nils-herrmann/clean_scirepeval huggingface dataset:
Automated generation of semi-labeled training samples for nonlinear neural network-based abundance estimation in hyperspectral data

As the initial stage of a supervised classification, the quality of training has a significant effect on the entire classification process and its accuracy. In hyperspectral data analysis, a judicious selection of training samples can be tremendously difficult due to the presence of subpixel targets and mixed pixels, in particular, when no prior knowledge about the data is available. For instance, the Multi-Layer Perceptron (MLP) neural network can provide very accurate nonlinear estimations of fractional abundances, provided that the training set contains all possible mixture conditions. However, the requirement of large volumes of training data is a serious limitation in remote sensing because, even if classes concurring to a per-pixel cover class mixture are known, proportions of these classes are very difficult to be estimated a priori. This paper investigates, explores and further proposes solutions to resolve the issues above. Specifically, we develop a nonlinear neural network-based mixture model, coupled with unsupervised algorithms for automated generation of semi-labeled samples that can be effectively used for mixed pixel classification. These unsupervised algorithms, intended for situations where ancilliary information is difficult to be collected prior to data analysis, rely on the principle that patterns that lie close to the location of decision boundaries in feature space are more informative than patterns drawn from the class cores. Computer simulations and real experiments are conducted for performance analysis of nonlinear unmixing techniques based on training samples. KeywordsHyperspectral imaging, Nonlinear mixture analysis, Training samples, Semi-labeled samples, Multi-layer perceptron.
Unmixing Prior to Supervised Classification of Remotely Sensed Hyperspectral Images

Supervised classification of hyperspectral images is a very challenging task due to the generally unfavorable ratio between the number of spectral bands and the number of training samples available a priori, which results in the Hughes phenomenon. For this purpose, several feature extraction methods have been investigated in order to reduce the dimensionality of the data to the right subspace without significant loss of the original information that allows for the separation of classes. In this letter, we explore the use of spectral unmixing for feature extraction prior to supervised classification of hyperspectral data using support vector machines. The proposed feature extraction strategy has been implemented in the form of four different unmixing chains and evaluated using two different scenes collected by National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer. The experiments suggest competitive results but also show that the definition of the unmixing chains plays an important role in the final classification accuracy. Moreover, differently from most feature extraction techniques available in the literature, the features obtained using linear spectral unmixing are potentially easier to interpret due to their physical meaning.
Spatial preprocessing for endmember extraction using unsupervised clustering and orthogonal subspace projection concepts

In this paper, we develop a new spatial preprocessing strategy which can be applied prior to a spectral-based endmember extraction process for unmixing of hyperspectral data. Our proposed approach directs the endmember searching process to regions which are both spectrally pure and spatially homogeneous in the scene. Our experimental results, conducted using simulated hyperspectral data sets with known endmembers and fractional abundances, reveal that the proposed approach can successfully integrate the spatial and spectral information in the search for more relevant endmembers.
One-Class Classification of Remote Sensing Images Using Kernel Sparse Representation

Sparse representations have been widely studied in remote sensing image analysis in recent years. In this paper, we develop a novel method for one-class classification (OCC) using a kernel sparse representation model for remotely sensed imagery. Training samples taken from the target class alone are used to build a learning dictionary for the sparse representation model, which is then optimized to produce a reconstruction residual. In the proposed model, a pixel is classified as the target class if the obtained reconstruction residual for the pixel is smaller than a given threshold; otherwise, the pixel is labeled as the outlier class. To improve the data separability between the target and outliner classes, the training samples taken from the target class are mapped into a high-dimensional feature space using a kernel function to build a learning dictionary for the kernel sparse representation model. OCC is then conducted in the mapped high-dimensional feature space using the reconstruction residual threshold, following the same principle as OCC in the original feature space. The proposed OCC method is evaluated and compared with several existing OCC methods in three different case studies. The experimental results indicate that the proposed method outperforms these existing methods, particularly when using a kernel sparse representation.
Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification

In this letter, we introduce an efficient algorithm to estimate the noise correlation matrix in the initial stage of the hyperspectral signal identification by minimum error (HySime) method, commonly used for signal subspace identification in remotely sensed hyperspectral images. Compared with the current implementations of this stage, the new algorithm for noise estimation relies on the reliable QR factorization, producing correct results even when operating with single-precision arithmetic. Additionally, our algorithm exhibits a lower computational cost, and it is highly parallel. The experiments on a multicore server, using two real hyperspectral scenes, expose that these theoretical advantages carry over to the practical results.
Parallel implementation of the N-FINDR endmember extraction algorithm on commodity graphics processing units

Endmember extraction is an important technique in the context of spectral unmixing of remotely sensed hyperspectral data. Winter's N-FINDR algorithm is one of the most widely used and successfully applied methods for endmember extraction from remotely sensed hyperspectral images. Depending on the dimensionality of the hyperspectral data, the algorithm can be time consuming. In this paper, we propose a new parallel implementation of the N-FINDR algorithm. The proposed implementation is quantitatively assessed in terms of both endmember extraction accuracy and parallel efficiency, using two different generations of commercial graphical processing units (GPUs) from NVidia. Our experimental results indicate that the parallel implementation performs better with latest-generation GPUs, thus taking advantage of the increased processing power of such units.
Parallel implementation of endmember extraction algorithms from hyperspectral data

Automated extraction of spectral endmembers is a crucial task in hyperspectral data analysis. In most cases, the computational complexity of endmember extraction algorithms is very high, in particular, for very high-dimensional datasets. However, the intrinsic properties of available techniques are amenable to the design of parallel implementations. In this letter, we evaluate several parallel algorithms that represent three representative approaches to the problem of extracting endmembers. Two parallel algorithms have been selected to represent a first class of algorithms based on convex geometry concepts. In particular, we develop parallel implementations of approximate versions of the N-FINDR and pixel purity index algorithms, along with a parallel hybrid of both techniques. A second class is given by algorithms based on constrained error minimization and represented by a parallel version of the iterative error analysis algorithm. Finally, a parallel version of the automated morphological endmember extraction algorithm is also presented and discussed. This algorithm integrates the spatial and spectral information as opposed to the other discussed algorithms, a feature that introduces additional considerations for its parallelization. The proposed algorithms are quantitatively compared and assessed in terms of both endmember extraction accuracy and parallel efficiency, using standard AVIRIS hyperspectral datasets. Performance data are measured on Thunderhead, a parallel supercomputer at NASA's Goddard Space Flight Center
New materials and technologies for micromachined metal oxide gas sensor arrays

We present a new silicon micromachined low power heater platform comprising sensor arrays which feature a number of different gas sensitive metal oxides. The sensing elements of the arrays are composed of alternatively n-type SnO/sub 2/, n-type WO/sub 3/, n-type ZnO, p-type Cr/sub 1.8/Ti/sub 0.2/O/sub 3+z/ or n-type V/sub 2/O/sub 5/. Characterisation of the arrays under trace gas exposure is presented.
Foreword to the Special Issue on Big Data in Remote Sensing

The papers in this special issue focus on the deployment of Big Data applications for use in remote sensing. This issue is intended to introduce the latest techniques to manage, exploit, process, and analyze big data in remote sensing applications. It contains 11 papers that exhibit the latest advances in Big Data in Remote Sensing. To understand big data, usually three facets should be taken into account from owning data, data methods, and data applications, which contribute together to a single big data life cycle, including identification of applications, data collections, data processing, data analysis, data visualization, data evaluation, and so on.
Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery

Anomaly detection is an active topic in hyperspectral imaging, with many practical applications. Reed-Xiaoli detector (RXD), a widely used method for anomaly detection, uses the covariance matrix and mean vector to represent background signals, assuming that the background information adjusts to a multivariate normal distribution. However, in general, real images present very complex backgrounds. As a result, in many situations, the background information cannot be properly modeled. An important reason is that that background samples often contain also anomalous pixels and noise, which lead to a high false alarm rate. Therefore, the characterization of the background is essential for successful anomaly detection. In this paper, we develop two novel approaches: weighted-RXD (W-RXD) and linear filter-based RXD (LF-RXD) aimed at improving background in RXD-based anomaly detection. By reducing the weight of the anomalous pixels or noise signals and increasing the weight of the background samples, W-RXD can provide better estimations of the background information. In turn, LF-RXD uses the probability of each pixel as background to filter wrong anomalous or noisy instances. Our experimental results, intended to analyze the performance of the newly developed anomaly detectors, indicate that the proposed approaches achieve good performance when compared with other classic approaches for anomaly detection in the literature.
Efficient implementation of morphological opening and closing by reconstruction on multi-core parallel systems

We present an efficient parallel implementation of morphological opening and closing by reconstruction operations, which have been used in the past for extracting relevant features prior to classification of remotely sensed hyperspectral images using morphological profiles. The proposed implementation has been developed and tested on various multi-core parallel platforms. These types of multi-processor systems are increasingly being used as a commodity parallel computing platform in different application domains. Our experimental results demonstrate that the proposed parallel codes fully exploit the processing power available in the considered multi-core machines.
Real-Time Endmember Extraction on Multicore Processors

In this letter, we discuss the use of multicore processors in the acceleration of endmember extraction algorithms for hyperspectral image unmixing. Specifically, we develop computationally efficient versions of two popular fully automatic endmember extraction algorithms: orthogonal subspace projection and N-FINDR. Our experimental results, based on the analysis of hyperspectral data collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible InfraRed Imaging Spectrometer, indicate that endmember extraction algorithms can significantly benefit from these inexpensive high-performance computing platforms, which can offer real-time response with some programming effort.
Magnetically actuated microvalve for disposable drug infusor

A magnetic microfluidic valve has been analyzed to improve its performance. Operation relies on the use of a permanent magnet which interacts with an electrodeposited layer of Co-Ni on a V-shaped cantilever beam. The deflection caused by the magnetic forces opens or closes the fluid flow. The microvalve performance has been optimized by means of finite element analysis. The FEA model has been experimentally validated using confocal microscopy and used to improve the magnetic circuit. Then, a fluidic cell has been built and the microvalve has been demonstrated to work as a flow regulator, when being magnetically actuated, and as a check valve. Experiments showed that the fabricated prototypes presented a good performance from 0 to 50 mbar, with control on a flow up to 18 seem.
Advanced Spectral Classifiers for Hyperspectral Images: A review

Hyperspectral image classification has been a vibrant area of research in recent years. Given a set of observations, i.e., pixel vectors in a hyperspectral image, classification approaches try to allocate a unique label to each pixel vector. However, the classification of hyperspectral images is a challenging task for a number of reasons, such as the presence of redundant features, the imbalance among the limited number of available training samples, and the high dimensionality of the data.
Reserved, on demand or serverless: Model-based simulations for cloud budget planning

Cloud computing providers offer a variety of pricing models, complicating the client decision, as no single model is the cheapest in all scenarios. In addition, small to mediumsized organizations frequently lack personnel that can navigate the intricacies of each pricing model, and as a result, end up opting for a sub-optimal strategy, leading to overpaying for computing resources or not being able to meet performance goals. In this paper, we: (1) present the results of a study that shows that, in Ecuador, a considerable percentage of companies choose conservative pricing strategies, (2) present a case study that shows that the conservative pricing strategy is suboptimal under certain workloads, and (3) propose a set of models, a tool and a process that can be used by tenants to properly plan and budget their cloud computing costs. Our tool is based on M (t)/M/∗ queuing theory models and is easy to configure and use. Note that, even though we are motivated by our study of adoption of cloud computing technologies in Ecuador, our tool and process are widely applicable and not restricted to the Ecuadorian context.
A novel thresholding method for automatically detecting stars in astronomical images

Tracking the position of stars or bright bodies in images from space represents a valuable source of information in different application domains. One of the simplest approaches used for this purpose in the literature is image thresholding, where all pixels above a certain intensity level are considered stars, and all other pixels are considered background. Two main problems have been identified in the literature for image thresholding-based star identification methods. Most notably, the intensity of the background is not always constant; i.e., a sloping background could give proper detection of stars in one part of the image, while in another part every pixel can have an intensity over the threshold value and will thus be detected as a star. Also, there is always some degree of noise present in astronomical images, and this noise can create spurious peaks in the intensity that can be detected as stars, even though they are not. In this work, we develop a novel image thresholding-based methodology which addresses the issues above. Specifically, the method proposed in this work relies on an enhanced histogram-based thresholding method complemented by a collection of auxiliary techniques aimed at searching inside diffuse objects such as galaxies, nebulas and comets, and thus enhance their detection by eliminating noise artifacts. Its black-box design and our experimental results indicate that this novel method offers potential for being included as a star identification module in already existent techniques and systems that require accurate tracking and recognition of stars in astronomical images.
Morphological feature extraction and spectral unmixing of hyperspectral images

Hyperspectral image processing has been a very active area in remote sensing and other application domains in recent years. Despite the availability of a wide range of advanced processing techniques for hyperspectral data analysis, a great majority of available techniques for this purpose are based on the consideration of spectral information separately from spatial information information, and thus the two types of information are not treated simultaneously. In this paper, we describe several innovative spatial/spectral techniques for hyperspectral image processing. The techniques described in this work cover different aspects of hyperspectral image processing such as dimensionality reduction, feature extraction, and spectral unmixing. The techniques addressed in this paper are based on concepts inspired by mathematical morphology, a theory that provides a remarkable framework to achieve the desired integration of spatial and spectral information. The proposed techniques are experimentally validated using standard hyperspectral data sets with ground-truth, and compared to traditional approaches in the hyperspectral imaging literature, revealing that the integration of spatial and spectral information can significantly improve the analysis of hyperspectral scenes when conducted in simultaneous fashion.
Learning Discriminative Sparse Representations for Hyperspectral Image Classification

In sparse representation (SR) driven hyperspectral image classification, signal-to-reconstruction rule-based classification may lack generalization performance. In order to overcome this limitation, we presents a new method for discriminative sparse representation of hyperspectral data by learning a reconstructive dictionary and a discriminative classifier in a SR model regularized with total variation (TV). The proposed method features the following components. First, we adopt a spectral unmixing by variable splitting augmented Lagrangian and TV method to guarantee the spatial homogeneity of sparse representations. Second, we embed dictionary learning in the method to enhance the representative power of sparse representations via gradient descent in a class-wise manner. Finally, we adopt a sparse multinomial logistic regression (SMLR) model and design a class-oriented optimization strategy to obtain a powerful classifier, which improves the performance of the learnt model for specific classes. The first two components are beneficial to produce discriminative sparse representations. Whereas, adopting SMLR allows for effectively modeling the discriminative information. Experimental results with both simulated and real hyperspectral data sets in a number of experimental comparisons with other related approaches demonstrate the superiority of the proposed method.
Spatial-spectral endmember extraction from hyperspectral imagery using multi-band morphology and volume optimization

We develop a new approach for characterization of mixed pixels in remotely sensed hyperspectral images. The proposed method first performs joint spatial-spectral pixel characterization via extended morphological transformations, and then automatically extracts pure spectral signatures (called endmembers) using volume optimization and convex geometry concepts. The proposed method outperforms other widely used approaches in the analysis of a real hyperspectral scene collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite mining district in Nevada. Ground-truth information available from U.S. Geological Survey is used to substantiate our findings.
Decision fusion based on extended multi-attribute profiles for hyperspectral image classification

In this paper, a new classification strategy for remotely sensed hyperspectral image data is presented and discussed. The proposed approach adopts a decision fusion strategy which combines a well-established classifier, such as the support vector machine (SVM), with the information provided by extended multi-attribute morphological profiles (EMAPs). EMAPs provide a multilevel characterization of an image created by using a sequence of morphological attribute filters to model different kinds of the structural information contained in such image. In our proposed decision fusion strategy, the SVM classifier is first applied to each of the components (which are associated to different attributes) provided by the EMAP. Then, we apply a decision fusion rule to obtain the final classification result. In our experimental results, conducted with a reference urban hyperspectral data sets widely used in the literature, we show that the proposed strategy provides state of the art results (especially when limited training sets are used.
Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral Images

Hyper spectral imaging is a new technique in remote sensing that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have explored the application of morphological operations to integrate both spatial and spectral responses in hyper spectral data analysis. These operations rely on ordering pixel vectors in spectral space, but there is no unambiguous means of defining the minimum and maximum values between two vectors of more than one dimension. Our original contribution in this paper is to examine the impact of different vector ordering strategies on the definition of multi-channel morphological operations. Our focus is on morphological unmixing, which decomposes each pixel vector in the hyper spectral scene into a combination of pure spectral signatures (called end members) and their associated abundance fractions, allowing sub-pixel characterization. Experiments are conducted using real hyper spectral data sets collected by NASA/JPL's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system.