nils-herrmann/clean_scirepeval huggingface dataset:
Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation

A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on low-rank and sparse representation. The proposed method is based on the separation of the background and the anomalies in the observed data. Since each pixel in the background can be approximately represented by a background dictionary and the representation coefficients of all pixels form a low-rank matrix, a low-rank representation is used to model the background part. To better characterize each pixel's local representation, a sparsity-inducing regularization term is added to the representation coefficients. Moreover, a dictionary construction strategy is adopted to make the dictionary more stable and discriminative. Then, the anomalies are determined by the response of the residual matrix. An important advantage of the proposed algorithm is that it combines the global and local structure in the HSI. Experimental results have been conducted using both simulated and real data sets. These experiments indicate that our algorithm achieves very promising anomaly detection performance.
Performance-Power Evaluation of an OpenCL Implementation of the Simplex Growing Algorithm for Hyperspectral Unmixing

Over the last few years, several new strategies for spectral unmixing of remotely sensed hyperspectral data have been proposed. Many of them have been developed to solve the most time-consuming and relevant step: endmember extraction. However, unmixing algorithms can be computationally very expensive in terms of processing time and energy consumption, a fact that compromises their use in applications under real-time and energy/power constraints. In this letter, we present a new parallel simplex growing algorithm (SGA) for hyperspectral data which exploits the memory hierarchy with operations in single-precision floating point. Those optimizations accelerate the most time-consuming parts of this method using the open computing language (OpenCL) standard. We have evaluated the performance versus energy consumption using the same open standard for parallel programming over a diverse set of heterogeneous platforms. Experiments have been conducted using real hyperspectral images collected by NASA’s Airborne Visible Infrared Imaging Spectrometer and a collection of 24 synthetic hyperspectral images simulated with different sizes and number of endmembers (10–30). Considering the power consumption and OpenCL across all the proposed devices, the analysis presented indicates that the SGA can now be executed in computationally efficient fashion, which was not possible before introducing the parallel implementation described in this letter.
Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis

The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available.
HyperMix: An open source tool for hyperspectral imaging

Spectral unmixing has been a popular technique for analyzing remotely sensed hyperspectral images. The goal of unmixing is to find a collection of pure spectral signatures (called endmembers) that can explain each (possibly mixed) pixel of the scene as a combination of endmembers, weighted by their coverage fractions in the pixel or abundances. Over the last years, many algorithms have been presented to address the different parts of the spectral unmixing chain. These algorithms can be categorized in estimation of the number of end-members, identification of the endmember signatures, and estimation of the endmember's abundances on each pixel. This work presents a tool that integrates many efficient implementations of different methods in order to build more complex processing graphical chains. Also, the tool offers compatibility with the graphical processing unit (GPU), so that algorithms are automatically executed in parallel by the GPU (if available) in order to improve performance. The tool is available online from http://hypercomphypermix.blogspot.com.es and has been validated with both synthetic and real hyper-spectral scenes providing state-of-the-art unmixing results.
A glass/silicon technology for low-power robust gas sensors

Semiconductor gas sensors are devices based on metallic oxides that operate at high temperatures for achieving good sensitivities to the gases of interest. Silicon micromachined structures are often used as platforms for obtaining both high temperatures and low-power consumption at the same time. In this paper, a microstructure based on the combination of micromachined silicon substrates and glass wafers is presented. The device incorporates an array of four different thin-film gas sensors that, depending on the design, can operate at the same or at different temperatures. The designs have been optimized by the finite element method (FEM) and the geometrical parameters of the structure have been selected in order to reduce the power consumption. The full process fabrication is presented. It is based on the combination of bulk micromachining, glass structuring, anodic bonding, and sensitive material deposition. Electrical, thermal, and mechanical tests have been done to demonstrate that the devices show high robustness and can reach high temperatures with low-power consumption.
Nonlinear Hyperspectral Unmixing Using Nonlinearity Order Estimation and Polytope Decomposition

Nonlinear hyperspectral unmixing (HSU) plays a key-role in understanding and quantifying the physical-chemical phenomena occurring over geometrically complex fields of view. Nonlinear HSU methods that do not rely on prior knowledge of the ground truth to analyze the scene are especially interesting. However, they can be affected either by overfitting or performance degradation provided by inaccurate setting of unmixing parameters. In this paper, we introduce a new nonlinear HSU architecture which aims at taking advantage of the benefit provided by the combination of polytope decomposition (POD) method together with artificial neural network (ANN)-based learning. Specifically, ANN is able to efficiently estimate the order p of the nonlinearity provided by the given scene even without the thorough knowledge of the ground truth. The ANN-based learning is used to feed the POD in order to deliver accurate unmixing based on a p-linear polynomial model. Experimental results over simulated and real scenes show promising performance of the proposed framework.
Real-Time Implementation of the Vertex Component Analysis Algorithm on GPUs

In this letter, we present a new parallel implementation of the vertex component analysis (VCA) algorithm for spectral unmixing of remotely sensed hyperspectral data on commodity graphics processing units. We first developed a C serial version of the VCA algorithm and three parallel versions: one using NVIDIA's Compute Unified Device Architecture (CUDA), another using CUDA basic linear algebra subroutines library CUBLAS, and the last using the CUDA linear algebra library CULA. Experimental results, based on the analysis of hyperspectral images acquired by a variety of hyperspectral imaging sensors, show the effectiveness of our implementation, which satisfies the real-time constraints given by the data acquisition rate.
Spatial–Spectral Hyperspectral Image Classification Using Random Multiscale Representation

This paper presents a novel spatial-spectral classification method for remotely sensed hyperspectral images. First of all, a multiscale representation technique based on random projection, referred as random multiscale representation (RMSR), is proposed to extract the spatial features from the given scene. The idea behind RMSR is to properly model the spatial characteristics comprised by each pixel vector and its neighbors by some criteria computed at all reasonable scales, and then compress the implicit high-dimensional spatial features by using a very sparse measurement matrix that approximately preserves the salient spatial information. The entire process is explicitly performed by computing simple criteria (i.e., the first two moments) at rectangular scales of random bands, according to the nonzero entries of the sparse measurement matrix. Subsequently, a composite kernel framework is utilized to balance the extracted spatial features and the original spectral features in the classifier. Our proposed method is shown to be effective for hyperspectral image classification purposes. Specifically, our experimental results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer and the reflective optics spectrographic imaging system demonstrate the effectiveness of the proposed method as compared to other state-of-the-art spatial-spectral classifiers.
Noise-robust spatial preprocessing prior to endmember extraction from hyperspectral data

This paper develops a noise-robust spatial preprocessing module which can be used prior to spectral unmixing of remotely sensed hyperspectral images. The method first derives a spatial homogeneity index which is relatively insensitive to the noise present in the original hyperspectral data. Then, it fuses this index with a spectral-based classification, obtaining a set of pure regions which are used to guide the unmixing process. An experimental comparison of the proposed method with other spatial-spectral unmixing approaches is conducted using both synthetic and real hyperspectral data collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experiments indicate that spectral unmixing can benefit from the proposed pre-processing approach, in particular, when the noise level present in the original hypespectral scene is relatively high.
Spatial/spectral endmember extraction by multidimensional morphological operations

Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data.
Multiple Morphological Profiles From Multicomponent-Base Images for Hyperspectral Image Classification

Morphological profiles (MPs) are a useful tool for remotely sensed image classification. These profiles are constructed on a base image that can be a single band of a multicomponent remote sensing image. Principal component analysis (PCA) has been used to provide other base images to construct MPs in high-dimensional remote sensing scenes such as hyperspectral images [e.g., by deriving the first principal components (PCs) and building the MPs on the first few components]. In this paper, we discuss several strategies for producing the base images for MPs, and further categorize the considered methods into four classes: linear, nonlinear, manifold learning-based, and multilinear transformation-based. It is found that the multilinear PCA (MPCA) is a powerful approach for base image extraction. That is because it is a tensor-based feature representation approach, which is able to simultaneously exploit the spectral-spatial correlation between neighboring pixels. We also show that independent component analysis (ICA) is more effective for constructing base images than PCA. Another important contribution of this paper is a new concept of multiple MPs (MMPs), aimed at synthesizing the spectral-spatial information extracted from the multicomponent base images, and further enhancing the classification accuracy of MPs. Moreover, we propose two different strategies to interpret the newly proposed MMPs by considering their hyperdimensional feature space: decision fusion and sparse classifier based on multinomial logistic regression (MLR). Experiments conducted on three well-known hyperspectral datasets are used to quantitatively assess the accuracy of different algorithms.
Cluster versus GPU implementation of an Orthogonal Target Detection Algorithm for Remotely Sensed Hyperspectral Images

Remotely sensed hyperspectral imaging instruments provide high-dimensional data containing rich information in both the spatial and the spectral domain. In many surveillance applications, detecting objects (targets) is a very important task. In particular, algorithms for detecting (moving or static) targets, or targets that could expand their size (such as propagating fires) often require timely responses for swift decisions that depend upon high computing performance of algorithm analysis. In this paper, we develop parallel versions of a target detection algorithm based on orthogonal subspace projections. The parallel implementations are tested in two types of parallel computing architectures: a massively parallel cluster of computers called Thunderhead and available at NASA’s Goddard Space Flight Center in Maryland, and a commodity graphics processing unit (GPU) of NVidia GeForce GTX 275 type. While the cluster-based implementation reveals itself as appealing for information extraction from remote sensing data already transmitted to Earth, the GPU implementation allows us to perform near real-time anomaly detection in hyperspectral scenes, with speedups over 50x with regards to a highly optimized serial version. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the attacks that collapsed the two main towers in the WTC complex.
Parallel Implementation of Sparse Representation Classifiers for Hyperspectral Imagery on GPUs

Classification is one of the most important analysis techniques for hyperspectral image analysis. Sparse representation is an extremely powerful tool for this purpose, but the high computational complexity of sparse representation-based classification techniques limits their application in time-critical scenarios. To improve the efficiency and performance of sparse representation classification techniques for hyperspectral image analysis, this paper develops a new parallel implementation on graphics processing units (GPUs). First, an optimized sparse representation model based on spatial correlation regularization and a spectral fidelity term is introduced. Then, we use this approach as a case study to illustrate the advantages and potential challenges of applying GPU parallel optimization principles to the considered problem. The first GPU optimization algorithm for sparse representation classification (SRCSC_P) of hyperspectral images is proposed in this paper, and a parallel implementation of the proposed method is developed using compute unified device architecture (CUDA) on GPUs. The GPU parallel implementation is compared with the serial and multicore implementations on CPUs. Experimental results based on real hyperspectral datasets show that the average speedup of SRCSC_P is more than 130×, and the proposed approach is able to provide results accurately and fast, which is appealing for computationally efficient hyperspectral data processing.
A novel optical waveguide microcantilever sensor for the detection of nanomechanical forces

This study presents a novel generic multipurpose probe based on an array of 20 waveguide channels with microcantilevers acting as optical waveguides operated in the visible range. The principle of operation is based on the sensitivity of energy transfer between two butt-coupled waveguides to their misalignment with respect to each other. The technique can be considered an alternative to the known methods used for the readout of the nanomechanical response of microcantilevers to the external force exerted on them. The cantilever displacement can be detected with a resolution of 18 fm//spl radic/Hz. The limit is generally defined by the shot noise of a conventional photodetector used for the readout of the output signal. Real-time parallel monitoring of several channels can be realized. In contrast to devices based on the atomic force microscope detection principle, no preliminary alignment or adjustment, except for light coupling, is required. The detection of the cantilever deflection at subnanometer range was demonstrated experimentally.
GPU Implementation of an Automatic Target Detection and Classification Algorithm for Hyperspectral Image Analysis

The detection of (moving or static) targets in remotely sensed hyperspectral images often requires real-time responses for swift decisions that depend upon high computing performance of algorithm analysis. The automatic target detection and classification algorithm (ATDCA) has been widely used for this purpose. In this letter, we develop several optimizations for accelerating the computational performance of ATDCA. The first one focuses on the use of the Gram-Schmidt orthogonalization method instead of the orthogonal projection process adopted by the classic algorithm. The second one is focused on the development of a new implementation of the algorithm on commodity graphics processing units (GPUs). The proposed GPU implementation properly exploits the GPU architecture at low level, including shared memory, and provides coalesced accesses to memory that lead to very significant speedup factors, thus taking full advantage of the computational power of GPUs. The GPU implementation is specifically tailored to hyperspectral imagery and the special characteristics of this kind of data, achieving real-time performance of ATDCA for the first time in the literature. The proposed optimizations are evaluated not only in terms of target detection accuracy but also in terms of computational performance using two different GPU architectures by NVIDIA: Tesla C1060 and GeForce GTX 580, taking advantage of the performance of operations in single-precision floating point. Experiments are conducted using hyperspectral data sets collected by three different hyperspectral imaging instruments. These results reveal considerable acceleration factors while retaining the same target detection accuracy for the algorithm.
Survey of geometric and statistical unmixing algorithms for hyperspectral images

Spectral mixture analysis (also called spectral unmixing) has been an alluring exploitation goal since the earliest days of imaging spectroscopy. No matter the spatial resolution, the spectral signatures collected in natural environments are invariably a mixture of the signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. In this paper, we give a comprehensive enumeration of the unmixing methods used in practice, because of their implementation in widely used software packages, and those published in the literature. We have structured the review according to the basic computational approach followed by the algorithms, with particular attention to those based on the computational geometry formulation, and statistical approaches with a probabilistic foundation. The quantitative assessment of some available techniques in both categories provides an opportunity to review recent advances and to anticipate future developments.
A Technique for Subpixel Analysis of Dynamic Mangrove Ecosystems With Time-Series Hyperspectral Image Data

Changes in mangrove forests are mostly caused by natural disasters, anthropogenic forces, and uncontrolled population growth. These phenomena often lead to a strong competition between existing mangrove species for their survival. Due to its enhanced spectral resolution, remotely sensed hyperspectral image data can play an important role in the task of mapping and monitoring changes in mangrove forests. In this paper, we use EO-1 Hyperion hyperspectral data to model the abundance of pure and mixed mangrove species at subpixel levels. This information is then used to analyze the interspecies competition in a study area over a period of time. Different characteristics, including the rate of growth, rate of reproduction, mortality rate, and other processes that control the coexistence of plant species are discussed. The obtained results, verified through field visits, illustrate that the proposed approach can interpret the dominance of certain mangrove species and provide insights about their state of equilibrium or disequilibrium over a fixed time frame.
A Gaussian approach to subspace based classification of hyperspectral images

Supervised classification of hyperspectral images is a challenging task due to the relatively low ratio between the number of training samples and the number of spectral channels. Subspace-based classification methods deal with this difficulty by assuming that feature vectors lie in a low-dimensional subspace. Based on the fact that a class in a hyperspectral image may be composed of a number of different groups of materials and mixture of spectral features, we suggest to estimate several lower dimensional random subspaces for the samples within each class. For subspace learning and classification, we propose to exploit the union of random subspaces in a Gaussian Mixture Model. Experimental results, conducted on two real hyperspectral data sets, indicate that the proposed method provides competitive classification results in comparison with other state-of-the-art approaches.