Krasnopolsky et al., 2003 - Google Patents
Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurementsKrasnopolsky et al., 2003
View HTML- Document ID
- 5894804008816052557
- Author
- Krasnopolsky V
- Schiller H
- Publication year
- Publication venue
- Neural Networks
External Links
Snippet
A broad class of neural network (NN) applications dealing with the remote measurements of geophysical (physical, chemical, and biological) parameters of the oceans, atmosphere, and land surface is presented. In order to infer these parameters from remote sensing (RS) …
- 238000005259 measurement 0 title abstract description 72
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. correcting range migration errors
- G01S13/9035—Particular SAR processing techniques not provided for elsewhere, e.g. squint mode, doppler beam-sharpening mode, spotlight mode, bistatic SAR, inverse SAR
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
- G01N2021/3155—Measuring in two spectral ranges, e.g. UV and visible
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/08—Adaptations of balloons, missiles, or aircraft for meteorological purposes; Radiosondes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Krasnopolsky et al. | Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements | |
| Yin et al. | Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI | |
| Tang et al. | Exploring deep neural networks to retrieve rain and snow in high latitudes using multisensor and reanalysis data | |
| Maturi et al. | A new high-resolution sea surface temperature blended analysis | |
| Yang et al. | AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system | |
| Embury et al. | Satellite-based time-series of sea-surface temperature since 1980 for climate applications | |
| Giangrande et al. | An application of linear programming to polarimetric radar differential phase processing | |
| Håkansson et al. | Neural network cloud top pressure and height for MODIS | |
| Wei et al. | A comparative assessment of multisensor data merging and fusion algorithms for high-resolution surface reflectance data | |
| Zhu et al. | Fusion of multisensor SSTs based on the spatiotemporal hierarchical Bayesian model | |
| Singh et al. | PIML-SM: Physics-informed machine learning to estimate surface soil moisture from multi-sensor satellite images by leveraging swarm intelligence | |
| Mile et al. | Supermodding–A special footprint operator for mesoscale data assimilation using scatterometer winds | |
| Lamer et al. | 2-SIM: A GCM-oriented ground-observation forward-simulator framework for objective evaluation of cloud and precipitation phase | |
| Zhang et al. | Soil moisture estimation based on the distributed scatterers adaptive filter over the QTP permafrost region using sentinel-1 and high-resolution TerraSAR-X data | |
| Kaleschke et al. | SMOS-derived Antarctic thin sea ice thickness: data description and validation in the Weddell Sea | |
| Rignot et al. | Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data | |
| Boukabara et al. | Global coverage of total precipitable water using a microwave variational algorithm | |
| Suggs et al. | Retrieval of geophysical parameters from GOES: Evaluation of a split-window technique | |
| Grecu et al. | Precipitation retrievals from satellite combined radar and radiometer observations | |
| Meng et al. | Cloud-dependent piecewise assimilation based on a hydrometeor-included background error covariance and its impact on regional Numerical Weather Prediction | |
| Afzali Gorooh et al. | Integrating LEO and GEO observations: Toward optimal summertime satellite precipitation retrieval | |
| Sakaida et al. | Sea surface temperature observation by Global Imager (GLI)/ADEOS-II: Algorithm and accuracy of the product | |
| Ricciardelli et al. | A Feedforward Neural Network approach for the detection of optically thin cirrus from IASI-NG | |
| Armstrong et al. | Spatial variability of mean daily estimates of actual evaporation from remotely sensed imagery and surface reference data | |
| Preusker et al. | Cloud‐top pressure retrieval using the oxygen A‐band in the IRS‐3 MOS instrument |