Satellite-borne remote sensing image compression method based on self-adaptive block compressed sensing

A block-compressed sensing and remote sensing image technology, applied in the field of image processing, can solve the problems of high complexity and high hardware requirements, and achieve the effect of eliminating block effects and reducing reconstruction time

Active Publication Date: 2019-11-01
SHANGHAI RADIO EQUIP RES INST
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Problems solved by technology

The patent document "Compressed Sensing Image Reconstruction Method Based on Measurement Domain Block Saliency Detection" with publication number CN105678699A proposes to transform the measured values ​​of sub-blocks, and classify sub-blocks as salient blocks or non-salient blocks according to the transformed structure , assign different sampling rates at the same time, and perform secondary measurement on the image. This method is more complex and requires higher hardware requirements.

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  • Satellite-borne remote sensing image compression method based on self-adaptive block compressed sensing
  • Satellite-borne remote sensing image compression method based on self-adaptive block compressed sensing
  • Satellite-borne remote sensing image compression method based on self-adaptive block compressed sensing

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Embodiment Construction

[0029] The present invention will be further elaborated below by describing a preferred specific embodiment in detail in conjunction with the accompanying drawings.

[0030] Such as figure 1 As shown, a spaceborne remote sensing image compression method based on adaptive block compressed sensing, including:

[0031] Step S1, divide an image into several sub-blocks, the size of the sub-blocks in this example is 64×64, but not limited to this value.

[0032] Step S2, obtain the gray level co-occurrence matrix of each sub-block, as shown in the following formula:

[0033]

[0034] in, Indicates the gray level co-occurrence matrix of the current sub-block, is the probability that a pixel with a gray level of α reaches a pixel with a gray level of β through a pre-set path relationship, α and β represent the gray level of the pixel, L represents the gray level of the image, and d k (k=1,2,3,4) is the path relationship between pixels on the image, respectively representing t...

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Abstract

The invention provides a satellite-borne remote sensing image compression method based on self-adaptive block compressed sensing, and aims to solve the problems that the data volume of a satellite-borne remote sensing image is large, if the whole image is subjected to compressed sensing, a measurement matrix in the compression process needs to occupy more storage space, and meanwhile, the reconstruction complexity is increased. According to the method, after an image is partitioned, whether the sub-blocks continue to be partitioned or not is judged according to the average entropy value of thegray level co-occurrence matrix of each sub-block; and after the partitioning is finished, different sampling rates are distributed to each sub-block according to the properties and the average entropy of each sub-block. The space occupied by the observation matrix is reduced, the blocking effect is eliminated, and the method is suitable for compression of satellite-borne remote sensing images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to block compressed sensing technology, in particular to a method for compressing remote sensing images through block compressed sensing, which can be used for compressing large-scale remote sensing images. Background technique [0002] Compared with other types of images, spaceborne remote sensing images have a larger amount of data and contain more texture information. The sampling frequency of traditional signals should be greater than twice the highest frequency of the signal, so following the Nyquist theorem to sample spaceborne images requires a higher sampling frequency. However, limited by the equipment conditions on the star, the traditional method cannot meet the high requirements for the storage capacity and processing speed of the compression encoding hardware. Compressed Sensing (CS) theory uses very little data to represent as much information as possible, and ...

Claims

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Application Information

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IPC IPC(8): H04N19/86H04N19/176H04N19/132H04N19/42H04N19/426G06T7/45
CPCG06T2207/20004G06T7/45H04N19/132H04N19/176H04N19/42H04N19/426H04N19/86
Inventor 于欢王凤姣焦美敏吴雪微丁红晖顾泽凌李乃星
Owner SHANGHAI RADIO EQUIP RES INST
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