Open AccessREMOTE SENSING IMAGE CLASSIFICATION USING ARTIFICIAL BEE COLONY ALGORITHM
Author(s) -
Srideepa Banerjee,
Akanksha Bharadwaj,
Daya Gupta,
V. K. Panchal
Publication year - 2014
Publication title -
international journal of computer science and informatics
Language(s) - English
Resource type - Journals
ISSN - 2231-5292
DOI - 10.47893/ijcsi.2014.1137
Subject(s) - land cover , computer science , artificial bee colony algorithm , remote sensing , classifier (uml) , swarm behaviour , artificial intelligence , contextual image classification , remote sensing application , fuzzy logic , satellite , data mining , algorithm , pattern recognition (psychology) , image (mathematics) , land use , geography , engineering , civil engineering , aerospace engineering , hyperspectral imaging
Remote Sensing has been globally used for knowledge elicitation of earth’s surface and atmosphere. Land cover mapping, one of the widely used applications of remote sensing is a method for acquiring geo-spatial information from satellite data. We have attempted here to solve the land cover problem by image classification using one of the newest and most promising Swarm techniques of Artificial Bee Colony optimization (ABC). In this paper we propose an implementation of ABC for satellite image classification. ABC is used for optimal classification of images for mapping the land-usage efficiently. The results produced by ABC algorithm are compared with the results obtained by other techniques like BBO, MLC, MDC, Membrane computing and Fuzzy classifier to show the effectiveness of our proposed implementation.