Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 549-554, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-549-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
09 Jun 2016
ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS
Z.-G. Zhou1, P. Tang2, and M. Zhou1 1Academy of Opto-electronics (AOE), Chinese Academy of Sciences (CAS), Beijing 100094, China
2Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), Beijing 100101, China
Keywords: Multi-temporal, Anomaly Detection, Change Detection, Disturbance Detection, Confidence Level, Time Series Abstract. Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level) of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1) Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST). (2) Forecasting and detecting disturbances in new time series data. (3) Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI) and Confidence Levels (CL). The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.
Conference paper (PDF, 1294 KB)


Citation: Zhou, Z.-G., Tang, P., and Zhou, M.: ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 549-554, https://doi.org/10.5194/isprs-archives-XLI-B3-549-2016, 2016.

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