Adaptable parallel strategy to extract polygons from massive classified images on multi‐core clusters

Abstract

Summary It is always computing intensive and time consuming to extract polygons from massive classified images. Although parallel computing can improve the efficiency of geographical data processing, the performance of the conversion suffers from the trade‐off between data decomposition and result stitching. In this paper, we present an adaptable parallel strategy that accelerates the conversion process on a multi‐core cluster. The strategy improves the method of data decomposition and optimizes task scheduling. In our strategy, an adaptable decomposition method is used to partition raster data according to the data complexity of the raster dataset and computing capability of the computing nodes. Moreover, the hierarchical task scheduling optimizes task allocation and load balance among computing nodes for the procedure of parallel conversion and stitching. We implemented parallel algorithms based on a boundary linking algorithm using the adaptable parallel strategy and compared the performance with the performances of conventional parallel strategies. The results show that the processing time of experimental raster data was reduced from 1362.36 to 165.78 s and that a desirable speedup with the maximal value of 8.23 was achieved. Copyright © 2016 John Wiley & Sons, Ltd.

Integrated development environment model for visual image processing based on Moore nearest neighbor model

“…The research on the image technology has been carried out in universities, research institutes, and companies, which is increasingly deepened and carried out in a systematic manner. Great attention is paid to the inheritance of knowledge and algorithm reuse [15][16][17][18][19][20][21], the pursuit of high efficiency, and low cost. Therefore, it is necessary to provide an environment with powerful support for the learning, research, and development of the image algorithm.…”