Algorithms for efficient multi-temporal change detection in SAR imagery

被引:0
|
作者
Allen, Michael [1 ]
Kosianka, Justyna W. [1 ]
Perillo, Mark [1 ]
机构
[1] Ursa Space Syst, 130 E Seneca St 520, Ithaca, NY 14850 USA
关键词
SAR; Change Detection; Time Series; Forest Change; Deforestation;
D O I
10.1117/12.2663997
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Advancements in the tasking and collection capabilities of SAR providers have reduced the spatiotemporal constraints on SAR-based change detection. As data constraints are relaxed, pairwise SAR-based change detection algorithms are rapidly becoming insufficient for summarizing change activity. To address this, we present two multi-temporal change detection algorithms that categorize, filter, and reduce the changes detected in any number of repeat pass SAR scenes: (1) Object Permanence change detection (OPcd); and (2) Activity Exclusion change detection (AEcd). When compared to traditional pairwise change detection methods, OPcd and AEcd allow for rapid digestion and efficient visualization of changes.
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页数:8
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