The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1, 27–34, 2014
https://doi.org/10.5194/isprsarchives-XL-1-27-2014
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1, 27–34, 2014
https://doi.org/10.5194/isprsarchives-XL-1-27-2014

  07 Nov 2014

07 Nov 2014

Scalable Evolutionary Computation for Efficient Information Extraction from Remote Sensed Imagery

L. M. Almutairi1, S. Shetty1, and H. G. Momm2 L. M. Almutairi et al.
  • 1Dept. of Electrical and Computer Engineering, Tennessee State University, Nashville, TN 37209, USA
  • 2Department of Geosciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA

Keywords: genetic programming, cloud computing, Hadoop, MapReduce, Hadoop Distributed File System

Abstract. 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 during the evolutionary process could significantly reduce the inherent computational cost of evolving solutions that are composed of multichannel large images. In this study, we present the design and implementation of a system that leverages cloud-computing technology to expedite supervised solution development in a centralized evolutionary framework. The system uses the MapReduce programming model to implement a distributed version of the existing framework in a cloud-computing platform. The proposed system has two major subsystems; (i) data preparation: the generation of random spectral indices; and (ii) distributed processing: the distributed implementation of genetic programming, which is used to spectrally distinguish the features of interest from the remaining image background in the cloud computing environment in order to improve scalability. The proposed system reduces response time by leveraging the vast computational and storage resources in a cloud computing environment. The results demonstrate that distributing the candidate solutions reduces the execution time by 91.58%. These findings indicate that such technology could be applied to more complex problems that involve a larger population size and number of generations.