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

Abstract

The invention discloses an iterative hyperspectral image lossless compression method based on group low-rank representation, and solves the problems that a traditional compression method ignores the correlation of an image space, a clustering result is unstable, and modules are not connected. The method comprises the following implementation steps: defining a spectral angle similarity measurement method; roughly clustering the original image; solving a rough clustering block coefficient matrix through low-rank representation; re-clustering the coefficient matrix to obtain an initial clustering result; iteratively optimizing the initial clustering result to obtain a prediction coefficient and a prediction residual error of a final clustering block; carrying out entropy coding to obtain a code stream file to be transmitted; and after entropy decoding, decompressing the code stream file at a decoding end to obtain a lossless compressed hyperspectral image. According to the method, a spectral angle correlation measurement method is defined, and utilization of spatial correlation is increased; low-rank representation is combined with subspace clustering, so that the stability of a clustering result is improved; and each module is correlated through iterative optimization, so that the result compression ratio is increased. The method is applied to 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 group low-rank representation, which is used for hyperspectral image compression. Background technique [0002] Hyperspectral images are obtained by reflecting electromagnetic waves of different bands from the same surface 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 continuou...

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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