nils-herrmann/clean_scirepeval huggingface dataset:
Estimation of mass and depth of buried radioactive materials using neural networks

In this paper, we investigate the use of a backpropagation neural network (BPNN) to estimate the mass and depth of buried radioactive materials, i.e., depleted uranium (DU). A Lanthanum bromide (LaBr) detector is employed to collect the data for buried targets with different mass and at different depths. Due to the sparseness and randomness of a gamma spectrum, spectral transformation methods are implemented for background normalization and feature extraction. These spectral transformations are based on various binned energy windows determined by the particle swarm optimization (PSO) approach. The transformed data will be used as the inputs to BPNN. Compared with the original spectra, principle component analysis (PCA)-transformed spectra, and uniformly partitioned spectra, the optimized spectral transformed data can provide more accurate estimates.
Neyman-Pearson detector design for steady point targets with known phase detection

In order to analyze a radar's reflecting echo, a detector must be built for hypothesis testing. Due to the difficulty in assigning prior probabilities to target presence at a particular point in space and determining the cost of failing to detect a target, Neyman-Pearson detectors prove best for most radar applications. This paper investigates the design of a Neyman-Pearson detector for hypothesis testing of steady point targets with known phase. Once the hypotheses are made, the performance of the detector is evaluated in terms of the probability of detection (P/sub D/) versus different signal-to-noise ratios (SNR) for a fixed probability of false alarm (P/sub F/) The detector is further analyzed through the receiver operating characteristic (ROC) curves for different SNRs.
Interpolation of geophysical data using spatio-temporal (3D) block singular value decomposition

Soil moisture data available from the Advanced Microwave Scanning Radiometer-Earth Observation System (AMSR-E) onboard the National Aeronautic and Space Administration's (NASA) AQUA satellite has many inherent gaps. For a region in the Southeast United States, data is collected for years 2005 and 2006. This dataset has nearly 30% missing data due to radio interference, instrument errors, just to mention a few. To address this issue, an improved singular spectral analysis (SSA) -based interpolation scheme is presented. Our approach improves the existing method by utilizing the local variations in the observations for approximation of missing values and thus significantly improving the computational efficiency of the algorithm. For the validation of our interpolation scheme, a subset of SST from GODAE's high resolution sea surface temperature pilot project (GHRSST-PP) is considered. Finally, the presented scheme is also validated and tested on satellite based soil moisture retrievals from AMSR-E. Optimization of the method is based on minimizing the mean square error (MSE) and it is found to be dependent on the nature of the data. The top thirteen dominant SSA modes are usually sufficient for interpolation of missing values.
Wide area monitoring using Common Information Model and Sensor Web

Real-time monitoring of power systems has become a reality with the use of Phasor Measurement Units (PMU). Wide-area monitoring of the power system in a de-regularized world requires neighboring utilities' to share data with each other. Sharing data is a difficult process as different utilities use different software applications that store data in their own proprietary data-formats. To minimize the data format constraints, EPRI (U.S. Electric Power Research Institute) developed the Common Information Model (CIM) for power systems. Using CIM as a common format for data from different utilities, the disparate applications can work together. Further, these CIM data can be stored in a data concentrator application like Sensor Web that obtains these messages from different utilities and sorts and stores them in a central repository. The data from this repository can be used to perform tasks like state estimation and contingency analysis.
Improved sequential endmember extraction algorithms

Most of sequential endmember extraction algorithms, such as iterative error analysis (IEA), vertex component analysis (VCA), and simplex growing algorithm (SGA), use sequential forward selection (SFS) searching strategy. The advantage is its low computational complexity. However, it is sensitive to the initial condition. To reduce the “nesting effect”, sequential forward floating selection (SFFS) strategy is investigated in this paper. Experimental results show that SFFS can improve the quality of the extracted endmembers without the initial condition problem. In order to reduce the computational cost of SFFS in endmember extraction, we propose a hybrid searching strategy by combining SFS and SFFS, which can produce a similar or even identical endmember set as the original SFFS.
A Hybrid Approach for Building Extraction From Spaceborne Multi-Angular Optical Imagery

The advent of high resolution spaceborne images leads to the development of efficient detection of complex urban details with high precision. This urban land use study is focused on building extraction and height estimation from spaceborne optical imagery. The advantages of such methods include 3D visualization of urban areas, digital urban mapping, and GIS databases for decision makers. In particular, a hybrid approach is proposed for efficient building extraction from optical multi-angular imagery, where a template matching algorithm is formulated for automatic estimation of relative building height, and the relative height estimates are utilized in conjunction with a support vector machine (SVM)-based classifier for extraction of buildings from non-buildings. This approach is tested on ortho-rectified Level-2a multi-angular images of Rio de Janeiro from WorldView-2 sensor. Its performance is validated using a 3-fold cross validation strategy. The final results are presented as a building map and an approximate 3D model of buildings. The building detection accuracy of the proposed method is improved to 88%, compared to 83% without using multi-angular information.
Use of rejection class to enhance airborne imagery classification

The performance of a prior classification work is enhanced by introducing a rejection class that accurately describes a particular source of false alarm. Success of this enhancement is measured by observing changes in the receiver operating characteristic (ROC) curves corresponding to two different fixed strengths of the rejection class. Source data is taken from airborne imagery collected by a line-scanner system known as REMIDS (Remote Identification System) through funding from the Environmental Security Technology Certification Program (ESTCP).
AN improved ensemble appraoch with Probabilistic Neural Network-Combinational algorithm

The Combinational algorithm in the ensemble approach plays a key role towards the performance. The standard majority voting, weighted average and probabilistic averaged weight could not tune well the decisions of the multi-classifiers to the class label. We propose the modeling of the multi-classifier decisions to the output variable using Probabilistic Neural Networks as the combinational algorithm. This proposed implementation of combinational algorithm gave a significant performance improvement against the standard combiners.
Color Representation and Classification for Hyperspectral Imagery

A hyperspectral image contains information in hundreds of spectral channels. The information is sparsely distributed in such a huge 3D image cube. In practical applications, it may be helpful if such a high dimensional data can be displayed into an informative color image. This 3-band color image provides a vivid and quick overview of existing objects/materials and their distribution in the image scene. Condensing the data from the original high dimensional space into such a low dimensional space will lose information. Keeping this in mind, our objective is to display the useful information contained in an original data as much as possible and as distinctively as possible. Our display approaches are based on the techniques in the class of principal components analysis (PCA). The generated color image is evaluated using a detection algorithm, and the result is compared with the one using the original data.
Sensor web for interoperability in power systems

Electrical systems are highly interrelated. A disturbance in one utility of a region can influence the stability of other utilities. Proper monitoring and critical information exchange in real time is the only solution to prevent an outage in this highly vulnerable system. But, the disparity in protocols used in the power industry and lack of infrastructure of information exchange are proving to be hindrance to achieve a reliable deregularized industry. In this research, an emerging Open Geospatial Consortium (OGC) standards-based sensor web technology has been adopted for achieving interoperability in the power systems. Sensor Web Enablement (SWE) and Common Information Model (CIM) provide a solution to heterogeneity of data and lack of central repository of the sensor data for proper action, in case of a contingency. The sensor data from utilities, published in CIM format, are exposed via Sensor Observation Service (SOS). This provides a standard method for discovering and accessing sensor data between utilities, which facilitates the rapid response to handle contingences. In addition, the application of SWE in power industry pushes power industry one step closer towards automation.
Wavelet as a diagnostic tool for fault classification and identification in underground power cable

In this paper, a diagnostic technique based on the wavelet transform (WT) for identifying and classifying different fault types is developed. In general, the wavelet transform provides multiresolution type analysis with variable window size; thus better suited for such analysis. Variable window sizes provide good resolution in the time and frequency domains; thus helping not only in identifying the fault, but also in localizing it. Three types of cables are used in this study; a normal cable, a shorted cable, and a cable with holes. Wavelet transformation is then applied to the impedance in each case, with various windowing to eliminate possible interference. The identification is then carried out by examining the resulting impedance magnitude. This study reveals that the wavelet transform is a better tool for fault identification and classification.
Spectral unmixing of airborne hyperspectral imagery for mapping giant reed infestations

Spectral unmixing techniques applied to hyperspectral imagery were examined for mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems and riparian areas throughout the southern United States and northern Mexico. Airborne hyperspectral imagery with 102 usable bands covering a spectral range of 475–845 nm was collected from two giant reed-infested sites along the US-Mexican portion of the Rio Grande. The imagery was transformed with minimum noise fraction (MFN) to reduce the spectral dimensionality and noise. Linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) were applied to the transformed MNF imagery based on endmember spectra extracted from the imagery. The abundance images were then converted into classification maps. For comparison, spectral angle mapper (SAM) and support vector machine (SVM) were used to classify the imagery. Accuracy assessment showed that MTMF was slightly better than or similar to LSU and that SVM performed better than the other three methods. The results from this study will be useful for distinguishing giant reed from associate plant species.
High-frequency analysis of the Ellipticus antenna

Efficient numerical solution techniques have been developed and used to examine the electromagnetic fields that can be developed in the working volume of the CW Ellipticus antenna operated at frequencies from 100 kHz to 1 GHz. An exponentially tapered transition section is designed to obtain the desired illumination pattern in the working volume. The input transition section is needed for impedance matching and to drive efficiently the Ellipticus antenna. A parametric study is performed to ascertain the performance of the Ellipticus antenna for frequencies up to 1 GHz.
An Adaptive Spectral Transformation Approach to Pan-Sharpening

Data transformation of multispectral imagery along the spectral dimension plays an important role in minimizing the spectral distortion of the resultant pan-sharpened image. Although there are various techniques available for spectral transformation, most of them are, data dependent. Hence, there is an ambiguity about the selection of the "appropriate" spectral transformation method. To alleviate this problem, an adaptive spectral transformation approach for pan-sharpening is presented in this paper. The efficiency of the presented method is tested by performing pan-sharpening of the high resolution (IKONOS and Quickbird) and the medium resolution (LandSat7 ETM+) datasets. The evaluation of the pan-sharpened images using global validation indexes reveal that the adaptive spectral approach helps reducing the spectral distortion.
FDTD analysis of noise radiation and propagation

The analysis of effects from noise illumination of electrical and electronic systems is complicated by stochastic rather than deterministic variables. An analysis technique is presented for noise propagation, where the noise is modeled as a time series of discrete time impulses with amplitudes computed by a white Gaussian noise simulation. Using the noise source to simulate the drive of an appropriate antenna, the analysis of the radiated noise signal is accomplished through the use of the finite-difference time-domain (FDTD) formulation. The presented technique is verified by a comparison with the results obtained from using the method of moments and a wire antenna radiator. In addition, limitations, stability, and applicability of the presented formulation are discussed.
Parallel optimization-based spectral transformation for detection and classification of buried radioactive materials

In this paper, we investigate detection and classification of buried radioactive materials of interest using data collected by a Sodium Iodide (NaI) detector with short sensor dwell time (i.e., less than or equal to 1 s). Due to the sparseness and randomness of a gamma spectrum, four different spectral transforms are implemented as mechanisms for background normalization and feature extraction. These spectral transforms are based on binned energy windows determined by a particle swarm optimization (PSO) method. However, the overall process is time-consuming. Since parallel computation is an appropriate approach to reduce the computation burden of the PSO-based search process, the parallel implementation on cluster for optimal bin partition is proposed in this paper. The speedup performance and resulting detection and classification performance are investigated.
Multiscale spectral-spatial hyperspectral classification using Multilinear PCA and contourlet transform

Multilinear principal component analysis (MPCA) is explored for hyperspectral imagery classification in this study. MPCA is first applied to the original image data, in 3rd-order tensor form, to reduce the dimensionality. Then, a multiscale transform technique (i.e., contourlet transform, etc.) is applied to each of the principal components (PCs) generated from MPCA. The resulting transform coefficients can be used as spatial features. These multiscale spatial features are further combined with spectral features (e.g., PCs) to yield spectral-spatial features for hyperspectral classification. The experimental result shows that improvement is obvious when the reduced dimensionality is small.
DTM extraction of lidar returns via adaptive processing

Airborne light detection and ranging is emerging as a tool to provide accurate digital terrain models (DTMs) of forest areas, since it can penetrate beneath the canopy. Although traditional techniques, such as linear prediction, have shown to be robust type methods for the extraction of DTMs, they fail to effectively model terrain with steep slopes and large variability. In this paper, a modified linear prediction technique, followed by adaptive processing and refinement, is developed. A comparison with the traditional linear prediction method is provided along with statistical measures to ascertain the validity of the foregoing technique.
Efficient Dense-Field Copy–Move Forgery Detection

We propose a new algorithm for the accurate detection and localization of copy-move forgeries, based on rotation-invariant features computed densely on the image. Dense-field techniques proposed in the literature guarantee a superior performance with respect to their keypoint-based counterparts, at the price of a much higher processing time, mostly due to the feature matching phase. To overcome this limitation, we resort here to a fast approximate nearest-neighbor search algorithm, PatchMatch, especially suited for the computation of dense fields over images. We adapt the matching algorithm to deal efficiently with invariant features, so as to achieve higher robustness with respect to rotations and scale changes. Moreover, leveraging on the smoothness of the output field, we implement a simplified and reliable postprocessing procedure. The experimental analysis, conducted on databases available online, proves the proposed technique to be at least as accurate, generally more robust, and typically much faster than the state-of-the-art dense-field references.
Exploiting Patch Similarity for SAR Image Processing: The nonlocal paradigm

Most current synthetic aperture radar (SAR) systems offer high-resolution images featuring polarimetric, interferometric, multifrequency, multiangle, or multidate information. SAR images, however, suffer from strong fluctuations due to the speckle phenomenon inherent to coherent imagery. Hence, all derived parameters display strong signal-dependent variance, preventing the full exploitation of such a wealth of information. Even with the abundance of despeckling techniques proposed over the last three decades, there is still a pressing need for new methods that can handle this variety of SAR products and efficiently eliminate speckle without sacrificing the spatial resolution. Recently, patch-based filtering has emerged as a highly successful concept in image processing. By exploiting the redundancy between similar patches, it succeeds in suppressing most of the noise with good preservation of texture and thin structures. Extensions of patch-based methods to speckle reduction and joint exploitation of multichannel SAR images (interferometric, polarimetric, or PolInSAR data) have led to the best denoising performance in radar imaging to date. We give a comprehensive survey of patch-based nonlocal filtering of SAR images, focusing on the two main ingredients of the methods: measuring patch similarity and estimating the parameters of interest from a collection of similar patches.
Recursive Texture Fragmentation and Reconstruction segmentation algorithm applied to VHR images

The Texture Fragmentation and Reconstruction (TFR) algorithm, recently proposed for the segmentation of textured images, has been applied with promising results to highresolution remote-sensing images. The algorithm provides a sequence of nested segmentation maps which allow the analysis at various scales of observation. However, the performance which is very good at large scales, with complex semantic areas retrieved with remarkable accuracy, becomes less satisfactory at finer scales. In this paper we propose to use the TFR in a recursive fashion, segmenting the image in just two regions, initially, with each region further segmented only if relevant subre-gions emerge. The recursive TFR allows one to better adapt to local statistics and to extract significant textures also at finer scales. Early experimental results validate the effectiveness of the new algorithm.
A hierarchical segmentation algorithm for multiresolution satellite images

We propose here a new algorithm for the unsupervised segmentation of multiresolution remote-sensing images. After a first segmentation step on the high-resolution panchromatic data, the image is converted in a set of disjoint regions, which are then clustered and merged progressively, based on multispectral, spatial and textural properties, producing a sequence of nested segmentation maps which provide a thorough and multi-scale description of the image. The algorithm is fast, since it works mainly at a region level, and preserves fine details thanks to the initial step at the high-resolution level. Experimental results on IKONOS data confirm the algorithm potential and point out to a few problems to address in future research.
SAR Image Despeckling by Soft Classification

We propose a new approach to synthetic aperture radar (SAR) despeckling, based on the combination of multiple alternative estimates of the same data. The many despeckling methods proposed in the literature possess different and often complementary strengths and weaknesses. Given a reliable pixelwise characterization of the image, one can take advantage of this diversity by selecting the most appropriate combination of estimators for each image region. Following this paradigm, we develop a simple algorithm where only two state-of-the-art despeckling tools, characterized by complementary properties, are linearly combined. To ensure the smooth combination of contributes, thus avoiding new artifacts, we propose a novel soft classification method, where a basic estimate of homogeneity is improved through nonlocal and multiresolution processing steps. The results of a number of experiments conducted on both synthetic and real-world SAR images are very promising, thus confirming the potential of the proposed approach.
Supervised segmentation of remote-sensing multitemporal images based on the tree-structured Markov random field model

We deal with the supervised segmentation of multi-temporal remote-sensing images following a statistical Bayesian approach. To take into account prior information on the class of images, like the correlation between neighboring pixels, as well as the available knowledge about the structure of the current image, we model the image as a tree-structured Markov random field. The data collected at two different dates are jointly processed as a single multi-component image, with the classes defined a priori based on ground truth information and grouped in changed and unchanged macro-classes. Experimental results in terms of classification accuracy prove the effectiveness of the proposed technique with respect to non-contextual methods, as well as to a disjoint approach. In addition, the classification tree allows for a direct interpretation of the result