CONCLUSIONS
Multi-temporal ERS-1 synthetic aperture radar (SAR) and
Landsat TM data were used to evaluate an artificial neural
network approach for crop classification. Six major crops,
i.e., winter wheat, com (good growth & poor growth),
soybeans (good growth & poor growth), barley/oats, alfalfa,
and pasture/cut-hay-alfalfa, were classified into eight classes.
The results show that both a single-date and multi-temporal
SAR data yielded poor classification accuracies using a
maximum likelihood classifier (MLC). With per-field
approach using a feed forward artificial neural network
(ANN), the overall classification accuracy of three-date SAR
data improved almost 20%, and the best classification of a
single-date (Aug. 5) SAR data improved the overall accuracy
by about 26%. These accuracies (<60%), however, were not
high enough for operational crop inventory and analysis.
Using the combination of TM3,4,5 and Aug. 5 SAR data, the
best per-field ANN classification of 96.8% was achieved. It
represents a 8.5% improvement over a single TM3,4,5
classification alone. It also represents a 5% increase over
the best per-pixel classification. This indicates that a
combination of mid-season SAR and VIR data was best suited
for crop classification. The results also show that the best
ANN classification had a 5% higher accuracy than a
minimum distance (MD) classification using the same
dataset.
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