nils-herrmann/clean_scirepeval huggingface dataset:
A new semi-supervised algorithm for hyperspectral image classification based on spectral unmixing concepts

Spectral unmixing is a fast growing area in hyperspectral image analysis. Many algorithms have been recently developed to retrieve pure spectral components (endmembers) and determine their abundance fractions in mixed pixels, which dominate hyperspectral images. However, possible connections between spectral unmixing concepts and classification algorithms have been rarely investigated. In this work, we propose a new method to perform semi-supervised hyperspectral image classification exploiting the information retrieved with spectral unmixing. The proposed method integrates a well-established discriminative classifier (multinomial logistic regression) with linear spectral unmixing. Furthermore, the proposed method uses a new active sampling approach which takes into account spatial context when generating new samples. The proposed method is experimentally validated using both simulated and real hyperspectral data sets.
Total Variation-Based Image Deconvolution: a Majorization-Minimization Approach

The total variation regularizer is well suited to piecewise smooth images. If we add the fact that these regularizers are convex, we have, perhaps, the reason for the resurgence of interest on TV-based approaches to inverse problems. This paper proposes a new TV-based algorithm for image deconvolution, under the assumptions of linear observations and additive white Gaussian noise. To compute the TV estimate, we propose a majorization-minimization approach, which consists in replacing a difficult optimization problem by a sequence of simpler ones, by relying on convexity arguments. The resulting algorithm has O(N) computational complexity, for finite support convolutional kernels. In a comparison with state-of-the-art methods, the proposed algorithm either outperforms or equals them, with similar computational complexity
Hyperspectral compressive acquisition in the spatial domain via blind factorization

Hyperspectral data compression has received considerable interest in recent years. Contrarily to the conventional compression schemes, which first acquire the full data set and then implement some compressing technique, compressive sensing (CS) acquires directly the compressed signal; the original signal is later recovered by solving a suitable optimization problem. The CS paradigm fits perfectly the requirements of onborad hyperspectral imaging systems in terms of energy, computing power, and bandwidth. By using CS in these systems, the amount data acquired in the system and transmitted to the ground stations is reduced and the bulk of the computation to infer the original data is carried out in the ground stations. In this paper, we present a new technique to perform CS of hyperspectral images (HSIs), which relies in two characteristics of these data: a) HSIs admits a low dimensional linear representation; and b) the coefficients (in the spatial domain) of the linear representations are are piecewise smooth, thus compressible. The proposed method is blind in the sense that linear representation is learned with low computational cost from the compressed measurements. The effectiveness of the proposed CS method is illustrated in both synthetic and real scenarios.
Learning dependent sources using mixtures of Dirichlet: Applications on hyperspectral unmixing

This paper is an elaboration of the DECA algorithm  to blindly unmix hyperspectral data. The underlying mixing model is linear, meaning that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. The proposed method, as DECA, is tailored to highly mixed mixtures in which the geometric based approaches fail to identify the simplex of minimum volume enclosing the observed spectral vectors. We resort then to a statitistical framework, where the abundance fractions 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. With respect to DECA, we introduce two improvements: 1) the number of Dirichlet modes are inferred based on the minimum description length (MDL) principle; 2) The generalized expectation maximization (GEM) algorithm we adopt to infer the model parameters is improved by using alternating minimization and augmented Lagrangian methods to compute the mixing matrix. The effectiveness of the proposed algorithm is illustrated with simulated and read data.
Fast Image Recovery Using Variable Splitting and Constrained Optimization

We propose a new fast algorithm for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an l2 data-fidelity term and a nonsmooth regularizer. This formulation allows both wavelet-based (with orthogonal or frame-based representations) regularization or total-variation regularization. Our approach is based on a variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The proposed algorithm is an instance of the so-called alternating direction method of multipliers, for which convergence has been proved. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is faster than the current state of the art methods.
Parallel Hyperspectral Coded Aperture for Compressive Sensing on GPUs

The application of compressive sensing (CS) to hyperspectral images is an active area of research over the past few years, both in terms of the hardware and the signal processing algorithms. However, CS algorithms can be computationally very expensive due to the extremely large volumes of data collected by imaging spectrometers, a fact that compromises their use in applications under real-time constraints. This paper proposes four efficient implementations of hyperspectral coded aperture (HYCA) for CS, two of them termed P-HYCA and P-HYCA-FAST and two additional implementations for its constrained version (CHYCA), termed P-CHYCA and P-CHYCA-FAST on commodity graphics processing units (GPUs). HYCA algorithm exploits the high correlation existing among the spectral bands of the hyperspectral data sets and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. The proposed P-HYCA and P-CHYCA implementations have been developed using the compute unified device architecture (CUDA) and the cuFFT library. Moreover, this library has been replaced by a fast iterative method in the P-HYCA-FAST and P-CHYCA-FAST implementations that leads to very significant speedup factors in order to achieve real-time requirements. The proposed algorithms are evaluated not only in terms of reconstruction error for different compressions ratios but also in terms of computational performance using two different GPU architectures by NVIDIA: 1) GeForce GTX 590; and 2) GeForce GTX TITAN. Experiments are conducted using both simulated and real data revealing considerable acceleration factors and obtaining good results in the task of compressing remotely sensed hyperspectral data sets.
Adaptive Local Phase Approximations and Global Unwrapping

The paper introduces a new modulo-2pi phase denoising algorithm based on local polynomial approximations. The zero and first order approximations of the phase are calculated in sliding windows of varying size. The former is used for point- wise adaptive window size selection, while the latter is used for filtering the phase in the obtained windows. For unwrapping, we input the PUMA unwrapping algorithm  with the denoised wrapped phase obtained with the proposed approach. Simulation shows that this technique enables strong noise attenuation while preserving image details.
Supervised hyperspectral image classification with rejection

Hyperspectral image classification is a challenging classification problem: 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 surpasses the need to classify the entire image. To achieve this, we use classification with rejection by providing the classifier an option not to classify a pixel and consequently reject it. We propose a method for supervised hyperspectral image classification combining the use of contextual priors with classification with rejection. Rejection is introduced as an extra class that models the probability of classifier failure. We validate the resulting algorithm in the AVIRIS Indian Pines scene and illustrate the performance increase resulting from classification with rejection.
Semisupervised Self-Learning for Hyperspectral Image Classification

Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional images with hundreds of spectral bands. 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 semisupervised learning 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 approach for semisupervised learning which adapts available active learning methods (in which a trained expert actively selects unlabeled samples) to a self-learning framework in which the machine learning algorithm itself selects the most useful and informative unlabeled samples for classification purposes. In this way, the labels of the selected pixels are estimated by the classifier itself, with the advantage that no extra cost is required for labeling the selected pixels using this machine-machine framework when compared with traditional machine-human active learning. The proposed approach is illustrated with two different classifiers: multinomial logistic regression and a probabilistic pixelwise support vector machine. 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 use of self-learning represents an effective and promising strategy in the context of hyperspectral image classification.
Blind model-based fusion of multi-band and panchromatic images

This paper proposes a blind model-based fusion method to combine a low-spatial resolution multi-band image and a high-spatial resolution panchromatic image. This method is blind in the sense that the spatial and spectral responses in the degradation model are unknown and estimated from the observed data pair. The Gaussian and total variation priors have been used to regularize the ill-posed fusion problem. The formulated optimization problem associated with the image fusion can be attacked efficiently using a recently developed robust multi-band image fusion algorithm in . Experimental results including qualitative and quantitative ones show that the fused image can combine the spectral information from the multi-band image and the high spatial resolution information from the panchromatic image effectively with very competitive computational time.
Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search Are Related

This paper considers a recently emerged hyperspectral unmixing formulation based on sparse regression of a self-dictionary multiple measurement vector (SD-MMV) model, wherein the measured hyperspectral pixels are used as the dictionary. Operating under the pure pixel assumption, this SD-MMV formalism is special in that it allows simultaneous identification of the endmember spectral signatures and the number of endmembers. Previous SD-MMV studies mainly focus on convex relaxations. In this study, we explore the alternative of greedy pursuit, which generally provides efficient and simple algorithms. In particular, we design a greedy SD-MMV algorithm using simultaneous orthogonal matching pursuit. Intriguingly, the proposed greedy algorithm is shown to be closely related to some existing pure pixel search algorithms, especially, the successive projection algorithm (SPA). Thus, a link between SD-MMV and pure pixel search is revealed. We then perform exact recovery analyses, and prove that the proposed greedy algorithm is robust to noise-including its identification of the (unknown) number of endmembers-under a sufficiently low noise level. The identification performance of the proposed greedy algorithm is demonstrated through both synthetic and real-data experiments.
A Framework for Fast Image Deconvolution With Incomplete Observations.

In image deconvolution problems, the diagonalization of the underlying operators by means of the fast Fourier transform (FFT) usually yields very large speedups. When there are incomplete observations (e.g., in the case of unknown boundaries), standard deconvolution techniques normally involve non-diagonalizable operators, resulting in rather slow methods or, otherwise, use inexact convolution models, resulting in the occurrence of artifacts in the enhanced images. In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast. We iteratively alternate the estimation of the unknown pixels and of the deconvolved image, using, e.g., an FFT-based deconvolution method. This framework is an efficient, high-quality alternative to existing methods of dealing with the image boundaries, such as edge tapering. It can be used with any fast deconvolution method. We give an example in which a state-of-the-art method that assumes periodic boundary conditions is extended, using this framework, to unknown boundary conditions. Furthermore, we propose a specific implementation of this framework, based on the alternating direction method of multipliers (ADMM). We provide a proof of convergence for the resulting algorithm, which can be seen as a "partial" ADMM, in which not all variables are dualized. We report experimental comparisons with other primal-dual methods, where the proposed one performed at the level of the state of the art. Four different kinds of applications were tested in the experiments: deconvolution, deconvolution with inpainting, superresolution, and demosaicing, all with unknown boundaries.
Sparse distributed hyperspectral unmixing

Blind hyperspectral unmixing is the task of jointly estimating the spectral signatures of material in a hyperspectral images and their abundances at each pixel. The size of hyperspectral images are usually very large, which may raise difficulties for classical optimization algorithms, due to limited memory of the hardware used. One solution to this problem is to consider distributed algorithms. In this paper, we develop a distributed sparse hyperspectral unmixing algorithm using the alternating direction method of multipliers (ADMM) algorithm and ℓ1 sparse regularization. Each sub-problem does not need to have access to the whole hyperspectral image. The algorithm is evaluated using a very large real hyperspectral image.
Collaborative sparse unmixing of hyperspectral data

Sparse unmixing aims at estimating the constituent materials (endmembers) and their respective fractional abundances in each pixel of a hyperspectral image by assuming that the endmembers are present in a large collection of pure spectral signatures (spectral library), known a priori. In this paper, we propose a refinement of the sparse unmixing approach by taking into account the fact that all the pixels of the image share the same set of endmembers, thus lying in a lower dimensional subspace. Our idea is based on the collaborative lasso, which enforces sparsity across the pixels. The goal of this line of attack is to obtain higher accuracy of the estimated fractional abundances, at the same time with a decrease in the number of endmembers used to explain the observed data. The experimental results, obtained with both simulated and real data, confirm the potential of the proposed approach in the unmixing problem.
The effect of forest understory on synthetic aperture radar backscatter

This paper studies the effect of the understory on radar backscatter from forest stands. The purpose is to understand in what extent the understory presence modifies the backscatter composition. In order to account for the understory, a third layer of scattering media, just above the ground, is added to the Michigan Microwave Scattering model. The model is parametrized with experimental data gathered at a forest site, constituted by typical Portuguese forest stand of maritime pine plantations, and published data on North American trembling aspen forest. A sensitivity analysis is conducted in order to evaluate the effect of the understory at L-, C- and X-bands and different polarizations. Simulation results show the importance of the forest understory on the backscattering process, and suggest the feasibility of inverting the proposed model for understory parameter inference.
Recent developments in sparse hyperspectral unmixing

This paper explores the applicability of new sparse algorithms to perform spectral unmixing of hyperspectral images using available spectral libraries instead of resorting to well-known endmember extraction techniques widely available in the literature. Our main assumption is that it is unlikely to find pure pixels in real hyperspectral images due to available spatial resolution and mixing phenomena happening at different scales. The algorithms analyzed in our study rely on different principles, and their performance is quantitatively assessed using both simulated and real hyperspectral data sets. The experimental validation of sparse techniques conducted in this work indicates promising results of this new approach to attack the spectral unmixing problem in remotely sensed hyperspectral images.
Robust Collaborative Nonnegative Matrix Factorization for Hyperspectral Unmixing

Spectral unmixing is an important technique for remotely sensed hyperspectral data exploitation. It amounts to identifying a set of pure spectral signatures, which are called endmembers, and their corresponding fractional, draftrulesabun-dances in each pixel of the hyperspectral image. Over the last years, different algorithms have been developed for each of the three main steps of the spectral unmixing chain: 1) estimation of the number of endmembers in a scene; 2) identification of the spectral signatures of the endmembers; and 3) estimation of the fractional abundance of each endmember in each pixel of the scene. However, few algorithms can perform all the stages involved in the hyperspectral unmixing process. Such algorithms are highly desirable to avoid the propagation of errors within the chain. In this paper, we develop a new algorithm, which is termed robust collaborative nonnegative matrix factorization (R-CoNMF), that can perform the three steps of the hyperspectral unmixing chain. In comparison with other conventional methods, R-CoNMF starts with an overestimated number of endmembers and removes the redundant endmembers by means of collaborative regularization. Our experimental results indicate that the proposed method provides better or competitive performance when compared with other widely used methods.
Oil spill segmentation of SAR images via graph cuts

Segmentation of dark patches in SAR images is an important step in any oil spill detection system. Segmentation methods used so far include 'adaptive image thresholding', 'hysteresis thresholding', 'edge detection' (see  and references therein) and entropy methods like the 'maximum descriptive length' technique . This paper extends and generalizes a previously proposed Bayesian semi-supervised segmentation algorithm  oriented to oil spill detection using SAR images. In the base algorithm on which we build on, the data term is modeled by a finite mixture of Gamma distributions, with a given predefined number of components, for modeling each one of two classes (oil and water). To estimate the parameters of the class conditional densities, an expectation maximization (EM) algorithm was developed. The prior is an M- level logistic (MLL) Markov Random Field enforcing local continuity in a statistical sense. The methodology proposed in  assumes two classes and known smoothness parameter. The present work removes these restrictions. The smoothness parameter controlling the degree of homogeneity imposed on the scene is automatically estimated and the number of used classes is optional. To extend the algorithm to an optional number of classes, the so-called alpha-expansion algorithm  has been implemented. This algorithm is a graph-cut based technique that finds efficiently (polynomial complexity) the local minimum of the energy, (i.e, a labeling) within a known factor of the global minimum. In order to estimate the smoothness parameter of the MLL prior, two different techniques have been tested, namely the least squares (LS) fit and the coding method (CD) . Semi-automatic estimation of the class parameters is also implemented. This represents an improvement over the base algorithm , where parameter estimation is performed on a supervised way by requesting user defined regions of interest representing the water and the oil. The effectiveness of the proposed approach is illustrated with simulated SAR images and real ERS and ENVISAT images.
Spatial-spectral hyperspectral image compressive sensing

Over the last decade, the number of missions including hyperspectral cameras of increasing resolution has grown considerably. Traditional compression techniques have been proposed as an efficient way to store and transmit the ever increasing amount of hyperspectral data and to cope with the limited transmission bandwidth between the onboard systems and the ground stations. On the other hand, compressive sensing techniques try to deal with the same issue from a different perspective: the signal is compressed while the acquisition takes place, and the decompression is carried out by solving an optimization problem. Due to scarce onboard resources, the compressing sensing paradigm in hyperspectral systems is attractive, namely because the main computational burden to recover the original data is carried out in the ground stations, where more computational resources are available. In this paper, we develop a new technique for compressive sensing of hyperspectral images, where the measurement process takes place both in the spatial and the spectral domains by efficiently exploiting the high correlation of hyperspectral images. The proposed technique was tested using synthetic and semi-real images yielding competitive results.