nils-herrmann/clean_scirepeval huggingface dataset:
On the performance improvement for linear discriminant analysis-based hyperspectral image classification

In this paper, we present a strategy to improve the performance of Fisher's linear discriminant analysis (FLDA) in dimensionality reduction for hyperspectral image classification. The practical difficulty of applying FLDA to hyperspectral imagery includes the unavailability of enough training samples and unknown information for all the classes including background. The original FLDA has been modified to avoid the requirements of training samples and complete class knowledge, which needs the desired class signatures only. The modified FLDA (MFLDA) can better preserve class information in the low-dimensional space. However, for an image scene with p known classes, the data dimensionality after FLDA and MFLDA transform is p-1. The class-separability performance of FLDA and MFLDA may be significantly improved if the transformed data dimensionality is p instead of p-1. An approach is proposed for this purpose and experimental results demonstrate its advantage.
On correcting HV impulse measurements by means of adaptive filtering and deconvolution

An adaptive data smoothing technique that uses a serial correlation test has been used to preprocess HV transient measurements before the application of the deconvolution routine to estimate the input signal. This technique has the advantage that no prior knowledge of the signal or noise characteristics is required. Successful applications to both simulated data with white-Gaussian noise and actual measured data with unknown noise characteristics are presented to demonstrate the accuracy of the technique.
High resolution satellite precipitation estimate using cluster ensemble cloud classification

The link-based cluster ensemble (LCE) method is applied to a high resolution satellite precipitation estimation (HSPE) algorithm, a modified form of the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. The HSPE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering and classification of cloud patches using cluster ensemble technique; and 4) dynamic application of brightness temperature (Tb) and rain rate relationships, derived using satellite observations. The LCE method combines multiple data partitions from different clustering in order to cluster the cloud patches. The results show that using the cluster ensemble increase the performance of rainfall estimates if compared to the HSPE algorithm using Self Organizing Map (SOM). The Heidke Skill Score (HSS) is improved 5% to 7% at medium and high level of rainfall thresholds.
A Systematic Approach to Wavelet-Decomposition-Level Selection for Image Information Mining From Geospatial Data Archives

Recently, wavelet-based methods have been efficiently used for segmentation and primitive feature extraction to expedite the image-retrieval process of semantic-enabled frameworks for image information mining from geospatial data archives. However, the use of wavelets may introduce aliasing effects due to subband decimation at a certain decomposition level. This paper addresses the issue of selecting a suitable wavelet decomposition level, and a systematic selection process is developed. To validate the applicability of this method, a synthetic image is generated to qualitatively and quantitatively assess the performance. In addition, results for a Landsat-7 Enhanced Thematic Mapper Plus imagery archive are illustrated, and the F-measure is used to assess the feasibility of this method for the retrieval of different classes
Detection of radioactive materials using surface proximity antennas

The objective of this study is to detect and identify radioactive materials using surface proximity antennas. To do so, we first developed a simulation model of earth as seen in Fig 1a. We have then designed microstrip patch antennas propagating (-z-direction) towards earth surface to understand the effects of earth and buried objects on the antenna performance . Various types of objects (pec, radioactive materials, glass, etc.) were placed at different depths and simulations were carried out to detect changes in antenna parameters such as return loss. It was seen that buried objects does affect the antenna performance changing with the type of the object. We have searched for frequencies in the spectrum and considered antenna array systems to increase the detectability of the problem. Figure 2a shows the antenna return loss comparison for three different cases; a) without the object, b) object located right beneath the surface, and c) object located 20cm beneath the surface. In Figure 2b, we present electric field distribution. The hot spot represents the region where the object is located. At the time of the presentation, we will provide antenna design details, and simulation results for various antenna topologies and operating frequencies.
Dimensionality reduction of hyperspectral imagery with sparse and collaborative graphs

Hyperspectral image dimensionality reduction with graph-based approaches is considered. With available labeled samples, a graph can be formed with these samples by constructing an affinity matrix through their sparse or collaborative representations. In addition, sparse or collaborative representation can be done using within-class samples, resulting in block-sparse representation, although within each block the representation can be either sparse or non-sparse (collaborative). The experimental results show that the block-sparse plus within-block-collaborative representation can yield the best performance.
Fusion of diverse features and kernels using LP-norm based multiple kernel learning in hyperspectral image processing

Multiple kernel learning (MKL) is an elegant tool for heterogeneous fusion. In support vector machine (SVM) based classification, MK is a homogenization transform and it provides flexibility in searching for high-quality linearly separable solutions in the reproducing kernel Hilbert space (RKHS). However, performance often depends on input and kernel diversity. Herein, we explore a new way to extract diverse features from hyperspectral imagery using different proximity measures and band grouping. The output is fed to ℓp-norm MKL for feature-level fusion, where larger p's are preferred for diverse vs sparse solutions. Preliminary results on benchmark data indicates that ℓp-norm MKSVM of diverse features and kernels leads to noticeable performance gain.
On the Use of the Genetic Algorithm Filter-Based Feature Selection Technique for Satellite Precipitation Estimation

A feature selection technique is used to enhance the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched by wavelet features. The feature selection technique includes a feature similarity selection method and a filter-based feature selection using genetic algorithm (FFSGA). It is employed in this study to find an optimal set of features where redundant and irrelevant features are removed. The entropy index fitness function is used to evaluate the feature subsets. The results show that using the feature selection technique not only improves the equitable threat score by almost 7% at some threshold values for the winter season, but also it extremely decreases the dimensionality. The bias also decreases in both the winter (January and February) and summer (June, July, and August) seasons.
Semantics-Enabled Knowledge Management for Global Earth Observation System of Systems

The Global Earth Observation System of Systems (GEOSS) is a distributed system of systems built on current international cooperation efforts among existing Earth observing and processing systems. The goal is to formulate an end-to-end process that enables the collection and distribution of accurate, reliable Earth Observation data, information, products, and services to both suppliers and consumers worldwide. Earth Observations (EO) are obtained from a multitude of sources and requires tremendous efforts and coordination among different agencies and user groups to come to a shared understanding on a set of concepts involved in a domain. Semantic metadata plays a crucial role in resolving the differences in meaning, interpretation, usage of the same or related data. Also the knowledge about the geopolitical background of the originating datasets could be encoded in the metadata that would address the diversity on a global scale. In distributed environments like GEOSS modularization is inevitable. In this paper we propose a framework for modular ontologies based knowledge management approach for GEOSS in which we explore approaches on formulating smaller interconnected ontologies. This analysis is exercised in a coastal zone domain.
Use of geo-ontology matching to measure the degree of interoperability

Interoperability is a key concern in geospatial domain. Approaches based on Geo-ontology which represents the semantics of the geospatial information are currently popular. However, there still remains the problem of reconciliation of different geo-ontological representations. Hence, many geo-ontology matching systems have been developed to harmonize and integrate various information sources for resolving the heterogeneity and achieve interoperability. However, deriving the degree of interoperability between two information sources is a challenge. Measuring the degree of interoperability helps in understanding, the readiness or the amenability to communicate across similar representations, thus identifying those information sources (out of many similar ones) that can be integrated. This paper describes a Geo-ontology matching framework to measure the degree of interoperability.
A new parameter estimation algorithm for an electrical analogue battery model

This paper describes a new parameter estimation algorithm for a well-recognized electrical analogue battery model. The limited bandwidth characteristic of the electrical analogue battery model is introduced and discussed, on which the new parameter estimation algorithm is built. While a real battery system is non-linear and time variant, a truncated representation of the system is provided by a commonly studied non-physical "electrical analogue" battery model. However, the limited bandwidth characteristic of the electrical analogue battery model is often overlooked. The proposed algorithm starts by assessing a desired battery application, followed by modeling the battery according to the application bandwidth, and then estimating the model parameters using the sequential quadratic programming method. This approach recognizes and makes use of the limited bandwidth of the battery model by reconciling the approximation with the limited bandwidth required by the simulation. This approach has been experimentally verified on a 6.8 Ah Ultralife UBBL10 Lithium-ion battery module. Since this electrical analogue battery model is independent of the battery chemistry it is applicable to Lithium-ion, Nickel-Metal-Hydride (NiMH), and Lead-acid batteries, among others.
Mobile computing and Sensor Web Services for coastal buoys

A new generation of mobile device users is coming of age in the next decade. These users can explore the mobile internet with its new features, services, and applications. Recently, an application platform like the Google's Android mobile platform has revolutionized open applications development for the mobile platform. As increasing number of companies expose their services as web services, enabling flexible mobile access to distributed web resources is a very relevant challenge. However, the current web is a collection of human readable pages that are unintelligible to computer programs. Semantic Web and Web Services have the potential of overcoming this limitation. Semantic web technology and the advent of universal and mobile access to internet services, provides additional features like knowledge-based, location or context aware information. For this, a standard ontology called Ontology Web Language for Services (OWL-S) is employed. The vision is to automatically discover services like Sensor Web Service, Geospatial Information Service, etc from mobile. In this work, we apply the above methods to the coastal sensor web.
Wavelet domain statistical hyperspectral soil texture classification

This communication presents an automatic soil texture classification system using hyperspectral soil signatures and wavelet-based statistical models. Previous soil texture classification systems are closely related to texture classification methods, where images are used for training and testing. In this study, we develop a novel system using hyperspectral soil textures, which provide rich information and intrinsic properties about soil textures, where two wavelet-domain statistical models, namely, the maximum-likelihood and hidden Markov models, are incorporated for the classification task. Experimental results show that these methods are both reliable and robust.
Adaptive hyperspectral pixel unmixing using best bands analysis and dc insensitive singular value decomposition

We present a novel approach to two-class linear hyperspectral pixel unmixing. The mixed pixel signatures and training signatures are preprocessed by low pass filtering, and then a best bands analysis determines which bands to use in the final unmixing stage. Abundance estimate statistical properties and partial residues are utilized in adapting the abundance estimate. The well known singular value decomposition method is modified to allow decomposition that is insensitive to DC offsets (DCI_SVD). The proposed method is compared to a best level discrete wavelet transform approach using the Haar mother wavelets . The contributions of this paper are: (1) The unmixing algorithm and (2) The DCI_SVD algorithm. Simulation results using hyperspectral signatures of Cotton, Cogongrass, Johnsongrass, Sicklepod and soil taken with an Analytical Spectral Devices handheld spectroradiometer show the efficacy of the proposed method and its superiority to the DWT approach in the harder unmixing cases.
Tensor-based offset-sparsity decomposition for hyperspectral image classification

In this paper, the tensor-based offset-sparsity decomposition (TOSD) method, or low-rank and sparse decomposition, is applied to hyperspectral imagery, where the low-rank tensor is considered to be enhanced or pruned data and used for classification. In the tensor form of dataset, all the information of the original 3D data cube, includes spatial and spectral information, can be better reserved. To make the low-rank assumption more possibly true, spatial and spectral segmentations are conducted in a preprocessing step for the TOSD. The experimental results demonstrate the TOSD offers better performance than the matrix-based one, and the spatial-spectral segmentation can further improve the performance.
Radar target identification via a combined E-pulse/SVD-Prony method

In this paper, the use of a combined SVD-Prony/E-pulse method for radar target identification and/or discrimination is investigated. The SVD-Prony method is first used in conjunction with a data preprocessing algorithm to accurately estimate the complex natural resonances of a radar target response. An E-pulse is then constructed from these estimates and convolved with the corresponding late-time response of the target for identification and discrimination. Results are presented for both simulated data with additive white Gaussian noise and measured data with unknown noise characteristics to demonstrate the application and accuracy of this technique.
Semisupervised classification of hyperspectral remote sensing images with spatial majority voting

Semisupervised learning is very useful for hyperspectral image classification, when the number of labeled samples is insufficient. In this paper, a simple selftraining method with spatial majority filtering is proposed to find unlabeled samples that can assist classifier training. It is based on the assumption that an unlabeled pixel, after initial classification, that has the same class label of neighboring pixels, is reliable. This results in majority voting (MV) based algorithm. The performance can be improved if the spectral similarity between a center and surrounding pixels is considered in weighted majority voting (MWV). Experiments with four real datasets confirm their effectiveness in improving classification accuracy when the number of labeled samples is very limited.
An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets

High correlation among the neighboring pixels both spatially and spectrally in a multispectral image makes it necessary to use an efficient data transformation approach before performing pan-sharpening. Wavelets and principal component analysis (PCA) methods have been a popular choice for spatial and spectral transformations, respectively. Current PCA-based pan-sharpening methods make an assumption that the first principal component (PC) of high variance is an ideal choice for replacing or injecting it with high spatial details from the high-resolution histogram-matched panchromatic (PAN) image. This paper presents a combined adaptive PCA-contourlet approach for pan-sharpening, where the adaptive PCA is used to reduce the spectral distortion and the use of nonsubsampled contourlets for spatial transformation in pan-sharpening is incorporated to overcome the limitation of the wavelets in representing the directional information efficiently and capturing intrinsic geometrical structures of the objects. The efficiency of the presented method is tested by performing pan-sharpening of the high-resolution (IKONOS and QuickBird) and the medium-resolution (Landsat-7 Enhanced Thematic Mapper Plus) datasets. The evaluation of the pan-sharpened images using global validation indexes reveal that the adaptive PCA approach helps reducing the spectral distortion, and its merger with contourlets provides better fusion results.
A numerical analysis of the balanced transmission line wave sensor

A transmission line exhibiting an electric dipole moment and a magnetic dipole moment which are perpendicular has been numerically analyzed, using the Sandia National Laboratory PATCH code, to evaluate its suitability as an incident field sensor for EMP (electromagnetic pulse) measurement over the frequency range of 10 kHz to 1 GHz. Two numerical models of the antenna structure which only differ in their triangulation schemes have been developed for this study. A course model with larger triangles is used to analyze the structure for frequencies up to 100 MHz. Above 100 MHz, a fine model with smaller triangles is used. The performance of this antenna is evaluated in terms of the front-to-back ratio and its frequency response. A comparison with theoretical and measured results is made to ascertain the accuracy of the numerical study. Currents on the antenna structure are evaluated for the case of maximum sensor response as the frequency increases. The two models yield similar results. This confirms the validity of the numerical models used in the PATCH code.<<ETX>>
An unmixing algorithm based on vicinal information

Pixel unmixing is a highly undetermined procedure. Due to sensor measurement noise, environmental changes, and intraclass variance, it is very difficult to make a high quality classification. It is even more unlikely to decompose the mixed pixel with a high degree of confidence. Many previous algorithms for linear pixel unmixing were only concerned with a single pixel or a small set of pixels with the same mixing proportions. In this paper we present a novel method for pixel unmixing based on the classification information of vicinal pixels. Since the end members' signatures are non-orthogonal, there are multiple possible combinations of these signatures to produce a particular mixed pixel. Thus, the selection of the end member for a mixed pixel becomes a vital problem. In addition to the widely used residual root mean square error (RMSE), four new performance metrics are proposed for comparing quantitatively the classification accuracy of the proposed method and conventional single pixel unmixing algorithm. While conventional methods aim to minimize residual errors, our method tries to achieve the best possible correct end member combination. The case study shows that in terms of both RMSE and new performance metrics, the proposed method achieves significant improvement over conventional algorithms.
Spectroscopic data correction using parametric modeling

Spectroscopic data are prone to instrumentation noise. Traditional methods for noise removal require a priori knowledge of the spectra. One does not have such information when analyzing chemical components in the environment. In this paper, parametric methods for performing spectral analysis of spectroscopic data are investigated. The corresponding model parameters used to describe the spectrum as well as the model order are examined for both synthetic and real data for efficient peak detection accuracy and noise removal. A comparison with the results obtained to the Savitzky-Golay fitter is also made to demonstrate the applicability of parametric methods.
A spatio-temporal ontological model for flood disaster monitoring

During an extreme event such as flood disaster, it is important to study flood inundation and receding patterns to understand the dynamic spatio-temporal behavior of flood. In addition to the general change detection techniques in RS, a proper conceptualization of ‘change’ during flood disaster is necessary to model its dynamic behavior. This motivated the development of Ontology, which is able to capture the dynamically evolving phenomenon. This Ontology is envisaged as a precursor for developing applications that integrate the spatio-temporal dimensions of a dynamically evolving system such as floods. This paper describes the conceptual framework that was developed to address the same.
Optimal wavelet features for an infrared satellite precipitation estimate algorithm

A satellite precipitation estimation algorithm based on wavelet features is investigated to find the optimal wavelet features in terms of wavelet family and sliding window size. In this work, the infrared satellite based images along with ground gauge (radar corrected) observations are used for the retrieval rainfall. The goal of this work is to find an optimal wavelet transform to represent better features for cloud classification and rainfall estimation. Our approach involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) feature extraction using a wavelet-based method; 3) clustering and classification of cloud patches using neural network, and 4) dynamic application of brightness temperature (Tb) and rain rate relationships, derived using satellite observations. The results show that Haar and Symlet wavelets with sliding window size 5×5 have better estimate performance than other wavelet families and window sizes.
Big data processing using hpc for remote sensing disaster data

Voluminous data (Multispectral, Hyperspectral) from Variety of sensors (Airborne sensors, space borne sensors) with Velocity (high temporal resolution) when used for decision making to support natural disasters such as earthquakes, floods, oil-spills etc., for near real time accurate responses, is a problem that needs Big Data Analytics. To gain rapid insight from this big data, high performance computing (HPC) with some scalable solution that reduces the execution time are in extreme demand. To serve this real time need, scalable hybrid parallelism approach based on state of art multi-core GPUs and Message Passing Interface (MPI) is explored for analyzing remote sensing disaster data. Spatio-temporal remote sensing data of oil-spill at Gulf of Mexico captured by LANDSAT 7 ETM+ is considered for analysis. The core objective includes performance evaluation of the analysis process across various parallel implementation platforms.