IAPRS & SIS. Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad. India,2002 
ARTIFICIAL NEURAL NETWORKS & MAXIMUM LIKELIHOOD FOR 
CLASSIFICATION OF PADDY WITH MULTI-TEMPORAL REFLECTANCE IMAGES 
C. S. Murthy*, P. V. Raju, K. Abdul Hakeem and K.V.S. Badrinath 
National Remote Sensing Agency, Hyderabad 500 037, India — (murthy. cs, raju pv, abdulhakeem_k, badrinath_kvs) @nrsa.gov.in 
COMMISSION VII, WG VII / 1.2 
KEY WORDS: Artificial Neural Networks, Multi-temporal, Classification, Reflectance, Accuracy 
ABSTRACT: 
The objective of the present study is to evaluate the performance of Artificial Neural Networks (ANN) and Maximum Likelihood (ML) 
for the classification of paddy with multi temporal reflectance images. The study was carried out in Sriramsagar command area, one ofthe 
largest surface irrigation systems in Andhra Pradesh state, India. The spatial information on the extent of paddy crop is very important for 
the command area authorities, to assess the irrigation utilization pattern. The classification of paddy in the study area was constrained by 
two factors; (1) non-availability of overpass data on optimum date and (2) staggered crop calendar. Two approaches were adopted for 
classification, namely, (1) sequential classification and (2) composite data classification. Sequential classification and masking was 
performed with February and March data, separately with ANN and MLC. The results indicate that the total estimated paddy area using 
ANN is 53% of reference estimate, where as that of MLC is only 38%. However, the accuracy of the estimate is 89% for ANN and 92% 
for MLC. Thus, the performance of ANN is relatively better than MLC, though in absolute sense, both the classifiers have failed to 
achieve complete classification through sequential approach. Combining the reflectance images of the two overpass dates i.e with multi- 
temporal data did the composite data classification. The estimated paddy area with MLC is about of 57% of reference estimate, where as 
that with ANN is 74%. The accuracy of the estimate is 89.14% for MLC and 89.84% with ANN. Thus, ANN classification has delineated 
more paddy area, compared to MLC without compromising with the accuracy of classification. The study concludes that Artificial Neural 
Networks has the potential for improving crop classifications using multi-temporal remote sensing images. 
“ 1. INTRODUCTION 
The differences in spectral reflectance pattern as a function of 
physiological properties of different vegetation types form the 
basic logic to identify the agricultural crops with remote sensing 
images. One of the major findings from various studies on remote 
sensing based crop land inventory is that maximum discrimination 
between different crop types occurs at different stages in the 
growth cycle and therefore, it is not possible to capture all these 
differences using a single date image. Consequently, the 
application of multi date or multi-temporal image data has been 
recommended to improve the discrimination ability (Hlavaka et al. 
(1980), Conese and Maseli (1991), Pax-Lenny and Wood cock 
(1997, Viera and Mather (2000). Although, Maximum Likelihood 
classification is the most common classification algorithm, studies 
have shown that other classification techniques such as Principal 
Components Analysis by Richards (1984), Change vector analysis 
by Lambin and Strahler (1994), greenness profile analysis by 
Badhwar (1984), spectra-temporal response surfaces analysis by 
Viera et al (1998, 2000), unsupervised classification and temporal 
profiling by Lo (1986) have greatly improved the classification. 
Non parametric classifiers like Artificial Neural Networks (ANN) 
have shown promise in the areas of pattern recognition and 
classification (Lipman 1987, King 1989, Benediktsson et al, 1990 
and Liu et al., 2000). ANN are parallel calculation architectures 
whose structures are based on the human brain. If suitably trained 
using a set of examples, they can learn and extract the link 
between the input data and the corresponding out data (Lippman, 
1987). 
Sriramsagar project command area, one of the largest surface 
irrigation systems in Andhra Pradesh. During rabi season (post 
  
* Corresponding author 
monsoon period i.e., December - May), paddy is the predominant 
crop followed by cotton and maize in the command area. The 
spatial information on the extent of paddy crop is very important 
for the command area authorities, to assess the irrigation 
utilization and to evaluate the irrigation system performance. 
Therefore, remote sensing data analysis was undertaken by 
National Remote Sensing Agency, in the command area during 
rabi 1998-99. 
The satellite data of January is very crucial for separating paddy 
from other crops. This is because, paddy in its early stages, has 
good seperability with the other crops which are in good 
vegetative phase. During rabi 1998-99, the satellite data of 
January is available only for one path, due to cloud cover problem. 
Therefore, crucial data for classification is missing for some 
portion of the study area falling in the 2" path of the satellite 
coverage. Further, the paddy cultivation is characterized by 
significant staggering in crop calendar, resulting in large 
variability in the crop condition during February and March. 
Under this background, the current study is taken up to improve 
the classification of paddy under these constraints, through 
different classification techniques. 
2. OBJECTIVE OF THE STUDY 
To evaluate the performance of Maximum Likelihood and 
Artificial Neural Networks algorithms for classifying paddy from 
non-paddy crops. The constraints for paddy classification in this 
study, as discussed in earlier sections are two fold; (1) non- 
availability of optimal overpass data and (2) staggered crop 
calendar.