214
as the best to discriminate soybeans, com and
sugarcane in the area.
- Separability of J-M distance indicated that the
statistical structure for crop discrimination was
three-or four-dimensional. .Comparison of the
classification matrices of the training areas using
band combinations 2-4-5 and 2-4-5-7 showed similar
results and a small difference in computer
processing time. However, the smaller data storage
volume of the three-band classification makes it the
most appropriate for crop discrimination using
digital analysis in this study area.
REFERENCES
Anuta, P.E., Bartolucci L.A., Dean, M.E., Lazano,
D.F., Malaret E., McGillem C.D., 1984 Landsat-4
MSS and Thematic Mapper data quality and
information content analysis. TREE Trans, on
Geosci. and Remote Sens. GE-22(3): 222-235.
Atkinson P., Cushnie J.L. and Townshend J.R.G. 1985.
Inproving Thematic Mapper land cover
classification using filtered data. Int. J. Remote
Sens. 6(6): 955-961.
Salcmonson, V.V., Smith P.L., Pink A.B., Webb, W.C.,
and Lynch T.J. 1980. An overview of progress in
the design and implementation of Landsat D systems
I.E.E.E. Trans. Geosci. Remote Sensing 18:137-
146.
Sheffield C., 1985. Selecting band combinations
frcm multispectral data. Photogram. Eng. Remote
Sens. 51(6): 681-687.
Swain, P.M., King, R.C. 1973. Two effective
feature selection criterion for multispectral
remote sensing. LARS information note 402673 West
Lafayette, Purdue University.
Townshend, J.R.G., Gayler, J.R., Hardy, J.R.,
Jackson M.J., and Baker, J.R. 1983. Preliminary
analysis of Landsat-4 Thematic Mapper products.
Int. J. Remote Sens. 4 (4): 817-828.
Townshend J.R.G. 1984 Agricultural land-cover
discrimination using thematic mapper spectral
bands. Int. J. Remote Sens. 5(4): 681-698.
Young, T.Y., Calvert, T.W. 1974. Classification
estimation and pattern recognition. New York,
Elsevier.