A Dynamic Monitoring Method Based on Multi-source Remote Sensing Sequence Images

Abstract

The invention relates to a dynamic monitoring method applicable to multi-source time-series remote sensing images. The method can effectively use the spatial information of data to improve the fusion method, reduce the characteristic difference between multi-source remote sensing images, and improve the effect of dynamic monitoring. First, construct a multi-source remote sensing image sequence, and use an appropriate image preprocessing method to preprocess the sequence remote sensing image, so that the remote sensing image sequence has the characteristics of consistent image radiation characteristics and high image registration accuracy; next, based on space-time The distributed enhanced multi-source remote sensing image fusion method-eMulTiFuse fuses the remote sensing image sequence to make the time series have more similar spatial characteristics; finally, based on the pixel features, the Mann-Kendall trend detection method is used to dynamically analyze the time series remote sensing images. Monitoring to obtain dynamic monitoring results. The present invention has higher accuracy.

Description

[0001] 1. Technical field [0002] The invention relates to a dynamic monitoring method applicable to multi-source time series remote sensing images, belonging to the technical field of spatial information. [0003] 2. Background technology [0004] At present, the dynamic monitoring based on time-series remote sensing images is mostly of medium and low resolution (especially MODIS time series), and the dynamic monitoring of single-source time-series remote sensing images is the main one. Single-source time series remote sensing images have more or less polluted areas such as no value, bad value, cloudy fog, etc., especially in warm and humid areas (such as Chongqing, Sichuan, Guizhou, Yunnan, etc.) are often disturbed by clouds and fog Therefore, the remote sensing images acquired by only one satellite are often difficult to meet the dynamic monitoring of this place. Studies have shown that when the time interval of cloud-free image data is longer than two years, the accuracy...

AI Technical Summary

Problems solved by technology

The existing multi-temporal remote sensing image dynamic monitoring method can not only monitor the long-term change trend of the research area, but also detect the occurrence of mutations and the real-time growth dynamics of crops, etc. The method of introducing multi-source time series remote sensing images into dynamic monitoring It has broad prospects and significance in research, but it may also cause inaccurate monitoring results due to changes in solar radiation angle, atmospheric conditions, ground humidity, sensor accuracy, etc.

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