Towards distributed region growing image segmentation based on MapReduce

Image segmentation is a critical step in image analysis, and usually involves a high computational cost, especially when dealing with large volumes of data. Given the significant increase in the spatial, spectral and temporal resolutions of remote sensing imagery in the last years, current sequential and parallel solutions fail to deliver the expected performance and scalability. This work proposes a scalable and efficient segmentation method, capable of handling efficiently very large high resolution images. The proposed solution is based on the MapReduce model, which offers a highly scalable and reliable framework for storing and processing massive data in cloud computing environments. The solution was implemented and validated using the Hadoop platform. Experimental results attest the viability of performing region growing segmentation in the MapReduce framework.