ANALYTICAL SOLUTION OF GLOBAL 2D DESCRIPTION OF SHIP GEOMETRY WITH DISCONTINUITIES USING COMPOSITION OF POLYNOMIAL RADIAL BASIS FUNCTIONS

One of the well-known problems in the curves and surfaces reconstruction theory regarding global analytic object description, besides the description of its curvature changes, inflexions and non-bijective parts, is the existence of oscillations near point discontinuities in the middle of the range and at the boundaries of the description. In the ship geometric modelling, ship hull form is usually described globally using parametric methods based on B-spline and NURB-spline, for they have general property of describing discontinuities. Nevertheless, they are not enabling direct, exact calculation of ship\u27s geometric properties, i.e. the calculation of the integrals for determining geometric and other geometry properties or the intersection with water surface. Because of above mentioned, the predominant way of computing geometric properties of the ships is still numerical computation using Simpson integration methods, which also dictates mesh based description of an observed geometry. Analytical solution of global 2D description for ship geometry with discontinuities will be shown in this paper, using the composition of polynomial RBFs, thus solving computational geometry problems, too

High-Resolution Satellite Imagery Classification for Urban Form Detection

Mapping urban form at regional and local scales is a crucial task for discerning the influence of urban expansion upon the ecosystem and the surrounding environment. Remotely sensed imagery is ideally used to monitor and detect urban areas that occur frequently as a consequence of incessant urbanization. It is a lengthy process to convert satellite imagery into urban form map using the existing methods of manual interpretation and parametric image classification digitally. In this work, classification techniques of high-resolution satellite imagery were used to map 50 selected cities of study of the National Urban System in Mexico, during 2015–2016. In order to process the information, 140 RapidEye Ortho Tile multispectral satellite imageries with a pixel size of 5 m were downloaded, divided into 5 × 5 km tiles and then 639 tiles were generated. In each (imagery or tile), classification methods were tested, such as: artificial neural networks (RNA), support vector machines (MSV), decision trees (AD), and maximum likelihood (MV); after tests, urban and nonurban categories were obtained. The result is validated with an accuracy method that follows a stratified random sampling of 16 points for each tile. It is expected that these results can be used in the construction of spatial metrics that explain the differences in the Mexican urban areas

Enhancing temporal series of Sentinel-2 and Sentinel-3 data products: from classical regression to deep learning approach

Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2020-2021The free and open availability of satellite images covering global extent in recent days provides many novel opportunities for global monitoring of the earth’s surface. Sentinel-2 (S2) and Sentinel-3 (S3) satellite missions capture mid to high resolution imagery with frequent revisit and show data synergy as they both focus on land and ocean observational needs. Specifically, the high temporal resolution of S3 (1-2 day revisit) presents potential in filling the data gaps in S2 (5 day revisit) vegetation products. In this scenario, this study assesses the feasibility of using Sentinel-3 images for Sentinel-2 vegetation products estimation using machine learning (ML) and deep learning (DL) approaches. This study employs four state of the art ML regression algorithms, linear regression, ridge regression, Support Vector Regression (SVR) and Random Forest Regression (RFR) and two DL network architectures with different depth and complexities, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) to predict the S2 NDVI and SAVI maps from the S3 spectral bands information. A paired S2/S3 dataset is prepared for the study area covering one S2 tile in Extremadura, Spain. The results demonstrate that all the DL architectures except pixel-wise MLP outperformed the ML models with the 3D CNN performing the best. The best performing 3D CNN architecture obtained remarkable mean squared error (MSE) of 0.00198 for NDVI and 0.00282 for SAVI while the best performing ML algorithms were patch-wise RFR with MSE of 0.0035 in case of NDVI and patchwise SVR with MSE of 0.00586 for SAVI. The models and the dataset prepared for this study will be useful for further research that focus on capitalizing the free and open availability of Sentinel-2 and Sentinel-3 imagery as well as new and advanced technologies to provide better vegetation monitoring capabilities for our planet

Dimension-reduction and discrimination of neuronal multi-channel signals

Dimensionsreduktion und Trennung neuronaler Multikanal-Signale

Geometrically Enriched Latent Spaces

A common assumption in generative models is that the generator immerses the latent space into a Euclidean ambient space. Instead, we consider the ambient space to be a Riemannian manifold, which allows for encoding domain knowledge through the associated Riemannian metric. Shortest paths can then be defined accordingly in the latent space to both follow the learned manifold and respect the ambient geometry. Through careful design of the ambient metric we can ensure that shortest paths are well-behaved even for deterministic generators that otherwise would exhibit a misleading bias. Experimentally we show that our approach improves interpretability of learned representations both using stochastic and deterministic generators

Nonlinear Parametric and Neural Network Modelling for Medical Image Classification

System identification and artificial neural networks (ANN) are families of algorithms used in systems engineering and machine learning respectively that use structure detection and learning strategies to build models of complex systems by taking advantage of input-output type data. These models play an essential role in science and engineering because they fill the gap in those cases where we know the input-output behaviour of a system, but there is not a mathematical model to understand and predict its changes in future or even prevent threats. In this context, the nonlinear approximation of systems is nowadays very popular since it better describes complex instances. On the other hand, digital image processing is an area of systems engineering that is expanding the analysis dimension level in a variety of real-life problems while it is becoming more attractive and affordable over time. Medicine has made the most of it by supporting important human decision-making processes through computer-aided diagnosis (CAD) systems. This thesis presents three different frameworks for breast cancer detection, with approaches ranging from nonlinear system identification, nonlinear system identification coupled with simple neural networks, to multilayer neural networks. In particular, the nonlinear system identification approaches termed the Nonlinear AutoRegressive with eXogenous inputs (NARX) model and the MultiScales Radial Basis Function (MSRBF) neural networks appear for the first time in image processing. Along with the above contributions takes place the presentation of the Multilayer-Fuzzy Extreme Learning Machine (ML-FELM) neural network for faster training and more accurate image classification. A central research aim is to take advantage of nonlinear system identification and multilayer neural networks to enhance the feature extraction process, while the classification in CAD systems is bolstered. In the case of multilayer neural networks, the extraction is carried throughout stacked autoencoders, a bottleneck network architecture that promotes a data transformation between layers. In the case of nonlinear system identification, the goal is to add flexible models capable of capturing distinctive features from digital images that might be shortly recognised by simpler approaches. The purpose of detecting nonlinearities in digital images is complementary to that of linear models since the goal is to extract features in greater depth, in which both linear and nonlinear elements can be captured. This aim is relevant because, accordingly to previous work cited in the first chapter, not all spatial relationships existing in digital images can be explained appropriately with linear dependencies. Experimental results show that the methodologies based on system identification produced reliable images models with customised mathematical structure. The models came to include nonlinearities in different proportions, depending upon the case under examination. The information about nonlinearity and model structure was used as part of the whole image model. It was found that, in some instances, the models from different clinical classes in the breast cancer detection problem presented a particular structure. For example, NARX models of the malignant class showed higher non-linearity percentage and depended more on exogenous inputs compared to other classes. Regarding classification performance, comparisons of the three new CAD systems with existing methods had variable results. As for the NARX model, its performance was superior in three cases but was overcame in two. However, the comparison must be taken with caution since different databases were used. The MSRBF model was better in 5 out of 6 cases and had superior specificity in all instances, overcoming in 3.5% the closest model in this line. The ML-FELM model was the best in 6 out of 6 cases, although it was defeated in accuracy by 0.6% in one case and specificity in 0.22% in another one

Towards text-based prediction of phrasal prominence

The objective of this thesis was text-based prediction of phrasal prominence. Improving natural sounding speech synthesis motivated the task, because phrasal prominence, which depicts the relative saliency of words within a phrase, is a natural part of spoken language. Following the majority of previous research, prominence is predicted on binary level derived from a symbolic representation of pitch movements. In practice, new classifiers and new models from different fields of natural language processing were explored. Applicability of spatial and graph-based language models was tested by proposing such features as word vectors, a high-dimensional vector-space representation, and DegExt, a keyword weighting method. Support vector machines (SVMs) were used due to their widespread suitability to supervised classification tasks with high-dimensional continuous-valued input. Linear inner product and non-linear radial basis function (RBF) were used as kernels. Furthermore, hidden Markov support vector machines (HM-SVMs) were evaluated to investigate benefits of sequential classification. The experiments on the widely used Boston University Radio News Corpus (BURNC) were successful in two major ways: Firstly, the non-linear support vector machine along with the best performing features achieved similar performance than the previous state-of-the-art approach reported by Rangarajan et al. [RNB06]. Secondly, newly proposed features based on word vectors moderately outperformed part-of-speech tags, which has been inevitably the best performing feature throughout the research of text-based prominence prediction