Scalable evolutionary computation for efficient information extraction from remote sensed imagery

Laila Muthyib Almutairi, Tennessee State University

Abstract

The evolutionary computation, in the form of genetic programming, is used to aid information extraction process from high-resolution satellite imagery in a semi-automatic fashion. Distributing and parallelizing the task of evaluating all candidate solutions could significantly reduce the computational cost inherent of evolving solutions composed of multi-channel large images. Therefore, the utilization of cloud-computing technology is used to enhance the existing evolutionary framework to expedite the supervised solution development stage. MapReduce programming model is used to implement a distributed version of the existed framework in cloud environment. The proposed system has three major subsystems; (i) data preparation: generation of random image band combinations, (ii) information extraction: utilization of genetic programming for spectrally distinguish the feature of interest from remaining image background of remote sensed imagery, and (iii) distributed processing: distributed implementation of genetic programming system on cloud computing environment to improve scalability. The proposed system reduces response time by leveraging the vast computational and storage resources in a cloud-computing environment. The results show that distributing the candidate solutions reduces the execution time by 91.58%. These findings allow the application of such technology to more complex problems with larger population size and number of generations.^

Subject Area

Information Technology|Information Science|Computer Science

Recommended Citation

Laila Muthyib Almutairi, "Scalable evolutionary computation for efficient information extraction from remote sensed imagery" (2014). ETD Collection for Tennessee State University. Paper AAI1567561.
http://digitalscholarship.tnstate.edu/dissertations/AAI1567561

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