Iterative hyperspectral image lossless compression method based on low-rank representation

Abstract

The invention discloses an iterative hyperspectral image lossless compression method based on low-rank representation, which solves the problems that the traditional compression method ignores the correlation of image space, the clustering result is unstable, and there is no connection between modules. The implementation steps include: defining the spectral angle similarity measurement method; roughly clustering the original image; solving the rough clustering block coefficient matrix by low-rank representation; re-clustering the coefficient matrix to obtain the initial clustering result; iteratively optimizing the initial clustering result to obtain The prediction coefficient and prediction residual of the final clustering block; entropy coding is then performed to obtain the code stream file to be transmitted; after entropy decoding, the code stream file is decompressed at the decoding end to obtain a lossless compressed hyperspectral image. The invention defines a spectral angle correlation measurement method to increase the utilization of spatial correlation; the combination of low-rank representation and subspace clustering increases the stability of clustering results; and iteratively optimizes and correlates each module to increase the result compression ratio. Used in the field of image compression.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to image lossless compression, in particular to an iterative hyperspectral image lossless compression method based on low-rank representation, which is used for hyperspectral image compression. Background technique [0002] Hyperspectral images are obtained by reflecting electromagnetic waves of different bands on the same ground object, and the number of bands in the visible to near-infrared spectrum range can reach hundreds. The nanoscale spectral resolution of hyperspectral images makes hyperspectral images rich in spectral information, which can provide precise details of ground features, and has a wide range of applications in environmental monitoring, military investigation, resource management, mineral exploration, and vegetation research. After decades of development, the amount of data acquired by imaging spectrometers has expanded rapidly with the continuous improve...

AI Technical Summary

Problems solved by technology

[0004] In summary, in the current lossless compression methods for hyperspectral images, there are problems: the spatial-spectral correlation of hyperspectral images is not fully utilized, the clustering results based on k-means are unstable and inaccurate, and the clustering results are not fully utilized. The relationship between the three modules of , prediction and entropy coding, these problems directly affect the compression results

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