Search results for "biophysical parameter retrieval"

showing 3 items of 3 documents

Optimized and automated estimation of vegetation properties: Opportunities for Sentinel-2

2014

La Biosfera es uno de los principales sistemas que conforman la Tierra. Su estudio permite comprender la relación entre la vegetación y el ciclo del carbono y cómo éste puede ser afectado por los cambios en los niveles de CO2 y los usos de suelo. Para el estudio de estas dinámicas a escala global y local, han sido desarrollados diversos modelos que son representaciones de la realidad en una escala y complejidad más simple. Parte de las variables de entrada de estos modelos son obtenidas mediante medidas de teledetección gracias al Global Climate Observing System (GCOS), que ha determinado un conjunto de 50 variables climáticas esenciales que contribuyen a los estudios de cambio climático qu…

:CIENCIAS TECNOLÓGICAS [UNESCO]:CIENCIAS TECNOLÓGICAS::Tecnología del espacio [UNESCO]leaf area indexUNESCO::CIENCIAS TECNOLÓGICAS::Tecnología del espacio:CIENCIAS DE LA TIERRA Y DEL ESPACIO::Otras especialidades de la tierra espacio o entorno [UNESCO]biophysical parameter retrievalradiative transfer models:CIENCIAS DE LA TIERRA Y DEL ESPACIO [UNESCO]leaf chlorophyll contentUNESCO::CIENCIAS TECNOLÓGICASLUT-based inversionempirical regression modelsmachine learningUNESCO::CIENCIAS DE LA TIERRA Y DEL ESPACIO::Otras especialidades de la tierra espacio o entornoSentinel-2UNESCO::CIENCIAS DE LA TIERRA Y DEL ESPACIO
researchProduct

Use of Guided Regularized Random Forest for Biophysical Parameter Retrieval

2018

This paper introduces a feature selection method based on random forest -the Guided Regularized Random Forest (GRRF)- which can be used in classification and regression tasks. The method is based on the regularization of the information gain in the random forest nodes to obtain a subset of relevant and non-redundant features. The proposed method is used as a preliminary step In the process of retrieving biophysical parameters from a hyperspectral image. Preliminary experiments show that we can reduce the RMSE of the retrievals by around 7% for the Leaf Area Index and around 8% for the fraction of vegetation cover when compared to the results using random forest features.

Mean squared error22/3 OA procedurebusiness.industryComputer scienceFeature extractionHyperspectral images0211 other engineering and technologiesHyperspectral imagingPattern recognitionFeature selection02 engineering and technologyBiophysical parameter retrievalRegularization (mathematics)RegressionRandom forestFeature selection0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligenceLeaf area indexbusinessRandom forest021101 geological & geomatics engineeringIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
researchProduct

On the semi-automatic retrieval of biophysical parameters based on spectral index optimization

2014

Abstract: Regression models based on spectral indices are typically empirical formulae enabling the mapping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one, until all band combinations and curve fitting functions are assessed. This paper describes the application of a Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package. ARTMO enables semi-automatic retrieval and mapping of biophysical parameters from optical remote sensing observations. The SI toolbox facilitates the assessment of biop…

Polynomialleaf area indexLogarithmbiophysical parameter retrievalEconomicsImaging spectrometerleaf chlorophyll contentempirical regression modelsCalibrationRadiative transferCurve fittingspectral indicesGeneral Earth and Planetary Scienceslcsh:Qlcsh:ScienceShortwaveGUI toolboxHyMapHyMapRemote sensingMathematics
researchProduct