Search results for "Computer Science Applications"
showing 10 items of 3993 documents
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…
Estimating Missing Information by Cluster Analysis and Normalized Convolution
2018
International audience; Smart city deals with the improvement of their citizens' quality of life. Numerous ad-hoc sensors need to be deployed to know humans' activities as well as the conditions in which these actions take place. Even if these sensors are cheaper and cheaper, their installation and maintenance cost increases rapidly with their number. We propose a methodology to limit the number of sensors to deploy by using a standard clustering technique and the normalized convolution to estimate environmental information whereas sensors are actually missing. In spite of its simplicity, our methodology lets us provide accurate assesses.
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…
Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis
2015
In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to p…
Hyperspectral dimensionality reduction for biophysical variable statistical retrieval
2017
Abstract Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to ret…
Gaussian Processes Retrieval of LAI from Sentinel-2 Top-of-Atmosphere Radiance Data
2020
Abstract Retrieval of vegetation properties from satellite and airborne optical data usually takes place after atmospheric correction, yet it is also possible to develop retrieval algorithms directly from top-of-atmosphere (TOA) radiance data. One of the key vegetation variables that can be retrieved from at-sensor TOA radiance data is leaf area index (LAI) if algorithms account for variability in atmosphere. We demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybrid machine learning framework. To achieve this, the coupled leaf-canopy-atmosphere radiative transfer models PROSAIL-6SV were used to simulate a look-up table (LUT) of TOA radi…
Evaluation of the S-NPP VIIRS land surface temperature product using ground data acquired by an autonomous system at a rice paddy
2018
Abstract The S-NPP VIIRS Land Surface Temperature (LST) product attained the stage V1 of validation maturity (provisional validated) at the end of 2014. This paper evaluates the current VIIRS V1 LST product versus concurrent ground data acquired at a rice paddy site from December 2014 to August 2016. The experimental site has three different seasonal and homogeneous land covers through the year, which makes the site interesting for validation activities. An autonomous and multiangular system was used to record continuous ground data at the site. The data acquired at zenith angles similar to the VIIRS viewing angles were used for the validation to avoid possible differences between satellite…
Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review
2020
Abstract Green fractional vegetation cover ( f c ) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of f c via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a compre…
First in-situ measurements of plume chemistry at mount garet volcano, island of gaua (Vanuatu)
2020
Recent volcanic gas compilations have urged the need to expand in-situ plume measurements to poorly studied, remote volcanic regions. Despite being recognized as one of the main volcanic epicenters on the planet, the Vanuatu arc remains poorly characterized for its subaerial emissions and their chemical imprints. Here, we report on the first plume chemistry data for Mount Garet, on the island of Gaua, one of the few persistent volatile emitters along the Vanuatu arc. Data were collected with a multi-component gas analyzer system (multi-GAS) during a field campaign in December 2018. The average volcanic gas chemistry is characterized by mean molar CO2/SO2, H2O/SO2, H2S/SO2 and H2/SO2 ratios …
A Geometry-Based Underwater Acoustic Channel Model Allowing for Sloped Ocean Bottom Conditions
2017
This paper proposes a new geometry-based channel model for shallow-water ocean environments, in which the ocean bottom can slope gently down/up. The need for developing such an underwater acoustic (UWA) channel model is driven by the fact that the standard assumption of a flat ocean bottom does not hold in many realistic scenarios. Starting from a geometrical model, we develop a stochastic channel model for wideband single-input single-output vehicle-to-vehicle UWA channels using the ray theory assuming smooth ocean surface and bottom. We investigate the effect of the ocean-bottom slope angle on the distribution of the channel envelope, instantaneous channel capacity, temporal autocorrelati…