Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1, 27-34, 2014
https://doi.org/10.5194/isprsarchives-XL-1-27-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Nov 2014
Scalable Evolutionary Computation for Efficient Information Extraction from Remote Sensed Imagery
L. M. Almutairi1, S. Shetty1, and H. G. Momm2 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.
Conference paper (PDF, 878 KB)


Citation: Almutairi, L. M., Shetty, S., and Momm, H. G.: Scalable Evolutionary Computation for Efficient Information Extraction from Remote Sensed Imagery, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1, 27-34, https://doi.org/10.5194/isprsarchives-XL-1-27-2014, 2014.

BibTeX EndNote Reference Manager XML