nils-herrmann/clean_scirepeval huggingface dataset:
Multi-sensor change detection based on nonlinear canonical correlations

The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of such datasets. However, for change detection existing methods are often unable to deal with heterogeneous image sources and very few consider possible nonlinearities in the data. Additionally, the availability of labeled information is very limited in change detection applications. For these reasons, we present the use of a semi-supervised kernel-based feature extraction technique. It incorporates a manifold regularization accounting for the geometric distribution and jointly addressing the small sample problem. An exhaustive example using Landsat 5 data illustrates the potential of the method for multi-sensor change detection.
Support Vector Machines for Nonlinear Kernel ARMA System Identification

Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA2K) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based system identification nonlinear models is presented, based on the use of composite Mercer's kernels. This general class can improve model flexibility by emphasizing the input-output cross information (SVM-ARMA4K), which leads to straightforward and natural combinations of implicit and explicit ARMA models (SVR-ARMA2K and SVR-ARMA4K). Capabilities of these different SVM-based system identification schemes are illustrated with two benchmark problems
Semi-Supervised Support Vector Biophysical Parameter Estimation

Two kernel-based methods for semi-supervised regression are presented. The methods rely on building a graph or hypergraph Laplacian with both the labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). The semi-supervised SVR methods are sucessfully tested in LAI estimation and ocean chlorophyll concentration prediction from remotely sensed images.
Retrieval of Vegetation Biophysical Parameters Using Gaussian Process Techniques

This paper evaluates state-of-the-art parametric and nonparametric approaches for the estimation of leaf chlorophyll content (Chl), leaf area index, and fractional vegetation cover from space. The parametric approach involves comparison of established and generic narrowband vegetation indices (VIs) and the Normalized Area Over reflectance Curve method, which calculates the continuum spectral region sensitive to Chl. However, as not all available bands take part in these spectral algorithms, it remains unclear whether optimal estimations are achieved. Alternatively, the nonparametric approach is based on Gaussian process (GP) techniques and allows inclusion of all bands. GP builds a nonlinear regression as a linear combination of spectra mapped to a high-dimensional space. Moreover, GP provides an indication of the most contributing bands for each parameter, a weight for the most relevant spectra contained in the training data set, and a confidence estimate of the retrieval. GP has previously demonstrated to be competitive in accuracy with support vector regression and neural networks.  GP identified most contributing bands in the red and red edge and, to a lower extent, in the blue and NIR parts of the spectrum. Since the proposed GP method is able to build robust relationships between the parameter of interest and only a few bands, it is a promising approach for multispectral data as well.
Feature extraction from remote sensing data using Kernel Orthonormalized PLS

This paper presents the study of a sparse kernel-based method for non-linear feature extraction in the context of remote sensing classification and regression problems. The so-called kernel orthonormalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse (reduced) approximation for large scale data sets, which ultimately leads to rKOPLS. The method demonstrates good capabilities in terms of expressive power of the extracted features and scalability.
Multitemporal Unmixing of Medium-Spatial-Resolution Satellite Images: A Case Study Using MERIS Images for Land-Cover Mapping

Data from current medium-spatial-resolution imaging spectroradiometers are used for land-cover mapping and land-cover change detection at regional to global scales. However, few landscapes are homogeneous at these scales, and this creates the so-called mixed-pixel problem. In this context, this study explores the use of the linear spectral mixture model to extract subpixel land-cover composition from medium-spatial-resolution data. In particular, a time series of MEdium Resolution Imaging Spectrometer (MERIS) full-resolution (FR; pixel size of 300 m) images acquired over The Netherlands is used to illustrate this study. The Netherlands was selected because of the following: 1) the fragmentation of its landscapes and 2) the availability of a high-spatial-resolution land-cover data set (LGN5) which can be used as a reference. The question then is to what extent a multitemporal unmixing of MERIS FR data delivers land-cover information comparable with the one provided by the LGN5. To this end, fully constrained linear spectral unmixing is applied to each individual MERIS image and to the multitemporal composite. The unmixing results are validated at both subpixel and per-pixel scales and at two thematic aggregation levels (12 and 4 land-cover classes). The obtained results indicate that the described unmixing approach yields moderate results for the 12-class case and good results for the 4-class case. These results might be explained by MERIS preprocessing steps, gridding effects, vegetation phenophases, and spectral class separability.
Semi-supervised Hyperspectral Image Classification with Graphs

This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to exploit the spatial/contextual information in the im- ages through composite kernels. The proposed method produces smoother classifications with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. Good accuracy in high dimensional spaces and low number of labeled samples (ill-posed situations) are produced as compared to standard inductive support vector machines.
Unsupervised Change Detection With Kernels

In this letter, an unsupervised kernel-based approach to change detection is introduced. Nonlinear clustering is utilized to partition in two a selected subset of pixels representing both changed and unchanged areas. Once the optimal clustering is obtained, the learned representatives of each group are exploited to assign all the pixels composing the multitemporal scenes to the two classes of interest. Two approaches based on different assumptions of the difference image are proposed. The first accounts for the difference image in the original space, while the second defines a mapping describing the difference image directly in feature spaces. To optimize the parameters of the kernels, a novel unsupervised cost function is proposed. An evidence of the correctness, stability, and superiority of the proposed solution is provided through the analysis of two challenging change-detection problems.
Cloud screening with combined MERIS and AATSR images

This paper presents a cloud screening algorithm based on ensemble methods that exploits the combined information from both MERIS and AATSR instruments on board ENVISAT in order to improve current cloud masking products for both sensors. The first step is to analyze the synergistic use of MERIS and AATSR images in order to extract some physically-based features increasing the separability of clouds and surface. Then, several artificial neural networks are trained using different sets of input features and different sets of training samples depending on acquisition and surface conditions. Finally, outputs of the trained neural networks are combined at the decision level to construct a more accurate and robust ensemble of classifiers. The proposed classifier is tested on more than 80 coregistered MERIS/AATSR images providing better classification accuracy than the official cloud flags and available operational cloud screening algorithms for MERIS and AATSR. Moreover, thanks to the synergy of both sensors, it correctly classifies critical cloud-screening problems such as snow and ice covers over land and sun-glint over ocean.
Semisupervised Image Classification With Laplacian Support Vector Machines

This letter presents a semisupervised method based on kernel machines and graph theory for remote sensing image classification. The support vector machine (SVM) is regularized with the unnormalized graph Laplacian, thus leading to the Laplacian SVM (LapSVM). The method is tested in the challenging problems of urban monitoring and cloud screening, in which an adequate exploitation of the wealth of unlabeled samples is critical. Results obtained using different sensors, and with low number of training samples, demonstrate the potential of the proposed LapSVM for remote sensing image classification.
Semisupervised nonlinear feature extraction for image classification

Feature extraction is of paramount importance for an accurate classification of remote sensing images. Techniques based on data transformations are widely used in this context. However, linear feature extraction algorithms, such as the principal component analysis and partial least squares, can address this problem in a suboptimal way because the data relations are often nonlinear. Kernel methods may alleviate this problem only when the structure of the data manifold is properly captured. However, this is difficult to achieve when small-size training sets are available. In these cases, exploiting the information contained in unlabeled samples together with the available training data can significantly improve data description by defining an effective semisupervised nonlinear feature extraction strategy. We present a novel semisupervised Kernel Partial Least Squares (KPLS) algorithm for non-linear feature extraction. The method relies on combining two kernel functions: the standard RBF kernel using labeled information and a generative kernel directly learned by clustering the data. The effectiveness of the proposed method is successfully illustrated in multi- and hyper-spectral remote sensing image classification: accuracy improvements between +15 - 20% over standard PCA and +10% over advanced kernel PCA and KPLS for both images is obtained. Matlab code is available at http://isp.uv.es for the interested readers.
Recent advances in remote sensing image processing

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.
Urban Image Classification With Semisupervised Multiscale Cluster Kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. 's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.
Multitemporal fusion of Landsat and MERIS images

Monitoring Earth dynamics from current and future observation satellites is one of the most important objectives for the remote sensing community. In this regard, the exploitation of image time series from sensors with different characteristics provides an opportunity to increase the knowledge about environmental changes, which are needed in many operational applications, such as monitoring vegetation dynamics and land cover/use changes. Many studies in the literature have proven that high spatial resolution sensors like Landsat are very useful for monitoring land cover changes. However, the cloud cover probability of many areas and the 15-days temporal resolution restrict its use to monitor rapid variation phenomena. On the contrary, sensors with coarser spatial resolution like MERIS acquire images every 1-3 days. In this paper, Landsat/TM and ENVISAT/MERIS sensors are combined in a synergistic manner to enhance image time series at high spatial resolution using the temporal information provided by the MERIS sensor. The capabilities of the proposed methodology are illustrated using a temporal image series of both sensors acquired over Albacete (Spain) in 2004. Additionally, the temporal profile of the NDVI is selected as demonstrative application of agricultural monitoring.
Prelaunch assessment of worldview-3 information content

The upcoming WorldView-3 satellite is designed to collect unique data by combining very-high spatial resolution (VHR) with observation bands in the short wave infrared (SWIR) in addition to the visible and near-infrared (VNIR) multispectral and panchromatic bands currently available on the VHR WorldView-2 system. These SWIR bands were specifically selected in order to target unique reflectance and absorption features presented by various surface materials and should, therefore, significantly improve the platforms information content for many image mining applications. This presentation explores the information content available to the WorldView-3 platform in two ways. First, second-order statistics and mutual information estimates are utilized to measure the spectral content of simulated WorldView-3, WorldView-2, and QuickBird data relative to AVIRIS hyperspectral imagery. Then, WorldView-3 supervised classification performance is explored relative to that of hyperspectral imagery for both urban and agricultural data sets. Results suggest that the additional spectral content of the WorldView-3 platform provides a competitive information source to that of hyperspectral for broad applications in agricultural, mineral exploration, or urban monitoring.
A modified FCM with optimal Peano scans for image segmentation

This paper describes a new method for fuzzy segmentation based on spatial constraints. Taking into account the neighborhood influence two techniques are used. First a new feature is derived from Peano scans to represent a spatial relationship among neighbors. Second we incorporate a regularization term to fuzzy C-means algorithm (FCM). The algorithm is tested on both synthetic and multispectral images. Experimental results are presented and discussed. They show the effectiveness of the method.
Scalable rendering of variable density point cloud data

In this paper, we present a novel proxy based method of adaptive haptic rendering of a variable density 3D point cloud data at different levels of detail without pre-computing the mesh structure. We also incorporate features like rotation, translation and friction to provide a better realistic experience to the user. Instead of a point proxy, a spherical proxy of variable radius is used which avoids the sinking of proxy during the haptic interaction of sparse data. The radius of the proxy is adaptively varied depending upon the local density of the point data using kernel bandwidth estimation. During the interaction, the proxy moves in small steps tangentially over the point cloud such that the new position always minimizes the distance between the proxy and the haptic interaction point (HIP). The raw point cloud data re-sampled in a regular 3D lattice of voxels are loaded to the haptic space after proper smoothing to avoid aliasing effects. The rendering technique is experimented with several subjects and it is observed that this functionality supplements the user's experience by allowing the user to interact with an object at multiple resolutions.
Maximum likelihood estimation of blur from multiple observations

A limitation of the existing maximum likelihood (ML) based methods for blur identification is that the estimate of blur is poor when the blurring is severe. In this paper, we propose an ML-based method for blur identification from multiple observations of a scene. When the relations among the blurring functions of these observations are known, we show that the estimate of blur obtained by using the proposed method is very good. The improvement is particularly significant under severe blurring conditions. With an increase in the number of images, direct computation of the likelihood function, however, becomes difficult as it involves calculating the determinant and the inverse of the cross-correlation matrix. To tackle this problem, we propose an algorithm that computes the likelihood function recursively as more observations are added.
Stabilization of Parametric Active Contours Using a Tangential Redistribution Term

Depending on implementation, active contours have been classified as geometric or parametric active contours. Parametric contours, irrespective of representation, are known to suffer from the problem of irregular bunching and spacing out of curve points during the curve evolution. In a spline-based implementation of active contours, this leads to occasional formation of loops locally, and subsequently the curve blows up due to instabilities. In this paper, we analyze the reason for this problem and propose a solution to alleviate the same. We propose an ordinary differential equation (ODE) for controlling the curve parametrization during evolution by including a tangential force. We show that the solution of the proposed ODE is bounded. We demonstrate the effectiveness of the proposed method for segmentation and tracking tasks on closed as well as open contours.
A Matte-less, Variational Approach to Automatic Scene Compositing

In this paper, we consider the problem of compositing a scene from multiple images. Multiple images, for example, can be obtained by varying the exposure of the camera, by changing the object at focus, or by simply sampling a video sequence at arbitrary time instants. We develop this problem in an optimization framework and then adopt a variational approach to derive a generalized algorithm which will be able to solve diverse applications depending on the nature of the input images. Our approach has distinct advantages over the existing digital compositing techniques, such as alpha matting and alpha blending, which require an explicit preparation of the matte while there is no such requirement in the proposed technique. We demonstrate the usefulness of our approach through results from diverse applications in computer vision.
A Theoretical Analysis of Data Reduction Using the Weber Quantizer

We present a theoretical analysis of a perceptual coding approach, the so called Weber quantizer. Extensive studies performed by experimental psychologists and physiologists have unveiled one major conclusion: human perception often follows Weber's law. Ernst Weber was an experimental physiologist who in 1834 first discovered the following implication DeltaI = kl, where DeltaI is the so called difference threshold or the just noticeable difference (JND). It describes the smallest amount of change of an (arbitrary) stimulus I which can be detected just as often as it cannot be detected and defines the Weber bound at [(1 - k)I, (1 + k)I].
Body Movement Activity Recognition for Ambulatory Cardiac Monitoring

Wearable electrocardiogram (W-ECG) recorders are increasingly in use by people suffering from cardiac abnormalities who also choose to lead an active lifestyle. The challenge presently is that the ECG signal is influenced by motion artifacts induced by body movement activity (BMA) of the wearer. The usual practice is to develop effective filtering algorithms which will eliminate artifacts. Instead, our goal is to detect the motion artifacts and classify the type of BMA from the ECG signal itself. We have recorded the ECG signals during specified BMAs, e.g., sitting still, walking, movements of arms and climbing stairs, etc. with a single-lead system. The collected ECG signal during BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts and sensor noise. A particular class of BMA is characterized by applying eigen decomposition on the corresponding ECG data. The classification accuracies range from 70% to 98% for various class combinations of BMAs depending on their uniqueness based on this technique. The above classification is also useful for analysis of P and T waves in the presence of BMA
Image Denoising Using Edge Model-based Representation of Laplacian Subbands

This paper presents a novel method of removing unstructured, spurious artifacts (more popularly called noise) from images. This method uses an edge model-based representation of Laplacian subbands and deals with noise at Laplacian subband levels to reduce it effectively. As the prominent edges are retained in their original form in the denoised images, the proposed method can be classified as an edge preserving denoising scheme. Laplacian subbands are represented using a Primitive Set (PS) consisting of 7 x 7 subimages of sharp and blurred Laplacian edge elements. The choice of edge model-based representation provides greater flexibility in removing characteristic artifacts from noise sources.
A Context-Sensitive Clustering Technique Based on Graph-Cut Initialization and Expectation-Maximization Algorithm

This letter presents a multistage clustering technique for unsupervised classification that is based on the following: 1) a graph-cut procedure to produce initial segments that are made up of pixels with similar spatial and spectral properties; 2) a fuzzy c-means algorithm to group these segments into a fixed number of classes; 3) a proper implementation of the expectation-maximization (EM) algorithm to estimate the statistical parameters of classes on the basis of the initial seeds that are achieved at convergence by the fuzzy c-means algorithm; and 4) the Bayes rule for minimum error to perform the final classification on the basis of the distributions that are estimated with the EM algorithm. Experimental results confirm the effectiveness of the proposed technique.