Search results for "Remote sensing"
showing 10 items of 1262 documents
Kernels for Remote Sensing Image Classification
2015
Classification of images acquired by airborne and satellite sensors is a very challenging problem. These remotely sensed images usually acquire information from the scene at different wavelengths or spectral channels. The main problems involved are related to the high dimensionality of the data to be classified and the very few existing labeled samples, the diverse noise sources involved in the acquisition process, the intrinsic nonlinearity and non-Gaussianity of the data distribution in feature spaces, and the high computational cost involved to process big data cubes in near real time. The framework of statistical learning in general, and of kernel methods in particular, has gained popul…
Cloud-screening algorithm for ENVISAT/MERIS multispectral images
2007
This paper presents a methodology for cloud screening of multispectral images acquired with the Medium Resolution Imaging Spectrometer (MERIS) instrument on-board the Environmental Satellite (ENVISAT). The method yields both a discrete cloud mask and a cloud-abundance product from MERIS level-lb data on a per-pixel basis. The cloud-screening method relies on the extraction of meaningful physical features (e.g., brightness and whiteness), which are combined with atmospheric-absorption features at specific MERIS-band locations (oxygen and watervapor absorptions) to increase the cloud-detection accuracy. All these features are inputs to an unsupervised classification algorithm; the cloud-proba…
A Support Vector Domain Description Approach to Supervised Classification of Remote Sensing Images
2007
This paper addresses the problem of supervised classification of remote sensing images in the presence of incomplete (nonexhaustive) training sets. The problem is analyzed according to two different perspectives: 1) description and recognition of a specific land-cover class by using single-class classifiers and 2) solution of multiclass problems with single-class classification techniques. In this framework, we analyze different one-class classifiers and introduce in the remote sensing community the support vector domain description method (SVDD). The SVDD is a kernel-based method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. T…
Encoding Invariances in Remote Sensing Image Classification With SVM
2013
This letter introduces a simple method for including invariances in support-vector-machine (SVM) remote sensing image classification. We design explicit invariant SVMs to deal with the particular characteristics of remote sensing images. The problem of including data invariances can be viewed as a problem of encoding prior knowledge, which translates into incorporating informative support vectors (SVs) that better describe the classification problem. The proposed method essentially generates new (synthetic) SVs from the obtained by training a standard SVM with the available labeled samples. Then, original and transformed SVs are used for training the virtual SVM introduced in this letter. W…
Anthropogenic carbon stocks analysis in sparsely urbanized areas using remote sensing: a case study
2013
Anthropogenic carbon stocks in urbanized areas is a topic of growing importance at both local and regional scale nowadays, but its assessment is subjects to difficulties due to lack of data and spatial heterogeneity of the target. Remote sensing of urban areas has demonstrated its usefulness in assessing phenomena such as soil sealing and surface imperviousness, which are considered to be effective indicators of urbanization. This work presents a preliminary study of mid resolution satellite data capabilities in providing information about anthropogenic carbon stocks over the area of Emilia-Romagna region in Northern Italy. This has been done through a dual approach consisting of: (1) a dir…
State and parameter update in a coupled energy/hydrologic balance model using ensemble Kalman filtering
2012
Summary The capability to accurately monitor and describe daily evapotranspiration (ET) in a cost effective manner is generally attributed to hydrological models. However, continuous solution of energy and water balance provides precise estimations only when a detailed knowledge of sub-surface characteristics is available. On the other hand, residual surface energy balance models, based on remote observation of land surface temperature, are characterised by sufficient accuracy, but their applicability is limited by the lack of high frequency and high resolution thermal data. A compromise between these two methodologies is represented by the use of data assimilation scheme to include sparse …
The ACRIDICON-CHUVA campaign: Studying tropical deep convective clouds and precipitation over Amazonia using the new German research aircraft HALO
2016
Abstract Between 1 September and 4 October 2014, a combined airborne and ground-based measurement campaign was conducted to study tropical deep convective clouds over the Brazilian Amazon rain forest. The new German research aircraft, High Altitude and Long Range Research Aircraft (HALO), a modified Gulfstream G550, and extensive ground-based instrumentation were deployed in and near Manaus (State of Amazonas). The campaign was part of the German–Brazilian Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems–Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modeling and to the GPM (Global Precipitatio…
Derivation of high-resolution leaf area index maps in support of validation activities: Application to the cropland Barrax site
2009
The validation of coarse satellite-derived products from field measurements generally relies on up-scaling the field data to the corresponding satellite products. This question is commonly addressed through the generation of a reference high-resolution map of an area covering several moderate resolution pixels. This paper describes a method by which reference leaf area index (LAI) maps can be generated from ground-truth LAI measurements. The method is based on a multivariate ordinary least squares (OLS) algorithm which uses an iteratively re-weighted least squares (IRLS) algorithm. It provides an empirical relationship (i.e. a transfer function) between in situ measurements and concomitant …
Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products
2021
Abstract Spatial downscaling has recently become a crucial process in the regional application of coarse-resolution passive microwave surface soil moisture (SSM) products. Extensive gaps in auxiliary optical/thermal infrared observation data (mainly caused by cloud cover) and gaps in coarse-resolution passive microwave SSM data lead to spatiotemporal discontinuity in downscaled SSM maps, thereby limiting their applications. An improved downscaling method for the 25-km European Space Agency (ESA) Climate Change Initiative (CCI) SSM product was proposed to obtain daily seamless downscaled SSM series at a 1-km scale. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daily land su…
Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data
2015
Abstract The accurate quantification of carbon fluxes at continental spatial scale is important for future policy decisions in the context of global climate change. However, many elements contribute to the uncertainty of such estimate. In this study, the uncertainties of eight days gross primary production (GPP) predicted by Random Forest (RF) machine learning models were analysed at the site, ecosystem and European spatial scales. At the site level, the uncertainties caused by the missing of key drivers were evaluated. The most accurate predictions of eight days GPP were obtained when all available drivers were used (Pearson's correlation coefficient, ρ ~ 0.84; Root Mean Square Error (RMSE…