卫星数据的大规模并行突变检测
Massively-Parallel Break Detection for Satellite Data
摘要：
遥感领域目前面临着大量的数据。虽然这提供了各种令人兴奋的研究机会，但它也带来了关于计算时间和空间要求的重大挑战。在实践中，庞大的数据量使得现有方法对于处理和分析所有可用数据来说太慢。这项工作的目的是加速BFAST，国家的最先进的方法之一，为打破检测给定的卫星图像时间序列。特别是，我们提出了一个完全并行的实现BFAST，可以有效地利用现代并行计算设备，如GPU。我们的实验评估表明，建议的GPU实现是四个数量级的速度比现有的公开可用的实现和高达十倍的速度比相应的多线程CPU执行。运行时间的大幅减少使得在几秒或几分钟内而不是几小时或几天内分析显著较大的数据集成为可能。我们展示了我们的实现的实际好处，给出了人工和真实的数据集。
英文摘要：
The field of remote sensing is nowadays faced with huge amounts of data. While this offers a variety of exciting research opportunities, it also yields significant challenges regarding both computation time and space requirements. In practice, the sheer data volumes render existing approaches too slow for processing and analyzing all the available data. This work aims at accelerating BFAST, one of the state-of-the-art methods for break detection given satellite image time series. In particular, we propose a massively-parallel implementation for BFAST that can effectively make use of modern parallel compute devices such as GPUs. Our experimental evaluation shows that the proposed GPU implementation is up to four orders of magnitude faster than the existing publicly available implementation and up to ten times faster than a corresponding multi-threaded CPU execution. The dramatic decrease in running time renders the analysis of significantly larger datasets possible in seconds or minutes instead of hours or days. We demonstrate the practical benefits of our implementations given both artificial and real datasets.
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