Search results for "Satellite image"

showing 10 items of 54 documents

Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Int…

2022

Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining…

VDP::Teknologi: 500General Earth and Planetary Sciencesmulti-temporal satellite imagesrelative radiometric normalization (RRN)change detectionimage fusionpseudo-invariant features (PIFs)Remote Sensing
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Retrieval of vegetation height in rice fields using polarimetric SAR interferometry with TanDEM-X data

2017

This work presents for the first time a demonstration with satellite data of polarimetric SAR interferometry (PolInSAR) applied to the retrieval of vegetation height in rice fields. Three series of dual-pol interferometric SAR data acquired with large baselines (2–3 km) by the TanDEM-X system during its science phase (April–September 2015) are exploited. A novel inversion algorithm especially suited for rice fields cultivated in flooded soil is proposed and evaluated. The validation is carried out over three test sites located in geographically different areas: Sevilla (SW Spain), Valencia (E Spain), and Ipsala (W Turkey), in which different rice types are present. Results are obtained duri…

Vegetation height010504 meteorology & atmospheric sciencesMean squared error0211 other engineering and technologiesSoil Science02 engineering and technology01 natural sciencesExternal referenceSatellite imageryComputers in Earth Sciences021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing2. Zero hungerVegetation heightGrowth cycleAgriculturePolSARGeologySynthetic aperture radar (SAR)Polarimetric sarInterferometryInterferometryTeoría de la Señal y ComunicacionesEnvironmental sciencePaddy fieldRiceTanDEM-XPolInSARRemote Sensing of Environment
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An Improved Forecasting Model from Satellite Imagery Based on Optimum Wavelet Bases and Adam Optimized LSTM Methods

2021

This paper proposes a new hybrid approach I-WT-LSTM (i.e., Improved Wavelet Long Short-Term Memory (LSTM) Model) for forecasting non-stationary time series (TS) from satellite imagery. The proposed approach consists of two steps: The first step aims at decomposing TS using Multi-Resolution Analysis wavelet (MRA-WT) into inter-and intra-annual components using 18 different mother wavelets (MW). Then, the energy to Shannon entropy ratio criterion is calculated to select the best MW. The second step is based on the LSTM model using Adam optimizer to predict the future. The proposed approach is tested using TS derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2001 t…

WaveletSeries (mathematics)Computer sciencebusiness.industrySatellite imageryPattern recognitionImage processingModerate-resolution imaging spectroradiometerArtificial intelligenceTime seriesHybrid approachbusinessEnergy (signal processing)
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Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series

2020

L'analyse prédictive permet d'estimer les tendances des évènements futurs. De nos jours, les algorithmes Deep Learning permettent de faire de bonnes prédictions. Cependant, pour chaque type de problème donné, il est nécessaire de choisir l'architecture optimale. Dans cet article, les modèles Stack-LSTM, CNN-LSTM et ConvLSTM sont appliqués à une série temporelle d'images radar sentinel-1, le but étant de prédire la prochaine occurrence dans une séquence. Les résultats expérimentaux évalués à l'aide des indicateurs de performance tels que le RMSE et le MAE, le temps de traitement et l'index de similarité SSIM, montrent que chacune des trois architectures peut produire de bons résultats en fon…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]FOS: Computer and information sciencesApprentissage profondComputer Science - Machine LearningImage and Video Processing (eess.IV)[INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]PrévisionComputer Science - Neural and Evolutionary ComputingDeep Learning AlgorithmsPrédiction[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]Electrical Engineering and Systems Science - Image and Video ProcessingLand cover change[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Machine Learning (cs.LG)SARIMA[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]FOS: Electrical engineering electronic engineering information engineeringSatellite imagesNeural and Evolutionary Computing (cs.NE)LSTMPredictionForecastingImages satellitaires
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The Mistigri Thermal Infrared Project: Scientific Objectives And Mission Specifications

2013

ISI Document Delivery No.: 147NI Times Cited: 4 Cited Reference Count: 117 Cited References: Abrams M, 2000, INT J REMOTE SENS, V21, P847, DOI 10.1080/014311600210326 Agam N, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008407 Allen RG, 2007, J IRRIG DRAIN E-ASCE, V133, P380, DOI 10.1061/(ASCE)0733-9437(2007)133:4(380) Alletto L, 2006, CHEMOSPHERE, V64, P1053, DOI 10.1016/j.chemosphere.2005.12.004 Arnfield AJ, 2003, INT J CLIMATOL, V23, P1, DOI 10.1002/joc.859 Baschek B., 2010, 2010 OC SCI M PORTL Bastiaanssen WGM, 1998, J HYDROL, V212, P198, DOI 10.1016/S0022-1694(98)00253-4 Bastiaanssen WGM, 2005, J IRRIG DRAIN E-ASCE, V131, P85, DOI 10.1061/(ASCE)0733-9437(2005)131:1(85) Beck LR, 2…

[SDV.SA]Life Sciences [q-bio]/Agricultural sciencesEngineeringDATA010504 meteorology & atmospheric sciencesLand surface temperature[SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph]media_common.quotation_subject[SDE.MCG]Environmental Sciences/Global Changes0211 other engineering and technologies[SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/AgronomyClimate change02 engineering and technologyURBAN01 natural sciencesLAND-SURFACE TEMPERATUREOriginalityAgency (sociology)CALIFORNIA CURRENT SYSTEMUrban heat islandArchitecture[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingmedia_common[SDV.EE]Life Sciences [q-bio]/Ecology environmentThermal infraredbusiness.industry[SDE.IE]Environmental Sciences/Environmental EngineeringSPECTRAL INDEXESREMOTE-SENSINGREFLECTION RADIOMETER ASTEREMISSIVITY SEPARATION ALGORITHM[SPI.ELEC]Engineering Sciences [physics]/ElectromagnetismAGRICULTURAL AREASystems engineering[SPI.OPTI]Engineering Sciences [physics]/Optics / PhotonicGeneral Earth and Planetary SciencesENERGY-BALANCE[SDV.EE.BIO]Life Sciences [q-bio]/Ecology environment/Bioclimatologybusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSATELLITE IMAGESHEAT-ISLAND
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Nonlinear Time-Series Adaptation for Land Cover Classification

2017

Automatic land cover classification from satellite image time series is of paramount relevance to assess vegetation and crop status, with important implications in agriculture, biofuels, and food. However, due to the high cost and human resources needed to characterize and classify land cover through field campaigns, a recurrent limiting factor is the lack of available labeled data. On top of this, the biophysical–geophysical variables exhibit particular temporal structures that need to be exploited. Land cover classification based on image time series is very complex because of the data manifold distortions through time. We propose the use of the kernel manifold alignment (KEMA) method for…

domain adaptationComputer science0211 other engineering and technologies02 engineering and technologyLand coverNormalized Difference Vegetation IndexVegetation coverkernel methods0202 electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringTime series021101 geological & geomatics engineeringRemote sensingManifold alignment[SHS.STAT]Humanities and Social Sciences/Methods and statisticsbusiness.industryVegetation15. Life on landGeotechnical Engineering and Engineering GeologyKernel methodKernel (image processing)Agriculturemanifold alignment020201 artificial intelligence & image processingSatellite Image Time SeriesLand cover classificationtime seriesScale (map)businessIEEE Geoscience and Remote Sensing Letters
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On the use of unmanned aerial systems for environmental monitoring

2018

[EN] Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that li…

environmental_sciencesINGENIERIA HIDRAULICA010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologies02 engineering and technology01 natural sciencesRiver monitoringBridge (nautical)Field (computer science)Vegetation indicesRiver monitoringEnvironmental monitoringEnvironmental impact assessmentSatellite imageryNatural disasterWater content2. Zero hungerMoistureAgricultural ecosystemsSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaEnvironmental monitoring04 agricultural and veterinary sciencesVegetationRemote sensingRemote sensing (archaeology)Vegetation indiceSystems engineeringUASEarth and Planetary Sciences (all)Context (language use)Leverage (statistics)EcosystemRemote sensing021101 geological & geomatics engineering0105 earth and related environmental sciencesData collectionPrecision agriculturebusiness.industryWater resources13. Climate actionAgricultureITC-ISI-JOURNAL-ARTICLESoil water040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental scienceGeneral Earth and Planetary SciencesPrecision agricultureSoil moisturebusinessITC-GOLDSettore ICAR/06 - Topografia E Cartografia
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Monitoring deforestation and forest degradation linking high-resolution satellite data and field data in the context of REDD+. A case of Tanzania

2017

El principal objetivo de este doctorado es apoyar el desarrollo de un sistema nacional de monitoreo forestal en Tanzania para informar sobre las emisiones actuales e históricas derivadas de la deforestación y la degradación forestal. El marco de la tesis se centra específicamente en el emergente contexto internacional de la iniciativa REDD + (Reducción de Emisiones por Deforestación y Degradación) de las Naciones Unidas, bajo la cual los países pueden obtener subsidios financieros para demostrar que están reduciendo sus emisiones de carbono de tierras forestales con respecto a su práctica histórica reciente. La investigación se centró en cinco áreas de investigación: La parte (1) revisa los…

forest degradationnational monitoring:CIENCIAS DE LA TIERRA Y DEL ESPACIO [UNESCO]field dataUNESCO::FÍSICATanzaniaforestremote sensinghigh spatial resolution:FÍSICA [UNESCO]tree coversatellite imagedeforestationforest biomasscarbon emissionsREDD+UNESCO::CIENCIAS DE LA TIERRA Y DEL ESPACIOsatellite datavegetation classification
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Frequency Based Detection and Monitoring of Small Scale Explosive Activity by Comparing Satellite and Ground Based Infrared Observations at Stromboli…

2014

Abstract Thermal activity is a common precursor to explosive volcanic activity. The ability to use these thermal precursors to monitor the volcano and obtain early warning about upcoming activity is beneficial for both human safety and infrastructure security. By using a very reliably active volcano, Stromboli Volcano in Italy, a method has been developed and tested to look at changes in the frequency of small scale explosive activity and how this activity changes prior to larger, ash producing explosive events. Thermal camera footage was used to designate parameters for typical explosions at Stromboli (size of spatter field, cooling rate, frequency of explosions) and this information was a…

geographygeography.geographical_feature_categoryExplosive materialInfrasoundInstrumentationAdvanced Spaceborne Thermal Emission and Reflection RadiometerGeophysicsVolcanoGeochemistry and PetrologySatelliteSatellite imageryModerate-resolution imaging spectroradiometerRemote Sensing Strombolian activitySeismologyGeologyRemote sensing
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Monitoring the evolution of coastline in Sicily by aerial photos and high resolution satellite imagery

2009

Italy is one of the country more exposed to coasts erosion: about 1200 km of coasts on about 8350 km are moved back of about 25 m in the last 50 years. Particularly Sicily have about 300 km of coasts moved back. There are various reasons of this phenomenon: natural factor (climatic or physical-oceanographic) with environmental and economic effects. The paper reviews the activity of the Dipartimento di Rappresentazione of University of Palermo in cooperation with the Regional Agency of Environmental Protection (ARPA) about the coastline evolution in Sicily. The research is carried out on selected physiographic units marked out by important erosive phenomena. The coastline trend are reconstru…

high resolution satellite imagery aerial photos QuickBird imagerySettore ICAR/06 - Topografia E Cartografia
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