A Rapid Parallel Mosaicking Algorithm for Massive Remote Sensing Images Utilizing Read Filtering

Abstract

Mosaicking is a crucial step in the application of remote sensing images. The amount of remote sensing image data has grown rapidly, along with the expansion of observed areas and increased image resolution. As a result, traditional serial mosaicking techniques are facing significant challenges. In recent times, various studies have utilized high-performance computing to hasten image mosaicking and attain favorable outcomes. Nevertheless, the current research only accelerates mosaicking through external technology, without optimizing from the perspective of algorithm flow, which introduces unnecessary data I/O and slows down the mosaicking. This paper introduces a rapid parallel remote sensing image mosaicking algorithm utilizing read filtering. To begin with, the target images are divided into blocks and stored in a distributed file system. Subsequently, the image blocks are read and filtered based on a designated input format. Finally, the overlapping and non-overlapping areas are read and processed asynchronously, reducing the data I/O and computing overhead, thereby improving the efficiency of parallel computing. The experiments indicate that the mosaicking algorithm introduced in this paper enhances throughput and speedup by an average of 1.38 MB/S and 0.87 relative to the current techniques, respectively, concerning various datasets and cores. This study provides a theoretical foundation and novel ideas for processing remote sensing images on cluster platforms.