An Anomaly Detection Method for Remote Sensing Time Series

Abstract

The invention discloses a technical scheme of a remote sensing time sequence abnormity detection method. The method can synchronously detect a plurality of time abnormities existing in a remote sensing time sequence, the confidence coefficient of all the abnormities is 95% and the abnormal confidence coefficient of the single abnormity is greater than 99% when the significance level alpha is 0.05, and time information, level information, degree information and confidence coefficient information of the abnormities are provided synchronously. According to the basic thinking, firstly, first-order season difference is performed on an original remote sensing time sequence to obtain a difference sequence serving a season error sequence; robustness estimation is performed on the mean value and the standard deviation of the season error sequence, and z standardization is performed on the season error sequence to obtain a standard score sequence serving as a time sequence abnormity test statistic sequence; a test statistic critical value is calculated under control of a given significance level and a multiple test total error rate; the absolute value and the critical value of the test statistic sequence are compared, the abnormities are marked as abnormal points when preset conditions are met, p values of the abnormities are calculated according to the test statistic of the abnormal points and converted into the confidence level. The abnormities in the remote sensing time sequence are detected through the procedure, and the time information, the level information, the degree information and confidence coefficient information of the abnormities are obtained.

Description

technical field [0001] The invention belongs to the field of remote sensing image processing and information extraction, and relates to remote sensing image change detection and abnormal land cover information extraction, in particular to a method for detecting abnormal changes in land cover and extracting abnormal information based on remote sensing time series data. Background technique [0002] The state of land cover changes continuously over time. These changes include both normal changes, that is, periodic seasonal changes (such as vegetation germination to lushness to leaf fall), and abnormal changes, such as changes in land cover types (such as vegetation into built-up areas, forest land into farmland, dry land into paddy fields), and abnormal changes in land cover status (such as deforestation, overgrazing, flooding, overfire, vegetation pests and diseases, etc.). [0003] There have been extensive studies on land cover change using remote sensing images. Tradition...

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Problems solved by technology

[0008] Aiming at the defects of existing remote sensing time series anomaly detection methods, such as high complexity and false detection rate, low multiple anomaly detection rate, and unreliable abnormal information, the present invention discloses a new technical solution, which can detect The abnormality at multiple times is detected at the same time. When the significance level is α=0.05, the overall confidence of the abnormal detection result is 95%, and the confidence of a single abnormality is greater than 99%. At the same time, the abnormal time, level, degree and confidence are given degree information

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