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An efficient pixel clustering-based method for mining spatial sequential patterns from serial remote sensing images

一种高效的基于像素聚类的空间序列图像挖掘方法

关键词:
来源:
Computers & Geosciences
全文链接1:
http://agri.ckcest.cn/topic/downloadFile/46cfd639-d65d-411b-84df-ef51248c9ada
全文链接2:
https://www.sciencedirect.com/science/article/pii/S0098300418300682
类型:
学术文献
语种:
英语
原文发布日期:
2019-03
摘要:
The accumulation of serial remote sensing images provides plentiful data for discovering sequential spatial patterns in various fields such as agricultural monitoring, urban development, and vegetation cover. Otherwise, traditional sequential pattern-mining algorithms cannot be directly or efficiently applied to remote sensing images. In this study, we propose a pixel clustering-based method to improve the efficiency of mining spatial sequential patterns from raster serial remote sensing images (SRSI). Firstly, the images are compressed by using the Run-Length coding schema. Then, pixels with identical sequences are clustered by means of the Run-length code-based spatial overlay operation. Finally, a pruning strategy is proposed, to extend the prefixSpan algorithm to skip unnecessary database scanning when mining from pixel groups. The experimental results indicate that the method presented in this paper could extract spatial sequential patterns from SRSI efficiently. Although accurate support rates for the patterns may not be obtained, our method could ensure that all patterns are extracted with a lower time cost.
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