Remote sensing image time sequence change detection method and system

Abstract

An embodiment of the invention provides a remote sensing image time sequence change detection method. The method comprises the steps of performing cloud removal on a to-be-detected time sequence imageto obtain a cloudless time sequence curve of each pixel of a cloudless time sequence image at each waveband; obtaining a seasonal breakpoint position in each cloudless time sequence curve; and basedon a preset screening algorithm, detecting whether the change of each seasonal breakpoint position is true change or false change. According to the remote sensing image time sequence change detectionmethod and system provided by embodiments of the invention, by utilizing spatial information of seasonal earth surface change, the position of each seasonal breakpoint in a time sequence is accuratelydetermined, and whether the change of each seasonal breakpoint position is the true change or the false change is judged, so that effective detection of the time sequence is realized.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of remote sensing image processing, and in particular to a method and system for detecting changes in time series of remote sensing images. Background technique [0002] Earth observation satellites can provide large-scale surface remote sensing images for a long time, and have great potential in time series change detection. Time series change detection is to use a series of images to qualitatively analyze the time effect of a phenomenon and quantify its transformation. Currently, change detection technology based on medium time and spatial resolution is used in the detection of agricultural land changes, agricultural conditions and forest conditions. , Urban sprawl and other aspects have made great progress. [0003] At present, there are various methods for time series change detection, which can be divided into two types according to their basic processing units: the first type is ...

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Problems solved by technology

[0004] However, the mutation detection of the above two methods is carried out in the trend component after removing the seasonal influence, and the cloud and snow pixels remaining in the trend component will be misjudged as the mutation pixel

Moreover, the above two analysis methods only consider the temporal changes of pixels, and do not effectively use the spatial information, especially the surrounding pixels that have a certain spatial correlation with the mutation pixel, and the surrounding pixels can reduce the analysis false alarm rate. The role in often can not be ignored, resulting in poor detection of time series changes

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