MULTI-TEMPORAL ERS-1 SAR AND LANDSAT TM DATA FOR AGRICULTURAL CROP 
CLASSIFICATION: AN ARTIFICIAL NEURAL NETWORK APPROACH 
Yifang Ban, Ph.D. Candidate 
Earth-Observations Laboratory, Institute for Space and Terrestrial Science 
Department of Geography, University of Waterloo 
Waterloo, Ontario, Canada N2L 3G1 
Tel: (519) 888-4567 x5267 
Fax: (519) 888-6768 
yban@cousteau.uwaterloo.ca 
Commission VII, Working Group 2 
KEYWORDS:  Multitemporal, SAR, Landsat, Agriculture, Classification, Artificial Neural Networks 
ABSTRACT: 
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, corn (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 2096, 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. 
INTRODUCTION 
Radar remote sensing has the potential to play an important 
role in agricultural crop monitoring due to its independence 
from solar illumination and cloud cover. With the launch of 
the European Remote Sensing Satellite (ERS-1), the first 
long-duration spaceborne imaging SAR system became 
available. This and other spaceborne SAR systems, such as 
JERS-1, ERS-2 and the Canadian RADARSAT, provide 
researchers with an excellent opportunity for developing 
multi-temporal SAR agricultural applications. 
The synergistic effect of integrating SAR data and imagery 
acquired in the visible and infrared (VIR) portions of the 
spectrum has also been recognized as important for two main 
reasons. First, timeliness of SAR fills information gaps 
during overcast or hazy periods at the critical stages of the 
growing season, and second, the combination of data from 
different parts of the spectrum often leads to increased 
classification accuracy. Previous studies have shown that 
combining airborne SAR and satellite VIR data improves 
crop classification accuracies (Brown et al., 1984; Guindon 
et al.,, 1984; Hirose et al, 1984; Brisco et al, 1989; 
Fiumara and Pierdicca, 1989; Dixon and Mack, 1990; Brisco 
and Brown, 1995). Very little research, however, has been 
done to improve crop classification accuracies using data _ 
from two satellite sensors (Kohl et al., 1993; Fog et al., 
1993). Thus, the potential of Satellite SAR and VIR 
synergism still needs further investigation. 
Conventional statistical classifiers, such as MLC, make a 
number of untenable assumptions about the dataset to be 
classified (Foody et al., 1995). For example, this parametric 
approach requires the data to have a Gaussian distribution. 
SAR data, however, are not normally distributed due to 
speckles. Therefore, the accuracies of SAR crop 
classification using conventional statistical classifiers are 
often not high enough for crop inventory and analysis. In 
order to improve classification accuracy, it is necessary to 
explore robust classifiers using non-parametric and non- 
statistical approaches. 
ANN classifier presents a distribution-free approach to 
image classification. It also has the special advantages of 
simple local computations and parallel processing 
(Schalkoff, 1992). In the past few years, studies have shown 
that neural networks compared well to statistical 
classification methods in classification of multi-date, multi- 
source remote  sensing/geographic data, very high 
dimensional data and classification with high number of 
classes (e.g., Benediktsson et al., 1990a; Benediktsson et 
al., 1990b; Kanellopoulos et al, 1991). Foody et al. have 
(1994; 1995) also found that ANN produced higher 
classification accuracies in general than those derived from 
statistical classifiers when they were applied to airborne 
SAR data for classifications of agricultural crops. Therefore, 
it is desirable to investigate the effectiveness of ANN for 
crop classifications using satellite SAR and VIR data. 
The objective of this study was to evaluate the synergy of 
multi-temporal ERS-1 SAR and Landsat TM data for crop 
classification using an artificial neural network approach. 
The specific objectives were: 
* to evaluate the crop classification accuracies using a 
single-date SAR data alone and multi-temporal SAR data, 
* toevaluate the synergism of multi-temporal ERS-1 SAR 
and Landsat TM data for improving crop classification, 
and 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996