An automatic thick cloud removal method for multi-temporal remote sensing images

Abstract

The invention discloses a thick-cloud automatic removing method of multi-temporal remote sensing images. The method comprises the following steps: step1, collecting remote sensing satellite thick cloud images and T multi-temporal images which are in a same area with the remote sensing satellite thick cloud images; step2, detecting a thick cloud area and acquiring a thick cloud area indication template (img file=' dest_path_image 001. TIF' wi=' 40' he=' 20' / )(img file=' 153288dest_path_image 002. TIF' wi=' 40' he=' 20' / ); step3, automatically selecting a reference image: automatically selecting one multi-temporal image as a reference image (img file=' dest_path_image 003. TIF' wi=' 121' he=' 44' / ); step4, using a Poisson equation restoration method to remove the thick cloud and acquiring a preliminary cloud removing result (img file=' 277102dest_path_image 004.TIF' wi=' 62' he=' 25' / ); step5, bringing the preliminary cloud removing result (img file=' 295873dest_path_image 004.TIF' wi=' 62' he=' 25' / ) and a reference image (img file=' 708400dest_path_image 003.TIF' wi=' 121' he=' 44' / ) into a variation model, removing the thick cloud again and acquiring a final cloud removing result (img file=' 313563dest_path_image 006.TIF' wi=' 137' he=' 49' / ). In the invention, a reference image is determined through a root-mean-square error ((img file=' dest_path_image 007.TIF' wi=' 49' he=' 20' / )) of a thick cloud image and a plurality of multi-temporal images between gradient values. Man-machine interaction is not needed. The thick cloud and shadows are automatically removed. A pixel brightness of an original image and gradient information of the reference image are combined and a good fidelity to a pixel value is achieved.

Description

technical field [0001] The invention relates to a method for automatically removing thick clouds from remote sensing images, in particular to a method for automatically removing thick clouds from multi-temporal remote sensing images, and belongs to the technical field of remote sensing image preprocessing. Background technique [0002] In remote sensing images, cloud cover is one of the important factors causing the lack of remote sensing data. Due to the interference of cloud cover in a large number of remote sensing images, the clarity of the information of interest is reduced, thereby reducing the utilization rate. Effectively reducing or removing the influence of clouds is an important way to improve the utilization rate of remote sensing data, and it is also an important issue in remote sensing image preprocessing. Thick cloud removal of remote sensing images can restore incomplete images, increase the source of remote sensing image data, reduce data costs, and provide...

AI Technical Summary

Problems solved by technology

However, the existing methods for removing thick clouds from multi-temporal images lack effective and stable criteria for selecting reference images.

At the same time, these methods require a lot of manual intervention, and it is difficult to remove thick clouds and cloud shadows at the same time

Therefore, these methods cannot be practically applied

Images