ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 303-310, 2016
https://doi.org/10.5194/isprs-annals-III-3-303-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
06 Jun 2016
DETECTING ANOMALY REGIONS IN SATELLITE IMAGE TIME SERIES BASED ON SESAONAL AUTOCORRELATION ANALYSIS
Z.-G. Zhou1, P. Tang2, and M. Zhou1 1Academy of Opto-electronics (AOE), Chinese Academy of Sciences (Key Laboratory of Quantitative Remote Sensing Information Technology, CAS), Beijing 100094, China
2Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), Beijing 100101, China
Keywords: Anomaly Detection, Remote Sensing, Disturbance Detection, SARIMA, Temporal Autocorrelation, Time Series Abstract. Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they are generally designed for detecting inter-annual or abrupt land cover changes, but are not focusing on detecting spatial-temporal changes in continuous images. In order to identify spatial-temporal dynamic processes of unexpected changes of land cover, this study proposes a method for detecting anomaly regions in each image of satellite image time series based on seasonal autocorrelation analysis. The method was validated with a case study to detect spatial-temporal processes of a severe flooding using Terra/MODIS image time series. Experiments demonstrated the advantages of the method that (1) it can effectively detect anomaly regions in each of satellite image time series, showing spatial-temporal varying process of anomaly regions, (2) it is flexible to meet some requirement (e.g., z-value or significance level) of detection accuracies with overall accuracy being up to 89% and precision above than 90%, and (3) it does not need time series smoothing and can detect anomaly regions in noisy satellite images with a high reliability.
Conference paper (PDF, 1742 KB)


Citation: Zhou, Z.-G., Tang, P., and Zhou, M.: DETECTING ANOMALY REGIONS IN SATELLITE IMAGE TIME SERIES BASED ON SESAONAL AUTOCORRELATION ANALYSIS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 303-310, https://doi.org/10.5194/isprs-annals-III-3-303-2016, 2016.

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