nils-herrmann/clean_scirepeval huggingface dataset:
HYCA: A New Technique for Hyperspectral Compressive Sensing

Hyperspectral imaging relies on sophisticated acquisition and data processing systems able to acquire, process, store, and transmit hundreds or thousands of image bands from a given area of interest. In this paper, we exploit the high correlation existing among the components of the hyperspectral data sets to introduce a new compressive sensing methodology, termed hyperspectral coded aperture (HYCA), which largely reduces the number of measurements necessary to correctly reconstruct the original data. HYCA relies on two central properties of most hyperspectral images, usually termed data cubes: 1) the spectral vectors live on a low-dimensional subspace; and 2) the spectral bands present high correlation in both the spatial and the spectral domain. The former property allows to represent the data vectors using a small number of coordinates. In this paper, we particularly exploit the high spatial correlation mentioned in the latter property, which implies that each coordinate is piecewise smooth and thus compressible using local differences. The measurement matrix computes a small number of random projections for every spectral vector, which is connected with coded aperture schemes. The reconstruction of the data cube is obtained by solving a convex optimization problem containing a data term linked to the measurement matrix and a total variation regularizer. The solution of this optimization problem is obtained by an instance of the alternating direction method of multipliers that decomposes very hard problems into a cyclic sequence of simpler problems. In order to address the need to set up the parameters involved in the HYCA algorithm, we also develop a constrained version of HYCA (C-HYCA), in which all the parameters can be automatically estimated, which is an important aspect for practical application of the algorithm. A series of experiments with simulated and real data shows the effectiveness of HYCA and C-HYCA, indicating their potential in real-world applications.
Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing

Spectral unmixing aims at estimating the fractional abundances of pure spectral signatures (also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral imaging instrument. In recent work, the linear spectral unmixing problem has been approached in semisupervised fashion as a sparse regression one, under the assumption that the observed image signatures can be expressed as linear combinations of pure spectra, known a priori and available in a library. It happens, however, that sparse unmixing focuses on analyzing the hyperspectral data without incorporating spatial information. In this paper, we include the total variation (TV) regularization to the classical sparse regression formulation, thus exploiting the spatial-contextual information present in the hyperspectral images and developing a new algorithm called sparse unmixing via variable splitting augmented Lagrangian and TV. Our experimental results, conducted with both simulated and real hyperspectral data sets, indicate the potential of including spatial information (through the TV term) on sparse unmixing formulations for improved characterization of mixed pixels in hyperspectral imagery.
On the use of spectral libraries to perform sparse unmixing of hyperspectral data

In recent years, the increasing availability of spectral libraries has opened a new path toward solving the hyperspec-tral unmixing problem in a semi-supervised fashion. The spectrally pure constituent materials (called endmembers) can be derived from a (potentially very large) spectral library and used for unmixing purposes. The advantage of this approach is that the results of the unmixing process do not depend on the availability of pure pixels in the original hyperspectral data nor on the ability of an endmember extraction algorithm to identify such endmembers. However, resulting from the fact that spectral libraries are usually very large, this approach generally results in a sparse solution. In this paper, we investigate the sensitivity of sparse unmixing techniques to certain characteristics of real and synthetic spectral libraries, including parameters such as mutual coherence and spectral similarity between the signatures contained in the library. Our main goal is to illustrate, via detailed experimental assessment, the potential of using spectral libraries to solve the spectral unmixing problem.
SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization

In this paper we present a supervised hyperspectral image segmentation algorithm based on a convex formulation of a marginal maximum a posteriori segmentation with hidden fields and structure tensor regularization: Segmentation via the Constraint Split Augmented Lagrangian Shrinkage by Structure Tensor Regularization (SegSALSA-STR). This formulation avoids the generally discrete nature of segmentation problems and the inherent NP-hardness of the integer optimization associated. We extend the Segmentation via the Constraint Split Augmented Lagrangian Shrinkage (SegSALSA) algorithm  by generalizing the vectorial total variation prior using a structure tensor prior constructed from a patch-based Jacobian . The resulting algorithm is convex, time-efficient and highly parallelizable. This shows the potential of combining hidden fields with convex optimization through the inclusion of different regularizers. The SegSALSA-STR algorithm is validated in the segmentation of real hyperspectral images.
Class-specific image denoising using importance sampling

In this paper, we propose a new image denoising method, tailored to specific classes of images, assuming that a dataset of clean images of the same class is available. Similarly to the non-local means (NLM) algorithm, the proposed method computes a weighted average of non-local patches, which we interpret under the importance sampling framework. This viewpoint introduces flexibility regarding the adopted priors, the noise statistics, and the computation of Bayesian estimates. The importance sampling viewpoint is exploited to approximate the minimum mean squared error (MMSE) patch estimates, using the true underlying prior on image patches. The estimates thus obtained converge to the true MMSE estimates, as the number of samples approaches infinity. Experimental results provide evidence that the proposed denoiser outperforms the state-of-the-art in the specific classes of face and text images.
Spectral partitioning for hyperspectral remote sensing image classification

In this paper, we present a new approach for spectral partitioning which is intended to deal with ill-posed problems in hyperspectral image classification. First, we use adaptive affinity propagation (AAP) to intelligently group the original spectral bands. Such grouping strategy not only allows us to reduce the number of spectral bands, but also to provide a different perspective on the original hyperspectral data. Then, a multiple classifier system (MCS) based on multinomial logistic regression (MLR) is applied. The system is trained using different band subsets resulting from the previously conducted intelligent grouping, and the results are combined to produce a final classification result. Our experimental results, conducted using the well-known hyperspectral scenes collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region in NW Indiana, indicate that the proposed method can provide important advantages in terms of classification, in particular, when the number of training samples available a priori is very low.
Hyperspectral unmixingwith sparse group lasso

Sparse unmixing has been recently introduced as a mechanism to characterize mixed pixels in remotely sensed hyperspectral images. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In available spectral libraries, it is observed that the spectral signatures appear organized in groups (e.g. different alterations of a single mineral in the U.S. Geological Survey spectral library). In this paper, we explore the potential of the sparse group lasso technique in solving hyperspectral unmixing problems. Our introspection in this work is that, when the spectral signatures appear in groups, this technique has the potential to yield better results than the standard sparse regression approach. Experimental results with both synthetic and real hyperspectral data are given to investigate this issue.
Hyperspectral Blind Reconstruction From Random Spectral Projections

This paper proposes a blind hyperspectral reconstruction technique termed spectral compressive acquisition (SpeCA) conceived to spaceborne sensors systems which are characterized by scarce onboard computing and storage resources and by communication links with reduced bandwidth. SpeCA exploits the fact that hyperspectral vectors often belong to a low-dimensional subspace and it is blind in the sense that the subspace is learned from the measured data. SpeCA encoder is computationally very light; it just computes random projections (RPs) with the acquired spectral vectors. SpeCA decoder solves a form of blind reconstruction from RPs whose complexity, although higher than that of the encoder, is very light in the sense that it requires only the modest resources to be implemented in real time. SpeCA coding/decoding scheme achieves perfect reconstruction in noise-free hyperspectral images (HSIs) and is very competitive in noisy data. The effectiveness of the proposed methodology is illustrated in both synthetic and real scenarios.
Hyperspectral compressive sensing from spectral projections

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 which will be later recovered on the ground station. 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 of data acquired 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 exploit the fact that HSIs admits a low dimensional linear representation. The proposed method is blind in the sense that linear representation is learned with low computational cost from the compressed measurements. Furthermore the proposed method is very light from the computational point of view and it can recover perfectly the original image in noise-free scenarios. The effectiveness of the proposed method is illustrated in both synthetic and real scenarios.
Plant production system monitoring via multiple signal classification and sparse regression

In this paper, we propose a dictionary pruning technique for signal unmixing, which is a recent unmixing concept aimed at extracting the signal contribution of an individual class of endmembers to each observed pixel in a hyperspectral scene. We focus on a specific application in the area of plant production system monitoring. The pruning technique allows to infer the physical state of vegetation in a region of interest on the ground, by selecting appropriate spectra from a very large spectral library designed to model a wide variety of such possible states. On the one hand, Multiple Endmember Spectral Mixture Analysis (MESMA), a combinatorial technique, was intensively used to solve the signal unmixing problem. Sparse unmixing, a fast approach dealing with spectral libraries, was recently proposed as a reliable technique for spectral unmixing. The performances of both techniques are influenced by the large number of spectra collected in the libraries. The aim of this paper is two-fold: 1) to present a subspace-based method for library pruning, such that only spectra likely to be present in the image are used in unmixing, which is expected to improve MESMA results, and 2) to introduce sparse unmixing as a reliable signal unmixing technique, given the natural ability of these methods to deal with applications in which a low number of atoms is used to accurately describe every observed vector. The results obtained in a simulated environment show the potential of the proposed scheme to boost the signal unmixing performance both in terms of accuracy and running time.
B-HYCA: Blind hyperspectral compressive sensing

Compressive Sensing has raised as a very useful way to save costs in the acquisition equipment due to the fact that with this technique we can measure the signal in an already compressed form. This is very interesting in hyperspectral applications due to the large amount of data that the hyper-spectral sensors collect, store and transmit to the ground stations. Over the last years many compressive sensing methods have been applied to hyperspectral images, and others have been proposed for exploiting the unique features of this kind of images. Over the last years, many techniques have been proposed to perform compressive sensing in hyperspectral imaging. One of them is the Hyperspectral Coded Aperture (HYCA), which exploits two characteristics of hyper-spectral imagery: 1) the hyperspectral vectors belong to a low dimensional subspace, and 2) the data cube components exhibit very high correlation in the spatial and in the spectral domains. However, HYCA requires the knowledge of the subspace in advance, which, very often, may compromise its applicability. In this paper it is presented a new technique similar to HYCA which does not require the knowledge of the subspace in advance; the proposed technique is termed blind HYCA (B-HYCA) and it performs a form of blind hyperspectral compressed sensing.
A fast parallel hyperspectral coded aperture algorithm for compressive sensing using OpenCL

In this paper, we develop a fast implementation of an hyperspectral coded aperture (HYCA) algorithm on different platforms using OpenCL, an open standard for parallel programming on heterogeneous systems, which includes a wide variety of devices, from dense multicore systems from major manufactures such as Intel or ARM to new accelerators such as graphics processing units (GPUs), field programmable gate arrays (FPGAs), the Intel Xeon Phi and other custom devices. Our proposed implementation of HYCA significantly reduces its computational cost. Our experiments have been conducted using simulated data and reveal considerable acceleration factors. This kind of implementations with the same descriptive language on different architectures are very important in order to really calibrate the possibility of using heterogeneous platforms for efficient hyperspectral imaging processing in real remote sensing missions.
Super-Resolution of Multispectral Multiresolution Images from a Single Sensor

Some remote sensing sensors, acquire multispectral images of different spatial resolutions in variable spectral ranges (e.g. Sentinel-2, MODIS). The aim of this research is to infer all the spectral bands, of multiresolution sensors, in the highest available resolution of the sensor. We formulate this problem as a minimisation of a convex objective function with an adaptive (edge-reserving) regulariser. The data-fitting term accounts for individual blur and downsampling per band, while the regulariser "learns" the discontinuities from the higher resolution bands and transfers them to other bands. We also observed that the data can be represented in a lower-dimensional subspace, reducing the dimensionality of the problem and significantly improving its conditioning. In a series of experiments with simulated data, we obtain results that outperform state-of-the-art, while showing competitive qualitative results on real Sentinel-2 data.
Semi-supervised discriminative random field 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 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 work, we propose a new semi-supervised discriminative random field (SSDRF) technique for spectral-spatial hyperspectral image classification. The proposed approach is validated using a hyperspectral dataset collected using NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region. The obtained results indicate that, by automatically generating unlabeled information, the proposed SSDRF algorithm exhibits very good performance in terms of accuracies in comparison with supervised algorithms. In terms of computational cost, the proposed SSDRF algorithm self learns the classifier with the same complexity as the supervised algorithm, and converges very efficiently.
Collaborative Sparse Regression for Hyperspectral Unmixing

Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In this paper, we present a refinement of the sparse unmixing methodology recently introduced which exploits the usual very low number of endmembers present in real images, out of a very large library. Specifically, we adopt the collaborative (also called “multitask” or “simultaneous”) sparse regression framework that improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Our experimental results with both synthetic and real hyperspectral data sets show clearly the advantages obtained using the new joint sparse regression strategy, compared with the pixelwise independent approach.
Multiple Feature Learning for Hyperspectral Image Classification

Hyperspectral image classification has been an active topic of research in recent years. In the past, many different types of features have been extracted (using both linear and nonlinear strategies) for classification problems. On the one hand, some approaches have exploited the original spectral information or other features linearly derived from such information in order to have classes which are linearly separable. On the other hand, other techniques have exploited features obtained through nonlinear transformations intended to reduce data dimensionality, to better model the inherent nonlinearity of the original data (e.g., kernels) or to adequately exploit the spatial information contained in the scene (e.g., using morphological analysis). Special attention has been given to techniques able to exploit a single kind of features, such as composite kernel learning or multiple kernel learning, developed in order to deal with multiple kernels. However, few approaches have been designed to integrate multiple types of features extracted from both linear and nonlinear transformations. In this paper, we develop a new framework for the classification of hyperspectral scenes that pursues the combination of multiple features. The ultimate goal of the proposed framework is to be able to cope with linear and nonlinear class boundaries present in the data, thus following the two main mixing models considered for hyperspectral data interpretation. An important characteristic of the presented approach is that it does not require any regularization parameters to control the weights of considered features so that different types of features can be efficiently exploited and integrated in a collaborative and flexible way. Our experimental results, conducted using a variety of input features and hyperspectral scenes, indicate that the proposed framework for multiple feature learning provides state-of-the-art classification results without significantly increasing computational complexity.
Non-cyclic deconvolution using an augmented lagrangian method

Alternating direction methods have been recently employed for addressing the optimization problems resulting from the regular-ization approach to linear inverse problems. These methods have been found to achieve state-of-the-art speed in problems such as (frame-based or total-variation-based) cyclic deconvolution, MRI reconstruction, removal of multiplicative or Poisson noise, and inpainting. The speed of these algorithms has three main reasons: (a) they use a regularized version of the Hessian of part of the objective function; (b) they exploit the fact that products by the direct operator and its transpose can be computed efficiently (using fast implementations such as the FFT and/or fast wavelet transforms); (c) a matrix inversion required by the algorithm can be solved in closed form, again using properties of unitary transforms and tight frames. In this paper, we extend this class of algorithms to problems where condition (c) is not met; this is the case, for example, of non-cyclic deconvolution problems. Instead of the exact solution of the matrix inversion, we use an iterative method. Preliminary experiments reported show promising results in terms of computation speed.
Linear spectral unmixing using collaborative sparse regression and correlated supports

This paper presents a new Bayesian collaborative sparse regression method for linear unmixing of hyperspectral images. Our contribution is twofold; first, we propose a new Bayesian model for structured sparse regression in which the supports of the sparse abundance vectors are a priori spatially correlated across pixels. Secondly, we propose an advanced Markov chain Monte Carlo algorithm to estimate the posterior probabilities that materials are present or absent in each pixel, and, conditionally to the maximum marginal a posteriori configuration of this support, compute the MMSE estimates of the abundance vectors. A remarkable property of this algorithm is that it self-adjusts the values of the parameters of the Markov random field, thus relieving practitioners from setting regularisation parameters, namely by cross-validation. The proposed methodology is illustrated with real hyperspectral data.