Paper
1 August 1990 A workstation based image acquisition and processing instrument for spatial analysis of vegetation
John Rasure, Tom Sauer, Charlie Gage
Author Affiliations +
Proceedings Volume 1395, Close-Range Photogrammetry Meets Machine Vision; 13951V (1990) https://doi.org/10.1117/12.2294310
Event: Close-Range Photogrammetry Meets Machine Vision, 1990, Zurich, Switzerland
Abstract
Measured changes in vegetation indicate the dynamics of ecological processes and can identify the impacts from disturbance. Traditional methods of vegetation analysis tend to be slow because they are labor intensive; as a result, these methods are often confined to small local area measurements. Scientists need new algorithms and instruments that will allow them to efficiently study environmental dynamics across a range of different spatial scales. Presented is a new methodology that addresses this problem. This methodology includes the acquisition, processing and presentation of near ground level (NGL) image data and its corresponding spatial characteristics. The systematic approach taken encom- passes a feature extraction process, a supervised and unsupervised classification process, and a region labeling process yielding spatial information.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John Rasure, Tom Sauer, and Charlie Gage "A workstation based image acquisition and processing instrument for spatial analysis of vegetation", Proc. SPIE 1395, Close-Range Photogrammetry Meets Machine Vision, 13951V (1 August 1990); https://doi.org/10.1117/12.2294310
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Cited by 1 scholarly publication.
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KEYWORDS
Image processing

Vegetation

Image classification

Image analysis

RGB color model

Feature extraction

Machine vision

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