadratic data term (log-likelihood), reflecting the additive and Gaussian nature of the noise, plus a convex (possibly nonsmooth) regularizer (e.g., a total variation or wavelet-based regularizer/prior). In this paper, we address these difficulties by: (1) converting the multiplicative model into an additive one by taking logarithms, as proposed by some other authors; (2) using variable splitting to obtain an equivalent constrained problem; and (3) dealing with this optimization problem using the augmented Lagrangian framework. A set of experiments shows that the proposed method, which we name MIDAL (multiplicative image denoising by augmented Lagrangian), yields state-of-the-art results both in terms of speed and denoising performance.
Nonnegative matrix factorization with collaborativity for hyperspectral unmixing

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. It expresses each (possibly mixed) pixel of the hyperspectral image as a combination of spectrally pure substances (called endmembers) weighted by their corresponding abundances. The spectral unmixing chain usually consists of three main steps: 1) estimation of the number of endmembers in a scene; 2) automatic identification of the spectral signatures of these endmembers; and 3) estimation of the endmember abundances in each pixel of the scene. Over the last years, several algorithms have been developed for each part of the chain. In this paper, we develop a new algorithm which can perform the three steps of the unmixing chain (at once) for hyperspectral images with significant amount of noise. The proposed algorithm, which does not require a previous subspace identification step to estimate the number of endmembers, starts with an overestimated number of endmember and then iteratively removes the less relevant endmember detected by a collaborative regularization prior. Our experimental results demonstrate that the proposed method exhibits very good performance when the number of endmember is not available a priori, a situation that is very common in practice.
Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles

In recent years, sparse representations have been widely studied in the context of remote sensing image analysis. In this paper, we propose to exploit sparse representations of morphological attribute profiles for remotely sensed image classification. Specifically, we use extended multiattribute profiles (EMAPs) to integrate the spatial and spectral information contained in the data. EMAPs provide a multilevel characterization of an image created by the sequential application of morphological attribute filters that can be used to model different kinds of structural information. Although the EMAPs' feature vectors may have high dimensionality, they lie in class-dependent low-dimensional subpaces or submanifolds. In this paper, we use the sparse representation classification framework to exploit this characteristic of the EMAPs. In short, by gathering representative samples of the low-dimensional class-dependent structures, any given sample may by sparsely represented, and thus classified, with respect to the gathered samples. Our experiments reveal that the proposed approach exploits the inherent low-dimensional structure of the EMAPs to provide state-of-the-art classification results for different multi/hyperspectral data sets.
Potential and limitations of band selection and library pruning in sparse hyperspectral unmixing

Sparse regression using spectral libraries is nowadays a widely used technique for hyperspectral data unmixing. Assuming that the potential endmembers are collected in a large database of spectra, sparse unmixing finds the fractional abundances of a reduced set of constituent materials by solving convex optimization problems which target, at the same time, low data reconstruction errors. The large amount of data, jointly with other limitations related to the internal characteristics of the large spectral libraries (such as spectra similarity), affect the performance of sparse unmixing algorithms in terms of accuracy and running time. Recently, an efficient method based on a multi-measurement vector (MMV) approach was proposed to select, from a large library, suitable spectra for unmixing. Many research efforts have also been devoted to data dimensionality reduction techniques, of which band selection is very popular. In this work, we investigate the effects on the unmixing performance when the two techniques are applied simultaneously. Our experiments show that important improvements can be achieved, depending on several factors, such as: data noise, number of endmembers present in the dataset, number of spectra retained from the library, the order of operations, accuracy of data subspace estimation.
A new extended linear mixing model to address spectral variability

Spectral variability is a phenomenon due, to a grand extend, to variations in the illumination and atmospheric conditions within a hyperspectral image, causing the spectral signature of a material to vary within a image. Data spectral fluctuation due to spectral variability compromises the linear mixing model (LMM) sum-to-one constraint, and is an important source of error in hyperspectral image analysis. Recently, spectral variability has raised more attention and some techniques have been proposed to address this issue, i.e. spectral bundles. Here, we propose the definition of an extended LMM (ELMM) to model spectral variability and we show that the use of spectral bundles models the ELMM implicitly. We also show that the constrained least squares (CLS) is an explicit modelling of the ELMM when the spectral variability is due to scaling effects. We give experimental validation that spectral bundles (and sparsity) and CLS are complementary techniques addressing spectral variability. We finally discuss on future research avenues to fully exploit the proposed ELMM.
InSAR phase unwrapping: a Bayesian approach

The paper proposes a Bayesian approach to absolute phase (not simply modulo-2/spl pi/) estimation in interferometric aperture radar (InSAR). The observation density is 2/spl pi/-periodic and accounts for the interferometric pair decorrelation and the system noise; the a priori. probability of the absolute phase is modeled by a compound Gauss Markov random field (CGMRF). To compute the absolute phase estimate we propose an iterative scheme aiming at the computation of the maximum a posteriori probability (MAP) estimate. Each iteration embodies a discrete optimization step (Z-step), implemented by network programming techniques, and an iterative conditional modes (ICM) step (/spl pi/-step). According to the terms Z-step and /spl pi/-step, we term our algorithm Z/spl pi/M, where the letter M stands for maximization. Experimental results, comparing the proposed algorithm with classical approaches, illustrate the effectiveness of the Z/spl pi/M algorithm.
Vertex component analysis GPU-based implementation for hyperspectral unmixing

Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other methods vertex component analysis (VCA) has become a very popular and useful tool to unmix hyperspectral data. VCA is a geometrical based method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Many Hyperspectral imagery applications require a response in real time or near-real time. Thus, to met this requirement this paper proposes a parallel implementation of VCA developed for graphics processing units. The impact on the complexity and on the accuracy of the proposed parallel implementation of VCA is examined using both simulated and real hyperspectral datasets.
Hyperspectral unmixing algorithm via dependent component analysis

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (end member signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the end-members signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on independent component analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.
Denoising of medical images corrupted by Poisson noise

Medical images are often noisy owing to the physical mechanisms of the acquisition process. The great majority of the denoising algorithms assume additive white Gaussian noise. However, some of the most popular medical image modalities are degraded by some type of non-Gaussian noise. Among these types, we refer the Poisson noise, which is particularly suitable for modeling the counting processes associated to many imaging modalities such as PET, SPECT, and fluorescent confocal microscopy imaging. The aim of this work is to compare the effectiveness of several denoising algorithms in the presence of Poisson noise. We consider algorithms specifically designed for Poisson noise (wavelets, Platelets, and minimum descritpion length) and algorithms designed for Gaussian noise (edge preserving bilateral filtering, total variation, and non-local means). These algorithms are applied to piecewise smooth simulated and real data. Somehow unexpectedly, we conclude that total variation, designed for Gaussian noise, outperforms more elaborated state-of-the-art methods specifically designed for Poisson noise.