Title :
A comparative study of Computational Intelligence based techniques in the field of remote Sensing Image classification
Author :
Singh, Vartika ; Kumar, Gourav ; Sabherwal, Divya
Author_Institution :
Global Warming & Ecological Studies, Amity Univ., Noida, India
Abstract :
Remote Sensing Image classification is one of the major research areas due to its wide spectrum of applications including natural terrain feature classification, land use monitoring, ground water exploration, environmental disaster assessment and urban planning etc. All these applications give a great success to terrain use but the only thing required is the proper classification of remotely sensed image. For this, there is the need of a high level Computational Intelligence based classifier for the perfect use of land cover features. Computational Intelligence explodes its area based on Swarm Intelligence, Modelization of human mind, nature inspired and some other intelligent techniques. From these major categories, we included Artificial Bee Colony optimization, Cuckoo Search, Rough Set, Fuzzy Set, Membrane Computing, Minimum Distance Classifier and Maximum Likelihood Classifier for the comparison classification of Alwar region, India. Kappa coefficient is taken as acceptance estimation parameter. User´s accuracy & Producer´s accuracy are considered to check the accuracy of a solitary land feature. In this paper, we want to scrutinize that which computational Intelligence based classifiers give appropriate results under same circumstances to optimize this land features.
Keywords :
disasters; fuzzy set theory; image classification; maximum likelihood estimation; particle swarm optimisation; rough set theory; swarm intelligence; terrain mapping; town and country planning; Alwar region; India; Kappa coefficient; artificial bee colony optimization; computational intelligence; cuckoo search; environmental disaster assessment; fuzzy set; ground water exploration; human mind; image classification; land cover features; land use monitoring; maximum likelihood classifier; membrane computing; minimum distance classifier; natural terrain feature classification; remote sensing; rough set; swarm intelligence; urban planning; Accuracy; Biomembranes; Computational intelligence; Image classification; Remote sensing; Vegetation; Vegetation mapping; Artificial Bee Colony Optimization; Cuckoo Search; Fuzzy Set; Membrane Computing; Nature Inspired Techniques; Remote Sensing; Rough Set; Swarm Intelligence;
Conference_Titel :
Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on
Conference_Location :
New Delhi
Print_ISBN :
978-9-3805-4415-1