Method for removing large-area thick clouds for optical remote sensing images through multi-temporal data

Abstract

The invention discloses a method for removing large-area thick clouds for optical remote sensing images through multi-temporal data. If large-area clouds exist in optical remote sensing images, and non-cloud data exist in other multi-temporal images in the areas, cloud area data can be repaired and reconstructed through complementary information of the data. The method comprises using all temporal non-cloud data for dictionary learning, taking relevance among images into account adaptively, learning an over-complete dictionary and optimal sparse representation coefficients of the images, and repairing and reconstructing the data in thick cloud areas. According to the method for removing large-area thick clouds, the different complementary information in multi-temporal image thick-cloud areas is used, relevance of the images serves as the weight, the image thick-cloud area data are filled by aid of a novel sparse representation theory, and accordingly, not only the high accuracy is obtained, but also the idea for removing large-area thick clouds is expanded, and an important practical significance is provided.

Description

technical field [0001] The present invention proposes a method for removing large-area thick clouds from optical remote sensing images by using multi-temporal data. The dictionary learning method of adaptive spectral weighting effectively utilizes the correlation between other time-phase data and thick cloud areas of images to be processed to realize thick cloud areas. Data filling relates to the technical field of optical remote sensing image processing. Background technique [0002] In actual imaging, optical remote sensing images are usually affected by weather conditions, the most common of which is clouds. Most of the remote sensing images obtained by optical sensors will be polluted by clouds to varying degrees, and the clouds cover up the real information of the underlying objects. In particular, thick clouds completely cover up the radiation information of ground objects, resulting in the lack of image information, which significantly reduces the image quality and s...

AI Technical Summary

Problems solved by technology

This type of method also requires that thick cloud areas cannot overlap, and the removal of thick clouds is not thorough, which easily affects image interpretation

[0006] The above method has a good effect on removing small thick clouds, but for the usually existing large areas of thick clouds, it is often unable to effectively use the complementary information between multi-temporal images, so it cannot completely remove

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