Steward et al., 2022 - Google Patents
Modeling EO/IR systems with ASSET: applied machine learning for synthetic WFOV background signature generationSteward et al., 2022
- Document ID
- 11125074080757022753
- Author
- Steward B
- Wagner B
- Hopkinson K
- Young S
- Publication year
- Publication venue
- Electro-optical and Infrared Systems: Technology and Applications XIX
External Links
Snippet
The AFIT Sensor and Scene Emulation Tool (ASSET) is a physics-based model used to generate synthetic data sets of wide field-of-view (WFOV) electro-optical and infrared (EO/IR) sensors with realistic radiometric properties, noise characteristics, and sensor …
- 238000010801 machine learning 0 title description 10
Classifications
-
- 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
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colour
- G01J3/28—Investigating the spectrum
- G01J3/2823—Imaging spectrometer
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
- G06T3/4061—Super resolution, i.e. output image resolution higher than sensor resolution by injecting details from a different spectral band
-
- 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
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Sonobe et al. | Crop classification from Sentinel-2-derived vegetation indices using ensemble learning | |
Steinmetz et al. | Sentinel-2 MSI and Sentinel-3 OLCI consistent ocean colour products using POLYMER | |
Verhoef et al. | Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models | |
Masiello et al. | Simultaneous physical retrieval of surface emissivity spectrum and atmospheric parameters from infrared atmospheric sounder interferometer spectral radiances | |
Murakami | Ocean color estimation by Himawari-8/AHI | |
Sobrino et al. | Evaluation of the DART 3D model in the thermal domain using satellite/airborne imagery and ground-based measurements | |
Raymond Hunt Jr et al. | Remote sensing of fuel moisture content from canopy water indices and normalized dry matter index | |
Obata et al. | Derivation of a MODIS-compatible enhanced vegetation index from visible infrared imaging radiometer suite spectral reflectances using vegetation isoline equations | |
Miura et al. | Assessment of cross-sensor vegetation index compatibility between VIIRS and MODIS using near-coincident observations | |
Ottaviani et al. | Iterative atmospheric correction scheme and the polarization color of alpine snow | |
Mileva et al. | New tool for spatio-temporal image fusion in remote sensing: A case study approach using Sentinel-2 and Sentinel-3 data | |
Steward et al. | Modeling EO/IR systems with ASSET: applied machine learning for synthetic WFOV background signature generation | |
Richardson et al. | The OCO‐2 oxygen A‐band response to liquid marine cloud properties from CALIPSO and MODIS | |
Wang et al. | Trade-off studies of a hyperspectral infrared sounder on a geostationary satellite | |
Nalli et al. | Introduction: Field measurements and remote sensing | |
Zhou et al. | Versatile time-dependent spatial distribution model of sun glint for satellite-based ocean imaging | |
Katkovsky et al. | SHARC method for fast atmospheric correction of hyperspectral data | |
Arnone et al. | Diurnal changes in ocean color in coastal waters | |
Chen et al. | Suomi NPP VIIRS DNB and RSB M bands detector-to-detector and HAM side calibration differences assessment using a homogenous ground target | |
Simoneau et al. | MATISSE: version 1.4 and future developments | |
Bowers et al. | Regional vicarious gain adjustment for coastal VIIRS products | |
Zheng et al. | Seasonal change analysis of multispectral BRDF for different surface types | |
Qin et al. | Hemisphere Harmonics Basis: A Universal Approach to Remote Sensing BRDF Approximation | |
Maestri et al. | Spectral infrared analysis of a cirrus cloud based on Airborne Research Interferometer Evaluation System (ARIES) measurements | |
Li et al. | ABI legacy atmospheric profiles and derived products from the GOES-R Series |