Compression method of cloudy remote sensing image based on quantization strategy

Abstract

The invention discloses a cloud-containing remote sensing image compression method based on a quantization strategy, which mainly solves the problems that the prior art cannot retain ground object information in areas covered by thin clouds and consumes a lot of coding. The scheme is: expand the boundary of the cloudy remote sensing image and the cloud mask image, and perform downsampling and interpolation on the cloud area; calculate the approximate median of the region of interest in the cloudy remote sensing image; obtain the grayscale of different cloud areas Mask and mark the grayscale images of the inner and outer edges of different cloud regions; quantify the cloud region of the cloud-containing remote sensing image according to the cloud mask image; combine the grayscale images of the inner and outer edges of the cloud region to perform boundary on the quantized image of the cloud region Smoothing: Subtract a median from the values ​​of all pixels in the smoothed image, then compress and encode, and send the code stream to the receiving end; the receiving end decodes the code stream to obtain the restored image. The invention reduces encoding consumption, can partially retain the information of thin cloud coverage areas, and can be used for the transmission of cloud-containing images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an efficient compression method for remote sensing images containing invalid cloud regions, which can be used for transmission and processing of various remote sensing images containing clouds. Background technique [0002] The rapid development of high-resolution remote sensing technology has intensified the contradiction between satellite-ground image transmission bandwidth and remote sensing image data volume. In order to transmit and process acquired images, fast and efficient data compression algorithms are required. However, the ubiquitous cloud areas in remote sensing images occupy a large amount of storage space and transmission bandwidth, which reduces the data ROI of the region of interest, that is, the bandwidth utilization rate of the ground object area data in the image except the cloud area. Therefore, it is of great significance to study image compre...

AI Technical Summary

Problems solved by technology

However, these current preprocessing methods are all based on the filling strategy. During the filling process, all the information in the cloud area will be lost, and the ground object information under the thin cloud cannot be preserved, and it cannot strike a balance between the compression performance and the image restoration quality of the cloud area.

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