Weighted Mean Function Research Articles

Multistrategy ensemble regression for mapping of built-up density and height with Sentinel-2 data

In this paper, we establish a workflow for estimation of built-up density and height based on multispectral Sentinel-2 data. To do so, we render the estimation of built-up density and height as a supervised learning problem. Given the rational level of measurement of those two target variables, the regression estimation problem is regarded as finding the mapping between an incoming vector, i.e., ubiquitously available features computed from Sentinel-2 data, and an observable output (i.e., training set), which is derived over spatially limited areas in an automated manner. As such, training sets are automatically generated from a joint exploitation of TanDEM-X mission elevation data and Sentinel-2 imagery, and, as an alternative, from cadastral sources. The training sets are used to regress the target variables for spatial processing units which correspond to urban neighborhood scales. From a methodological point of view, we introduce a novel ensemble regression approach, i.e., multistrategy ensemble regression (MSER), based on advanced machine learning-based regression algorithms including Random Forest Regression, Support Vector Regression, Gaussian Process Regression, and Neural Network Regression. To establish a robust ensemble, those algorithms are learned with a modified version of the AdaBoost.RT algorithm. However, to reliably ensure diversity between single boosted regressors, we include a random feature subspace method in the procedure. In contrast to existing approaches, we selectively prune non-favorable regressors trained during the boosting procedure and calculate the final prediction by a weighted mean function on the residual models to ensure enhanced accuracy properties of predictions. Finally, outputs are concatenated into a single prediction with a decision fusion strategy. Experimental results are obtained from four test areas which cover the settlement areas of the four largest German cites, i.e., Berlin, Hamburg, Munich, and Cologne. The results unambiguously underline the beneficial properties of the MSER approach, since all best predictions were obtained with a boosted regressor in conjunction with a decision fusion strategy in a comparative setup. The mean absolute errors of corresponding models vary between 3 and 16% and 1–5.4 m with respect to built-up density and height, respectively, depending on the validation strategy, size of the spatial processing units, and test area. Also in a domain adaptation setup (i.e., when learning a model over a source domain and applying it over a geographically different target domain) numerous predictions show comparable accuracy levels as predictions obtained within a source domain. This further underlines the viability to transfer a model and, thus, enable a substitution of the training data in the target domains.

Gaussian process regression method for forecasting of mortality rates

Abstract Gaussian process regression (GPR) has long been shown to be a powerful and effective Bayesian nonparametric approach, and has been applied to a wide range of fields. In this paper we present a new application of Gaussian process regression methods for the modelling and forecasting of human mortality rates. The age-specific mortality rates are treated as time series and are modelled by four conventional Gaussian process regression models. Furthermore, to improve the forecasting accuracy we propose to use a weighted mean function and the spectral mixture covariance function in the GPR model. The numerical experiments show that the combination of the weighted mean function and the spectral mixture covariance function provides the best performance in forecasting long term mortality rates. The performance of the proposed method is also compared with three existing models in the mortality modelling literature, and the results demonstrate that the GPR model with the weighted mean function and the spectral mixture covariance function provides a more robust forecast performance.

Receiving Buffer Adaptation for High-Speed Data Transfer

New applications based on cloud computing, such as data synchronization for large chain departmental stores and bank transaction records, require very high-speed data transport. Although a number of high-bandwidth networks have been built, existing transport protocols or their variants over such networks cannot fully exploit the network bandwidth. Our experiments show that the fixed-size application level buffer employed in the receiver side is a major cause of this deficiency. A buffer that is either too small or too large impairs the transfer performance. Due to the varied natures of network conditions and of real-time packet processing (i.e., consuming) speed at the receiver, it is important to ensure that the buffer size is dynamically adjusted according to the perceived execution situation during runtime. In this paper, we propose Rada, a dynamic receiving buffer adaptation scheme for high-speed data transfer. Rada employs an exponential moving average aided scheme to quantify the data arrival rate and consumption rate in the buffer. Based on these two rates, we develop a linear aggressive increase conservative decrease scheme to adjust the buffer size dynamically. Moreover, a weighted mean function is employed to make the adjustment adaptive to the available memory in the receiver. Theoretical analysis is provided to demonstrate the rationale and parameter bounds of Rada. The performance of Rada is also theoretically compared with potential alternatives. We implement Rada in a Linux platform and extensively evaluate its performance in a variety of scenarios. Experimental results conform to the theoretical results, and show that Rada outperforms the static buffer scheme in terms of throughput, memory footprint, and fairness.

An experimental evaluation of non‐rigid registration techniques on Quickbird satellite imagery

In remote sensing, because of the wide diversity of image characteristics (size, spatial and radiometric resolution, terrain relief, observation poses, etc.), image registration methods that work well on certain satellite images may not produce acceptable results for others, requiring more powerful techniques. A variety of registration techniques that account for images with non‐rigid geometric deformations have been proposed, including piecewise (linear or cubic) functions, weighted mean functions, radial basis functions, B‐spline functions, etc. This paper compares three of them: polynomial, piecewise‐linear and thin‐plate‐spline functions, and analyses their performance under a variety of factors: off‐nadir viewing, terrain relief, density of control points, and 3D geometric correction. Our comparison applies on panchromatic QuickBird imagery, both ortho‐ready (as provided by DigitalGlobe) and orthorectified, acquired on different dates, from different observation attitudes, and sensing different land covers: urban area, high‐relief terrain, and a combination of both.

Piecewise Weighted Mean Functions and Histograms

Piecewise Weighted Mean Functions and Histograms

Graphic Applications of Some Interpolating Weighted Mean Functions

Graphic Applications of Some Interpolating Weighted Mean Functions