Vine variety discrimination with airborne imaging spectroscopy

No abstract is available for this paper.

• Publication year

  2007

• Venue

  SPIE Optical Engineering + Applications

• Publication date

  2007-09-13

• Fields of study

  Agricultural and Food Sciences, Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1117/12.734177
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Delineation of vine parcels by segmentation of high resolution remote sensed images

  2007cited by this paper
• Transformation of high resolution aerial images in vine vigour maps at intra-block scale by semi automatic image processing

  2007cited by this paper
• Using hyperspectral remote sensing to map grape quality in 'Tempranillo' vineyards affected by iron deficiency chlorosis

  2007cited by this paper
• Hyperspectral Image Analysis for Precision Viticulture

  2006cited by this paper
• Kernel-based methods for hyperspectral image classification

  2005cited by this paper
• Robust support vector method for hyperspectral data classification and knowledge discovery

  2004cited by this paper
• Characterising and mapping vineyard canopy using high-spatial-resolution aerial multispectral images

  2003cited by this paper
• Row detection in high resolution remote sensing images of vine fields

  2003cited by this paper
• An assessment of support vector machines for land cover classi(cid:142) cation

  2002cited by this paper
• Discrimination of arid vegetation with airborne multispectral scanner hyperspectral imagery

  2001cited by this paper
• Use of hyperspectral reflectance for discrimination between grape varieties

  2001cited by this paper
• Use of hyperspectral imagery for mapping grape varieties in the Barossa Valley, South Australia

  2001cited by this paper
• Adaptive Threshold for Spectral Matching of Hyperspectral Data

  2001cited by this paper
• The Nature of Statistical Learning Theory

  2000cited by this paper
• Using vegetation reflectance variability for species level classification of hyperspectral data

  2000cited by this paper
• Support vector machines for optimal classification and spectral unmixing

  1999cited by this paper
• Monitoring vineyard variability from the air

  1999cited by this paper
• Hydrothermal Alteration Mapping at Bodie, California, Using AVIRIS Hyperspectral Data

  1998cited by this paper
• Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview

  1997cited by this paper
• Airborne imaging aids vineyard canopy evaluation

  1996cited by this paper
• The Nature of Statistical Learning

  1995cited by this paper
• SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum

  1994cited by this paper
• The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data

  1993cited by this paper
• A compact airborne spectrographic imager (CASI)

  1989cited by this paper
• MODTRAN: A Moderate Resolution Model for LOWTRAN

  1987cited by this paper
• The measurement of observer agreement for categorical data.

  1977cited by this paper

• Use of UAV-borne multispectral data and vegetation indices for discriminating and mapping three indigenous vine varieties of the Greek Vineyard

  2021cites this paper
• Hyperspectral discrimination of tea plant varieties using machine learning, and spectral matching methods

  2020cites this paper
• Remote Sensing and Cropping Practices: A Review

  2018cites this paper
• Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

  2016cites this paper
• SPECTRAL DISCRIMINATION AND REFLECTANCE PROPERTIES OF VARIOUS VINE VARIETIES FROM SATELLITE, UAV AND PROXIMATE SENSORS

  2015cites this paper
• Detecting and classifying vine varieties from very high resolution multispectral data

  2015cites this paper
• Spectral discrimination of tea plant varieties by statistical, machine learning and spectral similarity methods

  2014cites this paper
• Efficiency of Semi-Implicit Schemes for Anisotropic Diffusion in the Hypercube

  2012cites this paper
• Tensor-Driven Hyperspectral Denoising: A Strong Link for Classification Chains?

  2010cites this paper