A Comparative Study of Four Different Satellite Image Classification Techniques for Geospatial Management

Satellite imagery interpretation has become the technology of choice for a host of developmental, scientific, and administrative management work. The huge repository of geospatial data and information that are available as satellite imageries datasets from platforms such as Google Earth need to be classified and understood for natural resources management, urban planning, and sustainable development. The classification and analysis procedures involve algorithms like maximum likelihood classifier, isodata, fuzzy-logic classifier, and artificial neural network based classifier. Amongst these classifiers the optimum has to be selected for classifications which involve multiple features and classes. Herein lies the motivation for the present research, which can facilitate the selection of one amongst the many algorithms available to a decision maker/manager. The aforementioned techniques are applied for classification, and the respective accuracies in the classes of forestry, rock, water, built-up area, and dry river bed have been tabulated and verified from ground truth. The comparison is based on time and space complexity of the algorithms considering also the accuracy. It is found that traditional methods like MLC and Isodata offer good time and space consumption performance over the recent more adaptable algorithms as fuzzy and ANN. But the latter group excels in accuracy of assessment. The study suggests points and cases for ranking the techniques as best, 2nd best, and so on, where each technique could be optimally utilised for a given geospatial dataset based on its contents.