Nothing Special   »   [go: up one dir, main page]

Weyermann et al., 2015 - Google Patents

Minimizing reflectance anisotropy effects in airborne spectroscopy data using Ross–Li model inversion with continuous field land cover stratification

Weyermann et al., 2015

Document ID
8903214015318421834
Author
Weyermann J
Kneubühler M
Schläpfer D
Schaepman M
Publication year
Publication venue
IEEE Transactions on Geoscience and Remote Sensing

External Links

Snippet

The spectral and radiometric quality of airborne imaging spectrometer data is affected by the anisotropic reflectance behavior of the imaged surface. Illumination and observation angle- dependent patterns of surface reflected radiation propagate into products, hinder …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • 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
    • 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
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/007—Dynamic range modification
    • G06T5/008—Local, e.g. shadow enhancement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • 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
    • 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
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • 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

Similar Documents

Publication Publication Date Title
Kimm et al. Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the US Corn Belt using Planet Labs CubeSat and STAIR fusion data
Meroni et al. Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations
Colombo et al. Retrieval of leaf area index in different vegetation types using high resolution satellite data
Tan et al. The impact of gridding artifacts on the local spatial properties of MODIS data: Implications for validation, compositing, and band-to-band registration across resolutions
Woodgate et al. Understanding the variability in ground-based methods for retrieving canopy openness, gap fraction, and leaf area index in diverse forest systems
US8571325B1 (en) Detection of targets from hyperspectral imagery
Hedley et al. Spectral unmixing of coral reef benthos under ideal conditions
Chi et al. Spectral unmixing-based crop residue estimation using hyperspectral remote sensing data: A case study at Purdue university
US8897570B1 (en) Detection of targets from hyperspectral imagery
US8897571B1 (en) Detection of targets from hyperspectral imagery
Pasher et al. Multivariate forest structure modelling and mapping using high resolution airborne imagery and topographic information
Weyermann et al. Minimizing reflectance anisotropy effects in airborne spectroscopy data using Ross–Li model inversion with continuous field land cover stratification
Collings et al. Empirical models for radiometric calibration of digital aerial frame mosaics
Canisius et al. Comparison and evaluation of Medium Resolution Imaging Spectrometer leaf area index products across a range of land use
Neld et al. Reliable detection and characterization of low-frequency polarized sources in the LOFAR M51 field
Zhang et al. Shadow-aware nonlinear spectral unmixing for hyperspectral imagery
Li et al. Quantifying key vegetation parameters from Sentinel-3 and MODIS over the eastern Eurasian steppe with a Bayesian geostatistical model
Wang et al. Assessment of multiple scattering in the reflectance of semiarid shrublands
Fernandes et al. A multi-scale approach to mapping effective leaf area index in boreal Picea mariana stands using high spatial resolution CASI imagery
Feingersh et al. Correction of reflectance anisotropy: A multi-sensor approach
Mu et al. Improvement of NDVI mixture model for fractional vegetation cover estimation with consideration of shaded vegetation and soil components
Borana et al. Discrimination and Characterization of Prominent Desertic Vegetations using Hyperspectral Imaging Data
Weil-Zattelman et al. Image-Based BRDF Measurement
Zhukov et al. Technique of combined processing for data of an imaging spectrometer and of a multispectral camera
Caprioli et al. Radiometric normalization of Landsat ETM+ data for multitemporal analysis