Multi-temporal data based remote sensing image weighted regression recovery method

Abstract

The invention provides a multi-temporal data based remote sensing image weighted regression recovery method. If effective data of a remote sensing image fail to be obtained due to data loss, cloud pollution and other causes, the effective data can be recovered through supplementary information contained in data obtained at another time. Firstly, geometric registration is performed on the damaged image and an auxiliary image used for providing reference information; and then pixels similar to a pixel in each area to be recovered are searched in the auxiliary image, and a recovered pixel is obtained after weighted regression calculation of each point to be recovered is performed according to the similar pixel changing relationship between the image to be recovered and the auxiliary image. The corresponding relationship of image similar points in different temporal images is fully used for the multi-temporal data based remote sensing image weighted regression recovery method, weighted calculations of the pixel space and spectrum differences are performed, and the recovered result is enabled to be more close to real data. The calculation efficiency is relatively high and easy to achieve, the practicability is high, and the multi-temporal data based remote sensing image weighted regression recovery method can be used for the business-oriented operation of remote-sensing image recovery.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and relates to a remote sensing image weighted regression restoration method based on multi-temporal data, which can effectively restore damaged data in the remote sensing image by using complementary information in the multi-temporal remote sensing image. Background technique [0002] During the acquisition of remote sensing images, due to the influence of satellites, sensors, or the atmosphere, there may be some noise or data loss in the finally acquired images, which will affect the normal use of remote sensing images. Therefore, after the remote sensing image is acquired, it has become a very important subject to use technical means to correct and restore it. Commonly used image restoration methods can be divided into two types based on a single image and based on multiple images. Among them, restoration methods based on a single image include interpolation, histogram...

AI Technical Summary

Problems solved by technology

For the restoration method based on a single image, when the image loses too much information, it is difficult to obtain better results by restoring the remaining information in one image alone, and it is necessary to introduce information from another image.

However, the same ground object in different images may be affected by its own spectral characteristics, different image acquisition times, and atmospheric factors, which may cause large differences in imaging, which brings great interference to image restoration.

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