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

Yang et al., 2004 - Google Patents

Airborne hyperspectral imagery and yield monitor data for mapping cotton yield variability

Yang et al., 2004

Document ID
2722124598954221459
Author
Yang C
Everitt J
Bradford J
Murden D
Publication year
Publication venue
Precision Agriculture

External Links

Snippet

Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton (Gossypium hirsutum L.) yield variability as compared with yield monitor …
Continue reading at link.springer.com (other versions)

Classifications

    • 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
    • G01J3/30—Measuring the intensity of spectral line directly on the spectrum itself
    • G01J3/36—Investigating two or more bands of a spectrum by separate detectors
    • 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
    • G06K9/00624—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
Yang et al. Airborne hyperspectral imagery and yield monitor data for mapping cotton yield variability
Yang et al. Yield estimation from hyperspectral imagery using spectral angle mapper (SAM)
Yang et al. Relationships between yield monitor data and airborne multidate multispectral digital imagery for grain sorghum
Sakamoto et al. An alternative method using digital cameras for continuous monitoring of crop status
Hoffmann et al. Crop water stress maps for an entire growing season from visible and thermal UAV imagery
Ahamed et al. A review of remote sensing methods for biomass feedstock production
AU752868B2 (en) Method for monitoring nitrogen status using a multi-sprectral imaging system
Chen et al. Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management
Yang et al. Comparison of QuickBird satellite imagery and airborne imagery for mapping grain sorghum yield patterns
Yang A high-resolution airborne four-camera imaging system for agricultural remote sensing
Yang et al. Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield
US20010016053A1 (en) Multi-spectral imaging sensor
Yang et al. Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability
Mäkeläinen et al. 2D hyperspectral frame imager camera data in photogrammetric mosaicking
Yang et al. Mapping grain sorghum yield variability using airborne digital videography
Singh et al. Multi-temporal high resolution unmanned aerial vehicle (UAV) Multispectral imaging for menthol mint crop monitoring
Pauly Applying conventional vegetation vigor indices to UAS-derived orthomosaics: issues and considerations
Yang et al. Evaluating high resolution SPOT 5 satellite imagery to estimate crop yield
Yang et al. Comparison of airborne multispectral and hyperspectral imagery for estimating grain sorghum yield
Yang et al. Evaluating high-resolution QuickBird satellite imagery for estimating cotton yield
Yang Hyperspectral imagery for mapping crop yield for precision agriculture
Yang et al. Estimating cabbage physical parameters using remote sensing technology
Yang et al. Using multispectral imagery and linear spectral unmixing techniques for estimating crop yield variability
Yang et al. Airborne hyperspectral imaging and yield monitoring of grain sorghum yield variability
Yang et al. Optimum time lag determination for yield monitoring with remotely sensed imagery