Omkar, SN and Senthilnath, J and Mudigere, Dheevatsa and Kumar, M Manoj (2008) Crop Classification using Biologically-inspired Techniques with High Resolution Satellite Image. In: Photonirvachak - Journal of the Indian Society of Remote Sensing, 36 (2). pp. 175-182.
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Remote sensing provides a lucid and effective means for crop coverage identification. Crop coverage identification is a very important technique, as it provides vital information on the type and extent of crop cultivated in a particular area. This information has immense potential in the planning for further cultivation activities and for optimal usage of the available fertile land. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Further, image classification forms the core of the solution to the crop coverage identification problem. No single classifier can prove to satisfactorily classify all the basic crop cover mapping problems of a cultivated region. We present in this paper the experimental results of multiple classification techniques for the problem of crop cover mapping of a cultivated region. A detailed comparison of the algorithms inspired by social behaviour of insects and conventional statistical method for crop classification is presented in this paper. These include the Maximum Likelihood Classifier (MLC), Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) techniques. The high resolution satellite image has been used for the experiments.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Springer.|
|Keywords:||Satellite Image Classification; Maximum Likelihood Classifier; Neural Networks; Swarm Intelligence; Ant Colony Optimisation; Particle Swarm Optimisation|
|Department/Centre:||Division of Mechanical Sciences > Aerospace Engineering (Formerly, Aeronautical Engineering)|
|Date Deposited:||30 May 2009 06:19|
|Last Modified:||19 Sep 2010 05:30|
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