Remote sensing image classification for Jabalpur region using swarm classifiers

Abstract

Swarm intelligence algorithms have been widely applied in solving many complex problems in different domains. In this paper, swarm intelligence-based cuckoo search (CS) and artificial bee colony optimisation (ABC) are used for satellite image classification of Jabalpur region (Madhya Pradesh), India. The reason for the selection of CS and ABC algorithm over other swarm intelligence concepts is the wide and efficient applicability of these concepts in different domains. The swarm classifiers-based obtained results are compared with other than swarm intelligence techniques mainly maximum likelihood classifier (MLC), minimum distance classifier (MDC) and fuzzy logic. Results are evaluated in terms of kappa coefficient, user's accuracy, producer's accuracy and overall accuracy. Results in terms of individual feature accuracy (user's accuracy and producer's accuracy) and consolidated accuracy (kappa coefficient and overall accuracy) indicates the dominance of swarm classifiers in comparison with other considered classifiers for the classification of land cover features.