Hyperspectral image distributed compression method and system based on compressed sensing

Abstract

The invention discloses a hyperspectral image distributed compression method and system based on compressed sensing. The hyperspectral image distributed compression method comprises the steps of conducting spectral compression sampling on an original hyperspectral image; carrying out distributed encoding processing on the sampled data; dividing each waveband into at least one coding block with thesame number, taking the first waveband as a starting waveband, carrying out linear prediction on the current coding block by utilizing the corresponding coding block in the adjacent previous wavebandfor any coding block in any of the rest wavebands, and determining the number r of LSBs needing to be transmitted by the current coding block according to the prediction residual error; and transmitting the data after spatial lossless compression of each coding block in the first waveband, the first r LSBs of each coding block in other wavebands and the prediction coefficient to a receiving end for compressed sensing reconstruction of the hyperspectral image. The compression method provided by the invention has relatively low complexity, good compression performance and error code resistance.With a coding block as a basic unit, an error code is controlled in the coding block and cannot be transmitted to other blocks.

Description

technical field [0001] The present invention relates to the technical field of hyperspectral image information processing, in particular to a hyperspectral image distributed compression method and system based on compressed sensing. Background technique [0002] Hyperspectral remote sensing generates hundreds of band images in the visible light to near-infrared spectral range. Because it can provide rich spectral information of ground features, it has been widely used in the fields of environmental monitoring, disaster monitoring, and military reconnaissance. However, with the continuous improvement of the spatial and spectral resolution of imaging spectrometers, the amount of hyperspectral data acquired by them has expanded rapidly. For example, AVIRIS (Airborne Visible / Infrared Imaging Spectrometer, Airborne Visible / Infrared Imaging Spectrometer) acquires data of 224 bands in the spectral range of 0.4-2.5 μm, while IASI (Infrared Atmospheric Sounding Interferometer, Infrar...

AI Technical Summary

Problems solved by technology

Although CS can effectively reduce the dimensionality of hyperspectral images, the outstanding problem of applying CS to hyperspectral image compression is that compared with the original data, the dynamic range of sampled data is significantly increased, which offsets to a large extent The compression effect brought by the low sampling rate of CS; therefore, it is still necessary to use a low-complexity compression method to effectively compress the sampled data

However, LMM is an ideal model after all, and factors such as nonlinear mixing of spectra still exist, making LMM introduce errors in the reconstructed image, thus limiting the improvement of reconstruction quality

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