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Automatic corn-soybean classification using Landsat MSS data. I - Near-harvest crop proportion estimation. II - Early season crop proportion estimationThe techniques used initially for the identification of cultivated crops from Landsat imagery depended greatly on the iterpretation of film products by a human analyst. This approach was not very effective and objective. Since 1978, new methods for crop identification are being developed. Badhwar et al. (1982) showed that multitemporal-multispectral data could be reduced to a simple feature space of alpha and beta and that these features would separate corn and soybean very well. However, there are disadvantages related to the use of alpha and beta parameters. The present investigation is concerned with a suitable method for extracting the required features. Attention is given to a profile model for crop discrimination, corn-soybean separation using profile parameters, and an automatic labeling (target recognition) method. The developed technique is extended to obtain a procedure which makes it possible to estimate the crop proportion of corn and soybean from Landsat data early in the growing season.
Document ID
19840039598
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Badhwar, G. D.
(NASA Johnson Space Center Houston, TX, United States)
Date Acquired
August 12, 2013
Publication Date
January 1, 1984
Publication Information
Publication: Remote Sensing of Environment
Volume: 14
ISSN: 0034-4257
Subject Category
Earth Resources And Remote Sensing
Accession Number
84A22385
Distribution Limits
Public
Copyright
Other

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