Method of Removing Atmospheric Noise and Reconstructing Remote Sensing Monitoring Dataset

Abstract

The invention relates to a method for removing the influence of atmospheric noise and reconstructing a remote sensing monitoring data set. For the first time, a moving window is used to reduce the influence of the extreme value of the noise point on the digital signal waveform; choose iterative operation to achieve the best fitting effect; on the whole, the principle of remote sensing data reconstruction is simple, well retains the waveform characteristics of the original digital signal, and highlights the continuous change trend of the digital signal. Effectively solve the problem of excessive smoothing in digital signal processing. It is the first time to reduce the influence of noise points in the digital signal on the function fitting effect; the quadratic polynomial function better fits the change trend and curve direction of the original data, and the function fitting parameters are automatically completed, which solves the need for experiments to determine the optimal operation The problem of parameters has realized the whole process of automatic processing. After testing, the result data has a strong correlation with the actual NDVI information on the surface of the study area, which is indicative and representative.

Description

Technical field: The invention relates to a method for removing the influence of atmospheric noise and reconstructing a remote sensing monitoring data set, in particular, using a moving window to perform polynomial fitting iterative operations to filter out noise to reconstruct a time series data set. Background technique: There are dozens of satellite sequence data products commonly used in surface remote sensing monitoring, and the digital signal data with longer time series are mainly NDVI data sets obtained by satellite sensors such as NOAA / AVHRR, SPOT / VEGETATION, TERRA(AQUA) / MODIS, etc. Due to the influence of atmospheric conditions, solar elevation angle, and complex surface conditions, these data sets themselves have a lot of noise, and there are many and large outliers in the signal source, which destroys the continuity of the original surface signal. , the further research on surface remote sensing monitoring has caused great difficulties. The purpose of filtering...

AI Technical Summary

Problems solved by technology

The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, and provide a polynomial algorithm using a moving window to solve problems such as complex calculation process, excessive smoothing, and manual setting of calculation parameters in the process of digital information filtering and denoising. Fitting and Removing the Influence of Atmospheric Noise to Reconstruct Remote Sensing Monitoring Dataset

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