CONTINUOUS ANOMALY DETECTION IN SATELLITE IMAGE TIME SERIES BASED ON Z-SCORES OF SEASON-TREND MODEL RESIDUALS

被引：6

作者：

Zhou, Zeng-Guang ^{[1
,2
]}

Tang, Ping ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China

[2] Chinese Acad Sci, Acad Optoelect, Key Lab Quantitat Remote Sensing Informat Technol, Beijing 100094, Peoples R China

来源：

2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2016年

关键词：

Multi-temporal; land cover; change detection; disturbance detection; time series analysis;

D O I：

10.1109/IGARSS.2016.7729881

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Natural disasters or human activities, such as forest fire, flood, and deforestation, may lead to anomaly or disturbance of land cover. Continuous detection of anomalies is important for studying spatial-temporal processes of land cover changes. Although many time-series-analysis methods have been developed for change detection, to the best of our knowledge, few methods focus on continuously detecting anomalies in satellite image time series. This study proposes a method for continuous anomaly detection in satellite image time series based on Z-scores of Season-Trend model Residuals (ZSTR). The ability of ZSTR for continuous anomaly detection was validated with an experiment for detecting spatial-temporal anomaly regions caused by severe flooding.

引用

页码：3410 / 3413

页数：4

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