Search results for "Gaussia"
showing 10 items of 653 documents
Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow.
2021
In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Establishe…
Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index
2017
This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Res…
Predicting year of plantation with hyperspectral and lidar data
2017
This paper introduces a methodology for predicting the year of plantation (YOP) from remote sensing data. The application has important implications in forestry management and inventorying. We exploit hyperspectral and LiDAR data in combination with state-of-the-art machine learning classifiers. In particular, we present a complete processing chain to extract spectral, textural and morphological features from both sensory data. Features are then combined and fed a Gaussian Process Classifier (GPC) trained to predict YOP in a forest area in North Carolina (US). The GPC algorithm provides accurate YOP estimates, reports spatially explicit maps and associated confidence maps, and provides sens…
Calibrating Expert Assessments Using Hierarchical Gaussian Process Models
2020
Expert assessments are routinely used to inform management and other decision making. However, often these assessments contain considerable biases and uncertainties for which reason they should be calibrated if possible. Moreover, coherently combining multiple expert assessments into one estimate poses a long-standing problem in statistics since modeling expert knowledge is often difficult. Here, we present a hierarchical Bayesian model for expert calibration in a task of estimating a continuous univariate parameter. The model allows experts' biases to vary as a function of the true value of the parameter and according to the expert's background. We follow the fully Bayesian approach (the s…
Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests
2021
We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees to the locations of seedlings. In the model, every point in x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The par…
Autonomous ultrasonic inspection using Bayesian optimisation and robust outlier analysis
2020
The use of robotics is beginning to play a key role in automating the data collection process in Non Destructive Testing (NDT). Increasing the use of automation quickly leads to the gathering of large quantities of data, which makes it inefficient, perhaps even infeasible, for a human to parse the information contained in them. This paper presents a solution to this problem by making the process of NDT data acquisition an autonomous one as opposed to an automatic one. In order to achieve this, the robotic data acquisition task is treated as an optimisation problem, where one seeks to find locations with the highest indication of damage. The resulting algorithm combines damage detection tech…
A new approach to simulate coating thickness in cold spray
2020
Abstract In the process of cold spray on complex components, the coating thickness is an important indicator to monitor and control. Current methods such as destructive tests or direct mechanical measurements can only be performed after spraying. Besides, these methods lead to production shutdown and additional costs . This article presents a novel approach predicting coating thickness for components with complex curved surfaces, especially in the case of shadow effects. Firstly, a three-dimensional geometric model of the coating profile based on Gaussian distribution was developed. In addition, the relative deposition efficiency (RDE) resulting from the different robot kinematic parameters…
Visual saliency detection in colour images based on density estimation
2017
International audience; A simple and effective method for visual saliency detection in colour images is presented. The method is based on the common observation that local salient regions exhibit distinct geometric and and texture patterns from neighbouring regions. We model the colour distribution of local image patches with a Gaussian density and measure the saliency of each patch as the statistical distance from that density. Experimental results with public datasets and comparison with other state-of-the-art methods show the effectiveness of our method.
A Fast Multiresolution Approach Useful for Retinal Image Segmentation
2018
Retinal diseases such as retinopathy of prematurity (ROP), diabetic and hypertensive retinopathy present several deformities of fundus oculi which can be analyzed both during screening and monitoring such as the increase of tortuosity, lesions of tissues, exudates and hemorrhages. In particular, one of the first morphological changes of vessel structures is the increase of tortuosity. The aim of this work is the enhancement and the detection of the principal characteristics in retinal image by exploiting a non-supervised and automated methodology. With respect to the well-known image analysis through Gabor or Gaussian filters, our approach uses a filter bank that resembles the “à trous” wav…
A clustering package for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Model.
2018
International audience; In this article, a new Python package for nucleotide sequences clustering is proposed. This package, freely available on-line, implements a Laplacian eigenmap embedding and a Gaussian Mixture Model for DNA clustering. It takes nucleotide sequences as input, and produces the optimal number of clusters along with a relevant visualization. Despite the fact that we did not optimise the computational speed, our method still performs reasonably well in practice. Our focus was mainly on data analytics and accuracy and as a result, our approach outperforms the state of the art, even in the case of divergent sequences. Furthermore, an a priori knowledge on the number of clust…