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Neuro-classification of multi-type Landsat Thematic Mapper dataNeural networks have been successful in image classification and have shown potential for classifying remotely sensed data. This paper presents classifications of multitype Landsat Thematic Mapper (TM) data using neural networks. The Landsat TM Image for March 23, 1987 with accompanying ground observation data for a study area In Miami County, Indiana, U.S.A. was utilized to assess recognition of crop residues. Principal components and spectral ratio transformations were performed on the TM data. In addition, a layer of the geographic information system (GIS) for the study site was incorporated to generate GIS-enhanced TM data. This paper discusses (1) the performance of neuro-classification on each type of data, (2) how neural networks recognized each type of data as a new image and (3) comparisons of the results for each type of data obtained using neural networks, maximum likelihood, and minimum distance classifiers.
Document ID
19930049482
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Zhuang, Xin
(NASA Headquarters Washington, DC United States)
Engel, Bernard A.
(NASA Headquarters Washington, DC United States)
Fernandez, R. N.
(NASA Headquarters Washington, DC United States)
Johannsen, Chris J.
(Purdue Univ. West Lafayette, IN, United States)
Date Acquired
August 16, 2013
Publication Date
October 1, 1991
Subject Category
Earth Resources And Remote Sensing
Accession Number
93A33479
Funding Number(s)
CONTRACT_GRANT: NAGW-1472
Distribution Limits
Public
Copyright
Other

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