nils-herrmann/clean_scirepeval huggingface dataset:
Standards-Based Coastal Sensor Web

Coastal buoys and stations provide frequent, high quality marine observations for oceanographic study, weather service, atmospheric and public safety. Sharing of the generated data sets requires tremendous efforts and coordination among the different sensor network agencies to come to a shared understanding and for dissemination in a uniform way. Syntactic standardization provides data description models that are agreed upon by all the stakeholders. In addition, there is a need for semantic enrichment of the information sources which would help to understand the context of the data and helps to resolve the meaning, interpretation or usage of the same or related data. The standardized data models facilitate improved information retrieval on a variety of Spatiotemporal scales. In this paper we describe the mining of these information sources through a Web services based framework. The sensor observation service component of this framework allows operations such as spatial, temporal subsetting, filtering etc. Further, the terminology involved in the coastal domain is being conceptualized in the form of Ontology. The knowledgebase being developed using this ontological model is amenable to querying using SPARQL which is a standardized RDF query language. The knowledge-enabled client being developed will allow to process queries on the coastal sensors networks that goes beyond the prevalent key words based searches.
Wavelet features for information mining in remote sensing archives

Feature selection and extraction is an initial stage of image information mining, hence it is the vital step in the whole process. Content and semantic based interactive mining systems describe remote sensing images by means of relevant features. Using global features based on color, shape, and texture, these systems possess a rich semantic value, but they fail to capture the local properties of the image. To overcome this limitation, a number of region-based retrieval systems have been proposed. Existing region-based retrieval systems for information mining in remote sensing image archives extract primitive features based on color, texture, and shape from the segmented homogenous region and retrieve images based on similarity regions. The texture features used are based on the Spatial Gray Level Dependency (SGLD) matrices. The region-based image retrieval techniques using wavelet transforms have recently gained momentum in multimedia image archives. Inspired by the successful application of wavelet transform techniques in other imagery archives, this paper proposes to perform image segmentation using color and texture features from the wavelet coefficients for the region-based retrieval in remote sensing image archives. The feasibility study shows that the wavelet technique could be used in retrieval of images from the remote sensing archives.
Automated texture recognition system based on 2D minimum variance spectral estimation

The primary feature of any image texture is the spatial frequency content. This paper proposed the use of a 2D minimum variance spectral estimation (MVSE) method for recognizing target multispectral image textures. The power spectral density of the target texture is estimated via MVSE. This estimate is then used as a feature to discriminate between target and nontarget textures. A remotely sensed multispectral image of a row crop agricultural field is analyzed and, the corresponding results are presented to illustrate the applicability of the proposed technique.
A Machine Learning Based Spatio-Temporal Data Mining Approach for Detection of Harmful Algal Blooms in the Gulf of Mexico

Harmful algal blooms (HABs) pose an enormous threat to the U.S. marine habitation and economy in the coastal waters. Federal and state coastal administrators have been devising a state-of-the-art monitoring and forecasting system for these HAB events. The efficacy of a monitoring and forecasting system relies on the performance of HAB detection. We propose a machine learning based spatio-temporal data mining approach for the detection of HAB events in the region of the Gulf of Mexico. In this study, a spatio-temporal cubical neighborhood around the training sample is introduced to retrieve relevant spectral information of both HAB and non-HAB classes. The feature relevance is studied through mutual information criterion to understand the important features in classifying HABs from non-HABs. Kernel based support vector machine is used as a classifier in the detection of HABs. This approach gives a significant performance improvement by reducing the false alarm rate. Further, with the achieved classification accuracy, the seasonal variations and sequential occurrence of algal blooms are predicted from spatio-temporal datasets. New variability visualization is introduced to illustrate the dynamic behavior of HABs across space and time.
Image information mining for coastal disaster management

In this paper we propose a framework that focuses on the need for rapid image information mining in a coastal disaster event where it is necessary to explore vast amounts of data from multiple remote sensing sensors in real or near real time. The proposed system; Rapid Image Information Mining (RIIM) is a region based approach where in lieu of prevalent pixel based methods it localizes interesting zones and extracts information from them that are stored in a knowledge base. A set of primitive features are extracted from the regions, whose relevance for a particular land cover class or a combination of classes is then assessed based on a wrapper based genetic algorithm (GA) approach. In this, we use an induction algorithm along with the GA to arrive at an optimal set of features. We investigate feature selection and feature generation using this wrapper approach. A support vector machines based classification is applied for generating predictive models for those land cover classes that are important in coastal disaster events. In RUM, searching for a particular land cover type (e.g. flooded agriculture) is based on the actual meaning and content of it in the image instead of just the metadata.
Bi-Capon beamforming for MIMO radar: A correlation matrix-based method

In this paper, a bi-Capon beamforming (BCB) algorithm for MIMO radar based on a simplified correlation matrix is investigated. By vectorising the received data matrix and its transpose and iteratively optimizing two lower dimensional weight vectors, the bi-Capon beamforming (BCB) method can significantly decrease the computational complexity and the training samples requirement. Simulation results are presented to demonstrate the effectiveness of the proposed method.
An image-adaptive watermark based on a redundant wavelet transform

An image-adaptive watermarking technique based upon a redundant wavelet transform is proposed. The redundant transform provides an overcomplete representation of the image which facilitates the identification of significant image features via a simple correlation operation across scales. Although the watermarking algorithm is image adaptive, it is not necessary for the original image to be available for successful detection of the watermark. The performance and robustness of the proposed technique is tested by applying common image-processing operations such as filtering, requantization, and JPEG compression. A quantitative measure is proposed to objectify performance; under this measure, the proposed technique outperforms a wavelet scheme based on the usual critically sampled DWT.
Hyperspectral feature extraction using contourlet transform

In this paper, we explore hyperspectral feature extraction using the contourlet transform (CT), a promising multireolution analysis technique emerging in recent years. Hyperspectral imagery is first processed in the spectral domain with some decorrelation techniques. Then the nonsubsampled CT (NSCT) is applied in the spatial domain. The resulting NSCT coefficients are used as features for hyperspectral analysis. The spectral processing techniques being explored include one-dimensional discrete wavelet transform, principal component analysis, and band selection. The extracted features are tested in classification using support vector machine, which yield promising results.
Effect of vegetation height and volume scattering on soil moisture classification using synthetic aperture radar (SAR) images

Soil moisture monitoring around earthen levees can aid in the detection of vulnerability to potential failure of a levee segment. Estimation and classification of soil moisture from SAR is difficult when the surface is covered with significant vegetation. In levees the soil is typically covered with a uniform layer of grass. An increase in the height of grass creates more volume scattering and degrades the relationship between the backscattering and soil moisture. In this work the effect of different heights of grass on the soil moisture classification of earthen levees is studied. To classify the soil moisture a back propagation neural network is used with the following methodology: (1) segmentation of levee and buffer area from the background; (2) extracting the backscatter and texture features such as GLCM (Grey- Level Co-occurrence Matrix) and wavelet features; (3) training the back propagation neural network classier; (4) testing the area of interest and validation of the results using ground truth data. The preliminary results show that the height of grass has a significant impact on soil moisture classification accuracy. The grass height increase from one month's springtime growth caused the accuracy to decrease by around 20%.
On the transmission line formed by two elliptical conductors

GLS3D, a recently developed solver code, coupled with the existing EAGLE grid generation code, is used to numerically solve for the potential and charge distributions of a transmission line formed by two elliptical conductors. This technique employs the finite-difference solution using boundary fitted coordinates. Results obtained by this technique and by the finite-difference technique with a rectangular grid are presented. A comparison with theoretical results for circular and flat conductors is made to illustrate the validity of the technique.
Change Analysis for Hyperspectral Imagery

In this paper, the change vector analysis for hyperspectral imagery is investigated. Although plentiful change information is included in the change vectors with very high dimensionality, which permits the potential of finer change analysis, it is also very challenging to analyze these change vectors since any simple deterministic approaches using vector magnitudes and directions may not be feasible. In addition, the prior knowledge of ground truth is unavailable in most practical cases, where change analysis has to be completely unsupervised. Aiming at these difficulties, a simple but efficient relative radiometric normalization method is analyzed, and two automated approaches for change detection and classification using the hyperspectral change vectors after normalization are introduced. The experiment using real CASI datasets demonstrates the promising result.
Interpolation of incompletely and non-uniformly sampled survey data for buried radioactive point source detection

At the US Army's test sites, fired penetrators made of Depleted Uranium (DU) have been buried under ground and become hazardous waste. The first step for the detection and mitigation of DU contamination is surveying a site. Typically a survey is performed in an incomplete and non-uniform manner, causing difficulties for the subsequent detection techniques. In this research, we study how to interpolate such survey data into a uniform grid. We derive an inverse distance covariance matrix Ȃ, to map an incomplete and non-uniform sample vector v into g. We demonstrate that the information contained in Ȃ and g̑ can be used for interpolation through linear Kriging, to yield a uniform grid. We verify the effectiveness of this approach through simulation experiments.
Comprehensive performance analysis of Spatio-Temporal Data Mining approach on multi-temporal coastal remote sensing datasets

The present study discusses about the new textural feature extraction, its improvement and a comprehensive analysis of our previous Machine Learning based Spatio-Temporal (STML-HAB) Data Mining approach for HAB detection mentioned in Ref. . This study is an elaborative analysis extending our first results presented in Ref. . The additional Wavelet and GLCM textural features helped in improving the performance up to an accuracy of 0.9259 ‘K’ using SeaWiFS sensor data. This is a significant improvement of almost 17% compared to our first results with an accuracy of (0.7513 ‘K’).
Nonlinear classification of multispectral imagery using representation-based classifiers

The paper investigates representation-based classification for multispectral imagery. Due to the limited spectral dimension, the performance may be limited, and, in general, it is difficult to discriminate different classes using multispectral imagery. Nonlinear band generation method is proposed to use which can provide additional spectral information for multispectral classification. Two classifiers, sparse representation-based classification (SRC) and Nearest Regularized Subspace (NRS) are evaluated on the generated datasets. The results show our approach can outperform other nonlinear method such as the traditional kernel method in terms of classification accuracy and computational cost.
ARMA spectral estimation based on non-linear least squares

An autoregressive moving average (ARMA) spectral estimation method based on the nonlinear least squares technique is presented. Simultaneous estimates of the AR and MA parameters are obtained by minimizing the prediction error using the Levenburg-Marqudit optimal search algorithm. If the model order is properly selected, it can be shown that this technique converges to the true model parameter values assuming that the model is stable and minimum phase. This technique is valid when applied to non-Gaussian white noise. Results for simulated data are presented to illustrate the application and accuracy of the technique.<<ETX>>
Semantics driven framework for coastal zones

For millennia, coastal zones of the world have been major centers of human population. There is a wealth of accumulated information about coastal zones, such as data/images in various databases, files, spreadsheets, video and audio data. However, optimal harnessing of these resources has long been recognized as an insurmountable task. The major challenges being the heterogeneous nature of the data due to the diverse procedures and techniques used to collect it, and stored in a variety of formats and at different locations. The Earth Observation (EO) satellites have been collecting huge amounts of data over the past decades (Landsat data alone comprises 434 terabytes of archive), however the current methods of searching for useful information is only at the syntactic metadata level, thus the optimal exploitation of the archived data is severely constrained by the lack of content and semantics based knowledge retrieval. In this paper, we present a semantics driven framework for content-based retrieval from remote sensing data and for integrating heterogeneous resources in coastal zones. We discuss the need for an ontology-enabled middleware to achieve such interoperability. A methodology for domain specific qualitative spatial reasoning in coastal wetlands is also presented.
Embedded high performance computing for on-board hyperspectral image classification

Jetson TK1 is a recently launched embedded application development platform from NVIDIA, which features the Tegra K1 processor and Kepler Graphics Processing Unit (GPU). We envisage that such a system has huge potential for deploying an embedded system for on-board classification of hyperspectral images. We used a convolutional deep neural network for designing a unified model for hyperspectral image classification. Deep convolutional model hierarchically extracts spectral-spatial features from hyperspectral imagery and these features are used by the fully connected layer of neural network to perform pixel level classification of hyperspectral imagery. Our experimental results show that Jetson TK1 based hyperspectral image classification gives promising results and the possibility of having Jetson based embedded platform for on-board classification of hyperspectral images.
Precipitation data merging using general linear regression

Precipitation is a key component of the water and energy cycles of the earth's climate system. Today, estimates of global precipitation are derived routinely from satellite observations and numerical weather prediction (NWP) models. Future satellite constellations from the Global Precipitation Measurement (GPM) mission will continue to provide high resolution precipitation products (HRPP) improved spatial and temporal resolutions. We have investigated a data fusion methodology, based on linear regression and an error propagation model, to merge different precipitation datasets in order to develop a fused product which is statistically superior to any individual data set or their average.
Levee anomaly detection using polarimetric synthetic aperture radar data

This research presents results of applying the NASA JPL's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) quad-polarized L-band data to detect anomalies on earthen levees. Two types of problems / anomalies that occur along these levees which can be precursors to complete failure during a high water event are slough slides and sand boils. The study area encompasses a portion of levees of the lower Mississippi river in the United States. Supervised and unsupervised classification techniques have been employed to detect slough slides along the levee. RX detector, a training-free classification scheme is introduced to detect anomalies on the levee and the results are compared with the k-means clustering algorithm. Using the available ground truth data, a supervised kernel based classification technique using a Support Vector Machine (SVM) is applied for binary classification of slides on the levee versus the healthy levee and the performance is compared with a neural network classifier.
Ensemble Methodology Using Multistage Learning for Improved Detection of Harmful Algal Blooms

The available empirical remote sensing techniques for harmful algal bloom (HAB) detection are reliant on prior observations and thresholds. These techniques tend to give high false alarm rate, as they are limited in spatiotemporal contextual information and decision combination techniques. We propose a multistage learning based ensemble methodology addressing the above constraints for performance improvement of HAB detection. Machine learning-based spatiotemporal data mining approach, along with empirical relationships, is used for HAB detection in the first stage of the ensemble, to exploit the potential benefits of each individual detection technique. The decision outputs from these detection techniques are fused in the second stage using nonlinear modeling-based combination techniques unlike conventional weighted averages. The proposed ensemble methodology outperforms all of the individual members and gave a significant overall performance improvement up to 0.8632 kappa accuracy. The performance is evaluated over tenfold cross validation average and compared against various ensemble methods and combination techniques.
Supervised classification using polarimetric SAR decomposition parameters to detect anomalies on earthen levees

Earthen levees protect large areas of populated and cultivated land from flooding. The potential loss of life and property associated with the catastrophic failure of levees can be great. One type of problem that occurs along these levees which can lead to complete failure during a high water event is slough slides . Using Entropy (H), Anisotropy (A), and alpha (α) parameters, we implemented Wishart supervised classification method for the identification of anomalies on the levee. The effectiveness of the algorithms is demonstrated using quad-polarimetric L-band polarimetric Synthetic Aperture Radar (polSAR) imagery from the NASA Jet Propulsion Laboratory's (JPL's) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA.
A perturbation analysis of the regularized constrained total least squares

A perturbation analysis of the regularized constrained total least squares technique (RCTLS) is performed to ascertain the statistical characteristics of its solution in signal restoration. With an appropriate choice of a regularization parameter, it is shown that the RCTLS method reduces the mean square error (MSE) and variance estimates. Also, a MSE comparison with traditional techniques for different signal-to-noise ratios (SNR) is made to verify its applicability.
Binary fuzzy measures and Choquet integration for multi-source fusion

Countless challenges in engineering require the intelligent combining (aka fusion) of data or information from multiple sources. The Choquet integral (ChI), a parametric aggregation function, is a well-known tool for multi-source fusion, where source refers to sensors, humans and/or algorithms. In particular, a selling point of the ChI is its ability to model and subsequently exploit rich interactions between inputs. For a task with N inputs, the ChI has 2N interaction variables. Therefore, the ChI becomes intractable quickly in terms of storage and its data-driven learning. Herein, we study the properties of an efficient to store, compute, and ultimately optimize version of the ChI based on a binary fuzzy measure (BFM). The BFM is further motivated by empirical observations in the areas of multi-sensor fusion and hyperspectral image processing. Herein, we provide a deeper understanding of the inner workings, capabilities and underlying philosophy of a BFM ChI (BChI). We also prove that two fuzzy integrals, the ChI and the Sugeno integral, are equivalent for a BFM. Furthermore, only a small subset of BFM variables need be stored, which reduces the BChI to a relatively simple look up operation.
Enhancement of Satellite Precipitation Estimation via Unsupervised Dimensionality Reduction

A methodology to enhance satellite precipitation estimation using unsupervised dimensionality reduction (UDR) techniques is developed. This enhanced technique is an extension to the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched using wavelet features combined with dimensionality reduction. Cloud-top brightness temperature measurements from the Geostationary Operational Environmental Satellite (GOES)-12 are used for precipitation estimation at 4 km × 4 km spatial resolutions every 30 min. The study area in the continental U.S. covers parts of Louisiana, Arkansas, Kansas, Tennessee, Mississippi, and Alabama. Based on quantitative measures, root mean square error and Heidke skill score (HSS), the results show that the UDR techniques can improve the precipitation estimation accuracy. In addition, the independent component analysis is shown to have better performance than other UDR techniques; and in some cases, it achieves 10% improvement in the HSS.