Abstract
The objective of this research is to investigate crop estimation using SPOT-5 satellite imagery. We specifically considered tobacco as our pilot crop and compared the obtained results with manually delineated calculations. For this research SPOT-5 imagery of 2.5m spatial resolution, was provided by Space and Upper Atmosphere Research Commission (SUPARCO), space agency of Pakistan. After preprocessing, which is a preparatory step in analyzing and classifying satellite imagery to improve classification results and reduce the efforts and processing time, different supervised classifiers namely Maximum Likelihood approach, Neural Network and Minimum Distance Classifier have been used to classify the imagery. Training data for classifiers has been collected through multiple field surveys using GPS receivers. The results obtained clearly show that the performance of maximum-likelihood classifier is better than the other considered counterparts. Also it is indicated that the newly developed system offer an efficient, reliable and faster approach for estimation of tobacco crop.
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