Classification of wheat crop with multi-temporal images: performance of maximum likelihood and artificial neural networks

Abstract

The need for multi-temporal data analysis for delineation of wheat crop has been demonstrated first. It is found that Maximum Likelihood Classification (MLC) with the composite data of multi-temporal images is limited by the problem of large null set containing crop pixels. Therefore, for effective classification of multi-temporal images, two approaches are evaluated: (1) MLC with different strategies—sequential MLC (s_MLC), MLC with Principal Components (pca_MLC) and iterative MLC (i_MLC); and (2) Artificial Neural Networks (ANN) with back-propagation method. These classifiers were applied on multi-temporal Indian Remote Sensing satellite (IRS)-1B images to classify wheat crop in two areas of India, one with dominant wheat and the other with less dominant wheat cultivation. Among the three strategies of MLC, i_MLC has resulted in relatively better classification of wheat. However, the result of ANN classification is superior to that of i_MLC with respect to the correctness of labelling of wheat pixels. The performance of ANN is proved to be better, in both the situations of dominant wheat and less dominant wheat cultivation.