Search results for " lea"
showing 10 items of 6823 documents
Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods
2020
We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiol...
Elucidating the Influence of the Activation Energy on Reaction Rates by Simulations Based on a Simple Particle Model
2020
An application for visualizing the dynamic properties of an equimolar binary mixture of isotropic reactive particles is presented. By introducing a user selectable choice for the activation energy, the application is useful to demonstrate qualitatively that the reaction rate depends on the above choice and on temperature. The application is based on a 2D realistic dynamic model where atoms move because of their thermal energies and the trajectories are determined by solving numerically Newton’s laws according to a Molecular Dynamics (MD) scheme. Collisions are monitored as time progresses, and every time the collision energy is larger than the selected activation energy, a reactive event oc…
Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3
2012
Abstract ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT-5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. By using data from …
Recent Advances in Techniques for Hyperspectral Image Processing
2009
International audience; Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than thirty years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspec- tral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information. Performance of the discussed techniques is evaluated in …
Statistical retrieval of atmospheric profiles with deep convolutional neural networks
2019
Abstract Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retr…
Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V
2018
Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat −8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adap…
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
2020
Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…
Understanding deep learning in land use classification based on Sentinel-2 time series
2020
AbstractThe use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions. This shortcoming hampers a wider adoption of DL approaches by a wider users community, as model’s decisions are not accountable. In applications that involve the management of public budgets or policy compliance, a better interpretability of predictions is strictly required. This work aims …
Transferring deep learning models for cloud detection between Landsat-8 and Proba-V
2020
Abstract Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical proper…
Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data
2012
River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous…