Dingsheng Liu

TL;DR: This paper has proposed and designed a specific parallel IDW interpolation algorithm, incorporating both single process, multiple data (SPMD) and master/slave (M/S) programming modes, which indicates that the parallel algorithm can greatly reduce processing time and maximize speed and performance.

TL;DR: The proposed infrastructure of multiple data centers (MDC) for managing and processing massive remote sensing images is built on both groups of distributed DCs/clusters, which are equipped with DC or cluster resource manager.

TL;DR: A generic data‐structure oriented programming template to support massive remote sensing data processing in high‐performance clusters and templates that provide distributed abstractions for large remote sensing image data with complex data structure and allow these distributed data to be accessed as a global one are proposed.

TL;DR: To solve bottlenecks in remote sensing image processing, this paper proposes three corresponding optimization strategies, and their effectiveness is confirmed by further experiments.

TL;DR: A strategy of partitioning group based on hypergraph (PGH) is introduced to formulate the model of sharing files and another scheduling policy, which is called optimized task tree (OTT) strategy, is adopted to handle the DAG workflow of massive remote sensing data processing with data dependencies.