Burr et al., 2006 - Google Patents
Overview of physical models and statistical approaches for weak gaseous plume detection using passive infrared hyperspectral imageryBurr et al., 2006
View HTML- Document ID
- 17209925772915029087
- Author
- Burr T
- Hengartner N
- Publication year
- Publication venue
- Sensors
External Links
Snippet
The performance of weak gaseous plume-detection methods in hyperspectral long-wave infrared imagery depends on scene-specific conditions such at the ability to properly estimate atmospheric transmission, the accuracy of estimated chemical signatures, and …
- 238000001514 detection method 0 title abstract description 43
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
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Liu et al. | Transfer learning for soil spectroscopy based on convolutional neural networks and its application in soil clay content mapping using hyperspectral imagery | |
Peng et al. | Prediction of soil nutrient contents using visible and near-infrared reflectance spectroscopy | |
Lu et al. | Evaluating empirical regression, machine learning, and radiative transfer modelling for estimating vegetation chlorophyll content using bi-seasonal hyperspectral images | |
Jiang et al. | Estimating soil organic carbon of cropland soil at different levels of soil moisture using VIS-NIR spectroscopy | |
Sun et al. | Estimating rice leaf nitrogen concentration: influence of regression algorithms based on passive and active leaf reflectance | |
Liu et al. | A novel principal component analysis method for the reconstruction of leaf reflectance spectra and retrieval of leaf biochemical contents | |
Laamrani et al. | Ensemble identification of spectral bands related to soil organic carbon levels over an agricultural field in Southern Ontario, Canada | |
Gholizadeh et al. | A memory-based learning approach as compared to other data mining algorithms for the prediction of soil texture using diffuse reflectance spectra | |
Men et al. | A classification method for seed viability assessment with infrared thermography | |
S. Veum et al. | Predicting profile soil properties with reflectance spectra via Bayesian covariate-assisted external parameter orthogonalization | |
Sun et al. | Improving the retrieval of crop canopy chlorophyll content using vegetation index combinations | |
Dong et al. | Hyperspectral target detection via adaptive information—Theoretic metric learning with local constraints | |
Alebele et al. | Estimation of canopy biomass components in paddy rice from combined optical and SAR data using multi-target Gaussian regressor stacking | |
Wei et al. | Estimation of arsenic content in soil based on laboratory and field reflectance spectroscopy | |
Rocha et al. | Machine learning using hyperspectral data inaccurately predicts plant traits under spatial dependency | |
Burr et al. | Overview of physical models and statistical approaches for weak gaseous plume detection using passive infrared hyperspectral imagery | |
Xu et al. | A correlated multi-pixel inversion approach for aerosol remote sensing | |
Brigot et al. | Retrieval of forest vertical structure from PolInSAR data by machine learning using LIDAR-derived features | |
Du et al. | Using different regression methods to estimate leaf nitrogen content in rice by fusing hyperspectral LiDAR data and laser-induced chlorophyll fluorescence data | |
Gewali et al. | Gaussian processes for vegetation parameter estimation from hyperspectral data with limited ground truth | |
Zhang et al. | Cloud detection from FY-4A’s geostationary interferometric infrared sounder using machine learning approaches | |
Yang et al. | Soil nutrient estimation and mapping in farmland based on UAV imaging spectrometry | |
Zhao et al. | Calibration transfer based on affine invariance for NIR without transfer standards | |
Blanco-Sacristán et al. | Spectral diversity successfully estimates the α-diversity of biocrust-forming lichens | |
Zhang et al. | Rapid identification and prediction of cadmium-lead cross-stress of different stress levels in rice canopy based on visible and near-infrared spectroscopy |