Vegetation index time sequence reconstruction method combining matrix completion and trend filtering

Abstract

The invention provides a vegetation index time sequence reconstruction method in combination with matrix completion and trend filtering, which comprises the following steps of: firstly, determining a missing position in time sequence data through quality marker data in a vegetation index product; the method comprises the following steps of: converting a one-dimensional vector into a two-dimensional matrix through matrix change, establishing an optimal energy equation for low-rank matrix completion for a matrix after each pixel is converted, and realizing matrix completion through an imprecise augmented Lagrange algorithm to obtain a time sequence completion matrix which does not contain data missing preliminarily; and finally, vectorizing the completion matrix, establishing an energy optimization equation of weighted trend filtering on the basis of a one-dimensional vector, and solving the model through an alternating direction multiplier method so as to further filter residual noise and obtain smooth and clean high-quality vegetation index time sequence data. And high-precision reconstruction of the long-time remote sensing vegetation index sequence is realized.

Description

technical field [0001] The invention relates to the technical fields of remote sensing technology and vegetation ecology, in particular to a vegetation index time series reconstruction method combined with matrix completion and trend filtering. Background technique [0002] Remote sensing vegetation index is an important index for studying the state and change of vegetation in terrestrial ecosystems, and it can reflect the long-term dynamic changes of vegetation structure and function in large-scale regions. In the past few decades, with the launch of a large number of optical remote sensing satellites and sensor imagers, such as Landsat, AVHRR, MODIS, SPOT, etc., a series of remote sensing vegetation indexes with a duration of more than ten years or even decades have been formed. Time series, including normalized difference vegetation index NDVI, enhanced vegetation index EVI, etc. At present, these standard NDVI time series products have been widely used in the fields of ...

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Problems solved by technology

[0005] The present invention proposes a vegetation index time series reconstruction method combined with matrix completion and trend filtering to solve the technical problem that high-quality NDVI time series data cannot be obtained in the prior art

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