nils-herrmann/clean_scirepeval huggingface dataset:
Regularized optimization for joint super-resolution and high dynamic range image reconstruction in a perceptually uniform domain

This paper discusses resolution enhancement of a set of images with varying exposure durations, having a high combined dynamic range. So far, little has been said in relation to the Human Visual System when it comes to Super-Resolution and High Dynamic Range fusion, unlike the case for traditional Super-Resolution where errors are measured with respect to human perception in the pixel domain. We propose a Super-Resolution method in the L*a*b* domain to bridge that gap and present some image reconstruction results.
Classification of power system events: voltage dips

The extensive monitoring programs that are run by power utilities enable new insights of the power system operation and new characterisation methods must be used for the classification and analysis of the recordings. This paper focuses on events that cause a temporary decrease in the fundamental frequency voltage magnitude (voltage dip). The analysis of the recordings from surveys in medium and low voltage networks shows that new classes of voltage dips should be introduced in order to characterise the events that are captured by the power quality monitors. The aim of the paper is to show the distinctive characteristics of each class and give the guidelines for the automatic processing of the recordings. Finally, a large number of recordings from a medium voltage network are classified using these characteristics and the results are presented.
A Bayesian framework-based end-to-end packet loss prediction in IP networks

Channel modelling in a network path is of major importance in designing delay sensitive applications. It is often not possible for these applications to retransmit packets due to delay constraints and they must therefore be resilient to packet losses. In this paper, we first establish an association between traffic delays and the queue size at a network gateway. A novel method for predicting packet losses is then proposed that is based on the correlation between the packet losses and the variations in the end-to-end time delay observed during transmission. We show that this makes it possible to predict packet losses before they occur. The transmission of multimedia streams can then be dynamically adjusted to account for the predicted losses. As a result, better error-resilience can be provided for multimedia streams transmitting through a dynamic network channel. This means that they can provide an improved quality of transmission under the same network budget constraint. Experiments have been performed and preliminary results have shown that the method can provide a much smoother and more reliable transmission of data.
Object Tracking using Incremental 2D-PCA Learning and ML Estimation

Video surveillance has drawn increasing interests in recent years. This paper addresses the issue of moving object tracking from videos. A two-step processing procedure is proposed: an incremental 2DPCA (two-dimensional principal component analysis)-based method for characterizing objects given the tracked regions, and a ML (maximum likelihood) blob-tracking process given the object characterization and the previous blob sequence. The proposed incremental 2DPCA updates the row- and column-projected covariance matrices recursively, and is computationally more efficient for online learning of dynamic objects. The proposed ML blob-tracking takes into account both the shape information and object characteristics. Tests and evaluations were performed on indoor and outdoor image sequences containing a range of single moving object in dynamic backgrounds, which have shown good tracking results. Comparisons with the method using the conventional PCA were also made.
Pedestrian Detection Using Augmented Training Data

Detecting pedestrians is a challenging and widely explored problem in computer vision. Many approaches rely on large quantities of manually labelled training data to learn a pedestrian classifier. To reduce the need for collecting and manually labelling real image training data, this paper investigates the possibility to use augmented images to train a pedestrian classifier. Augmented images are generated by rendering virtual pedestrians onto real image backgrounds. Classifiers learned from real or augmented training data are evaluated on real image test data from the widely used Daimler Mono Pedestrian benchmark data set. Results show that augmented training data generated from a single 200 frame image sequence reach 70 % average detection rate at one False Positives Per Image (FPPI), compared to 81 % for a classifier trained by a large-scale real data set. Results also show that complementing real training data with augmented data improves detection performance, compared to using real training data only.
Classification of bird species by using key song searching: a comparative study

To better understand the environment we are living, especially animals and birds around us, we need to study their behavior and the way of their communication. This paper addresses the problem of classifying bird species of interests using the digital signals of recorded bird songs. First, through comparisons of speech and bird song signals and the experiments, we propose a simple model (similar to that of the speech) for generating synthetic bird songs. We then propose a key-bird-song searching method for the recognition of bird species of interest. This is possible since bird songs appear to be simpler as compared to human speech. A hierarchical classification method is then suggested. In the coarse level of the classification, only candidate songs from those birds whose time-dependent coupled sound patterns are 'close' to that of the species of interest are chosen as the candidates. In the fine level, time-frequency 'format' trajectory-related features from the candidate songs are used for the classification. A case study is conducted for the recognition of a selected bird species, the Great Tit. Preliminary experimental results from using five different bird species and 87 songs have shown promising results in recognizing the selected bird species of interest, with less than 3% of classification errors.
Laboratory Tests and Web Based Surveillance to Determine the Ice- and Snow Performance of Insulators

To be able to determine, verify and monitor the ice-and snow performance of different insulation solutions, laboratory test methods and an on-site, on-line web based surveillance system are needed. The method for determining the ice performance in laboratory conditions, ice progressive stress (IPS) method, is described in this paper. Further it is described how to use the results of this type of test to estimate statistically the performance of complete overhead line equipped by different insulators. To verify the laboratory ice-and snow test method, to get an idea about the type and number of ice and snow events actually occurring in service and to get more information about ice and snow phenomena in real life an on-site on-line web based surveillance system was designed and built. This sophisticated system, including the automatic image analysis and used statistical tools is described in this paper. Besides the description of the laboratory test method and the surveillance system, service experience, pictures and interesting results obtained so far are also presented.
Fusion of methods for the classification of remote sensing images from urban areas

The fusion of methods for the classification of panchromatic high resolution satellite remote sensing images from urban areas is addressed. In this paper we propose to aggregate the results provided by different classifiers with complementary properties. Classical combination rules fail to properly combine conflictual information or information provided by sources with different reliabilities. To overcome those problems, the proposed approach is based on the definition of two measures of accuracy. Based on fuzzy set theory, both a local and a global accuracy are defined for each classifier. The fusion is then performed with an adaptive fuzzy combination operator. In terms of classification accuracy, the proposed method performs better than each classifier used separately. Results are presented on IKONOS images.
Foreword to the Special Issue on Data Fusion

DATA fusion emerged as a new topic in the late 1980s, but it was only by the first half of the following decade that the availability of remotely sensed data in digital form by different sources allowed the consideration of remote-sensing data fusion. At that point, the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society recognized the need for a Special Issue on the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING about "data fusion," which was published in May 1999. That pioneering issue brought to the attention of many researchers the need for an increased effort toward the joint exploitation of multiple data or information sources.
Support Vector Regression for the Estimation of Forest Stand Parameters Using Airborne Laser Scanning

Airborne laser scanning is nowadays widely used for the estimation of forest stand parameters. Prediction models have to deal with high-dimensional laser data sets as well as limited field calibration data. This problem is enhanced in mountainous areas where forest is highly heterogeneous and field data collection is costly. Artificial neural network models and support vector regression (SVR) have already demonstrated their ability to address such issues for species-specific plot volume prediction. In this letter, we compare the stand parameter prediction accuracies of support vector machines and ordinary least squares multiple-regression models for dominant height, basal area, mean diameter, and stem density. The sensitivity of these techniques to the input variables is investigated by testing data sets which include different numbers and types of laser metrics, and by reducing their dimension with principal component and independent component analyses. Whereas usual variables only reflect the vertical distribution, we also integrate the entropy of the horizontal distribution of the point cloud in the laser metrics. The results show that SVR prediction models are of similar accuracy with multiple-regression models but are more robust regarding the metrics included in the data sets. Preliminary dimension reduction of the data set by principal component analysis generally benefits more to SVR than to multiple regression. The optimal combination of laser metrics to be included in the data sets mainly depends on the forest parameter to be estimated.
Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis

In this letter, a technique based on independent component analysis (ICA) and extended morphological attribute profiles (EAPs) is presented for the classification of hyperspectral images. The ICA maps the data into a subspace in which the components are as independent as possible. APs, which are extracted by using several attributes, are applied to each image associated with an extracted independent component, leading to a set of extended EAPs. Two approaches are presented for including the computed profiles in the analysis. The features extracted by the morphological processing are then classified with an SVM. The experiments carried out on two hyperspectral images proved the effectiveness of the proposed technique.
Spectral–Spatial Classification of Hyperspectral Data Based on a Stochastic Minimum Spanning Forest Approach

In this paper, a new method for supervised hyperspectral data classification is proposed. In particular, the notion of stochastic minimum spanning forest (MSF) is introduced. For a given hyperspectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of MSFs. The next step consists in building an MSF from each of the M marker maps. Finally, all the M realizations are aggregated with a maximum vote decision rule in order to build the final classification map. The proposed method is tested on three different data sets of hyperspectral airborne images with different resolutions and contexts. The influences of the number of markers and of the number of realizations M on the results are investigated in experiments. The performance of the proposed method is compared to several classification techniques (both pixelwise and spectral-spatial) using standard quantitative criteria and visual qualitative evaluation.
Rotation-Based Support Vector Machine Ensemble in Classification of Hyperspectral Data With Limited Training Samples

With different principles, support vector machines (SVMs) and multiple classifier systems (MCSs) have shown excellent performances for classifying hyperspectral remote sensing images. In order to further improve the performance, we propose a novel ensemble approach, namely, rotation-based SVM (RoSVM), which combines SVMs and MCSs together. The basic idea of RoSVM is to generate diverse SVM classification results using random feature selection and data transformation, which can enhance both individual accuracy and diversity within the ensemble simultaneously. Two simple data transformation methods, i.e., principal component analysis and random projection, are introduced into RoSVM. An empirical study on three hyperspectral data sets demonstrates that the proposed RoSVM ensemble method outperforms the single SVM and random subspace SVM. The impacts of the parameters on the overall accuracy of RoSVM (different training sets, ensemble sizes, and numbers of features in the subset) are also investigated in this paper.
Population density estimation using textons

In this paper we propose an efficient method for population density estimation using textons and k nearest neighbor classifier (k-NN). Leung Malik (LM) filter bank is used for texture extraction (textons) from Google Earth Satellite Images and classification into high, medium, low population density and non-populated areas. We have tested the proposed method for 5 different images of cities of Pakistan at high resolution. Comparison of our results with those obtained using Grey Level Co-occurrence Matrix (GLCM) are also presented, indicating the effectiveness of the proposed method.
Stochastic Approach in Wet Snow Detection Using Multitemporal SAR Data

This letter introduces an alternative strategy for wet snow detection using multitemporal synthetic aperture radar (SAR) data. The proposed change detection method is primarily based on the comparison between two X-band SAR images acquired during the accumulation (winter) and melting (spring) seasons, in the French Alps. The new decision criterion relies on the local intensity statistics of the SAR images by considering the backscattering ratio as a stochastic process: the probability that “the intensity ratio fits into the predetermined range of values” is larger than a defined confidence level. Both the conducted snow backscattering simulations and the state-of-the-art measurements indicate more complex relation between the backscattering properties of the two snow types, with respect to the conventional assumption of the augmented electromagnetic absorption associated to the wet snow. Therefore, rather than adopting the standard hypothesis, we analyze the wet/dry snow backscattering ratio as a function of the local incidence angle (LIA). After employing the multilayer snow backscattering simulator, calibrated with scatterometer measurements in C-band, we modify, to some extent, the range of ratio values indicating the presence of the wet snow, by including positive ratio values for lower LIA. By simultaneously accounting for the speckle noise, the proposed stochastic approach derives the refined wet snow probability map. The performance analyses are carried out both through the comparison with the ground air temperature map and by comparing two copolarized channels processed separately.
Super-resolution: an efficient method to improve spatial resolution of hyperspectral images

Hyperspectral imaging is a continuously growing area of remote sensing application. The wide spectral range, providing a very high spectral resolution, allows to detect and classify surfaces and chemical elements of the observed image. The main problem of hyperspectral data is that the high spectral resolution is usually complementary to the spatial one, which can vary from a few to tens of meters. Many factors, such as imperfect imaging optics, atmospheric scattering, secondary illumination effects and sensor noise cause a degradation of the acquired image quality, making the spatial resolution one of the most expensive and hardest to improve in imaging systems. In this work, a novel method, based on the use of source separation technique and a spatial regularization step by simulated annealing is proposed to improve the spatial resolution of cover classification maps. Experiments have been carried out on both synthetic and real hyperspectral data and show the effectiveness of the proposed method.
Improving MODIS Spatial Resolution for Snow Mapping Using Wavelet Fusion and ARSIS Concept

We propose to fuse the high spatial content of two 250-m spectral bands of the moderate resolution imaging spectroradiometer (MODIS) into its five 500-m bands using wavelet-based multiresolution analysis. Our objective was to test the effectiveness of this technique to increase the accuracy of snow mapping in mountainous environments. To assess the performance of this approach, we took advantage of the simultaneity between the advanced spaceborne thermal emission and reflection radiometer (ASTER) and MODIS sensors. With a 15-m spatial resolution, the ASTER sensor provided reference snow maps, which were then compared to MODIS-derived snow maps. The benefit of the method was assessed through the investigation of various metrics, which showed an improvement from 3% to 20%. Therefore, the enhanced snow map is of great benefit for environmental and hydrological applications in steep terrain.
A new nonparametric method for testing stationarity based on trend analysis in the time marginal distribution

In this manuscript, we propose a novel nonparametric test for nonstationarities that are seen as a trend or an evolution in the local energy of the signal. The idea of the proposed technique consists in applying empirical mode decomposition for estimating and further quantifying the trend in the time marginal of the estimated time-frequency representation. Such methodology allows for the detection of slowly-varying nonstationarities of first and second-order.
Unsupervised classification of hyperspectral images by using linear unmixing algorithm

In this paper, we present an unsupervised classification algorithm for hyperspectral images. For reducing the dimension of hyperspectral data, we use a linear unmixing algorithm to extract the endmembers and their abundance maps. Compared to the components obtained by traditional PCA-basedmethod, the abundancemaps have physical meanings (such as the abundance of vegetation). For determining the number of endmembers contained in an image, we propose an eigenvalue based approach. The validation of this approach on synthetic data shows that this approach provides a robust estimation of the actual number of endmembers. Using the estimated abundance maps of the endmemebers, we perform a preliminary segmentation and use the mean values of the segmented regions as feature for the classification. We then perform Kmeans classifications on the segmented abundance maps with the number of clusters determined by the Krzanowski and Lai's method.
Polarimetric Incoherent Target Decomposition by Means of Independent Component Analysis

This paper presents an alternative approach for polarimetric incoherent target decomposition (ICTD) dedicated to the analysis of very high-resolution polarimetric synthetic aperture radar (POLSAR) images. Given the non-Gaussian nature of the heterogeneous POLSAR clutter due to the increase in spatial resolution, the conventional methods based on the eigenvector target decomposition can ensure uncorrelation of the derived backscattering components at most. By introducing the independent component analysis (ICA) in lieu of the eigenvector decomposition, our method is rather deriving statistically independent components. The adopted algorithm, i.e., FastICA, uses the non-Gaussianity of the components as the criterion for their independence. Considering the eigenvector decomposition as being analogs to the principal component analysis (PCA), we propose the generalization of the ICTD methods to the level of the blind source separation (BSS) techniques (comprising both PCA and ICA). The proposed method preserves the invariance properties of the conventional ones, appearing to be robust both with respect to the rotation around the line of sight and to the change of the polarization basis. The efficiency of the method is demonstrated comparatively using POLSAR RAMSES X-band and ALOS L-band data sets. The main differences with respect to the conventional methods are mostly found in the behavior of the second most dominant component, which is not necessarily orthogonal to the first one. The potential of retrieving nonorthogonal mechanisms is moreover demonstrated using synthetic data. On the expense of a negligible entropy increase, the proposed method is capable of retrieving the edge diffraction of an elementary trihedral by recognizing dipole as the second component.
On the use of morphological alternated sequential filters for the classification of remote sensing images from urban areas

The problem of classification of high-resolution remotely sensed images from urban areas is addressed. Previous studies have shown the interest in exploiting the local geometrical information of each pixel to improve the classification. This is performed using the derivative morphological profile obtained with a granulometric approach using respectively opening and closing operators. We propose to replace this by a morphological alternated sequential filter, where the openings and the closings are applied alternately. The results and the robustness provided by the ASF are presented on IKONOS panchromatic data.
Classification of hyperspectral images using automatic marker selection and Minimum Spanning Forest

A new method for segmentation and classification of hyperspectral images is proposed. The method is based on the construction of a Minimum Spanning Forest (MSF) from region markers. Markers are defined automatically from classification results. For this purpose, pixel-wise classification is performed and the most reliable classified pixels are chosen as markers. Furthermore, each marker defined from classification results is associated with a class label. Each tree in the MSF grown from a marker forms a region in the segmentation map. By assigning a class of each marker to all the pixels within the region grown from this marker, classification map is obtained. Furthermore, the classification map is refined, using results of a pixel-wise classification and a majority voting within the spatially connected regions. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana's Indian Pine site. The use of different dissimilarity measures for construction of the MSF is investigated. The proposed scheme improves classification accuracies, when compared to previously proposed classification techniques, and provides accurate segmentation and classification maps.