Input, ---+ W, a Tu 
oW net, 
SE > 0 .=f{(net,A) ----20, 
A 3) 
| Outputs 
zd (activation) 
  
  
  
  
Input, ---> W, | 
weights 
  
Figure 1. An Artificial Neuron Computational Structure. 
where net-2W, O; (Schalkoff, 1992). 
j 
The programs use a back-propagation network that learns 
using the Generalized Delta Rule: 
AW, =n5,09 +CAW, 
where n = learning rate, & = momentum, à, = error at the 
kth-layer, o; is the output of layer j and W, is the 
connection weight between the jth-layer node and the kth- 
layer node (Li and Si, 1992). 
The term "back-propagation" refers to the training method 
by which the connection weights of the network are 
adjusted. The training of the network is similar to any 
supervised classification procedure, i.e. calibration blocks 
have to be selected and used to adapt the classifier. In this 
case network weights were adapted. The back-propagation 
learning procedure is simple and will not be detailed here. 
A multi-layer feed forward neural network using back- 
propagation was evaluated in this research. Specifically, 
EASI/PACE software NNCREAT, NNTRAIN and NNCLASS 
(PCI, 1994) were used. 
RESULTS AND DISCUSSION 
Per-pixel Classification 
Although three-date SAR combination had an 4% 
improvement of classification accuracy over the best single 
date classification alone, the overall validation accuracies 
for both single-date SAR and multi-temporal SAR were very 
low (see Table 1). The first reason for the poor accuracies 
was that MLC was not an effective classifier for SAR data 
classifications due to speckle. The second reason for the 
poor performances was that ERS-1 SAR data do not provide 
enough differences for eight crop classes due to its high 
incidence angle. Satellite SAR systems with multi-incidence 
angle, multi-resolution, and multi-wavelength, such as the 
Canadian RADARSAT, are very desirable to improve the 
performance of satellite SAR data for crop classification. 
The third reason for the poor accuracies was that the 
calibration and validation blocks were selected based on the 
August 5 field data, but the change of the crops over the 
50 
growing season could cause confusions. For example, a 
pasture/cut-hay class in August was an alfalfa class in June. 
TM3,4,5 alone produced a 89.895 classification accuracy 
(Table 1). Combinations of SAR and TM data improved the 
classification accuracies in general. The best overall 
accuracy (91.85) was the combination of all three dates SAR 
and TM3,4,5. It represents a 2% increase over the TM3,4,5 
classification alone. 
Per-field Classification 
Per-field classification with an ANN proved to be very 
effective. The crop classification accuracies improved by 
almost 20% using the combination of June, July and August 
SAR data. The best single-date (Aug.5) SAR classification 
with ANN improved the overall accuracy by about 26% 
(Table 2). The accuracies (<60%), however, were not high 
enough for operational crop inventory and analysis. 
The best per-field classification of 96.8% with an ANN 
classifier was achieved using the combination of TM3,4,5 
and Aug. 5 SAR data (Table 2). 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. Another possible explanation is that the 
SAR data and TM data were acquired one day apart and the 
ground condition was the same. In this classification, all 
crops achieved 100% accuracies except alfalfa and 
pasture/cut-hay-alfalfa classes (Table 3). Alfalfa had 16.3% 
commission error to first corn class (i.e., good growth), and 
pasture/cut-hay-alfalfa had a 15.1% commission error with 
second class of soybeans (i.e., poor growth). 
The second best classification accuracy was achieved using 
the combination of TM3,4,5 and July 24 SAR data (Table 2). 
The classification accuracy of TM3,4,5 using an ANN, 
however, is lower than that of a MLC. This is possibly due 
to the second corn class (ie., poor growth) was not well 
trained. It resulted in poor accuracy of second corn class 
(only 25.9%) with a commission error to barley/oat class 
62.1%, while all other classes were 100% correctly 
classified. 
The best ANN classification improved 5% in accuracy than a 
MD classification using the same dataset. ANN produced 
better accuracies in general than those derived from a MD 
classifier (Tables 3&4). This is because the post- 
segmentation classifier based on the MD classification of 
field means used calibration data obtained as in a per-pixel 
classification. Such a procedure fails to exploit the full 
range of information that segmentation offers. 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996 
e E es. all 
N 
S(