Multi-level codebook vector quantitative method for compressed encoding of hyperspectral remote sensing image

Abstract

The invention discloses a multi-level codebook vector quantitative method for compressed encoding of a hyperspectral remote sensing image and relates to the technical field of image processing. According to the method, a spectral vector of the hyperspectral image is divided into a low-dimension part, a middle-dimension part and a high-dimension part according to the distortion condition, then a large-size codebook is adopted in the low-dimension part with large distortion, a medium-size codebook is adopted in the middle-dimension part with not large distortion, a small-size codebook is adopted in the high-dimension part with small distortion, and therefore the multi-level codebooks are adopted, the mode that only a quarter of the weight training code index of the low-dimension part is extracted after dispersion degrees are ranked is combined, and the targets of effectively reducing the quantizing distortion of the hyperspectral image and obviously reducing calculation amounts of all parts are achieved with the same compression ratio. On the condition of the low calculation complexity, high-quality compressed encoding of the hyperspectral image is achieved with higher speed, and the method has the actual application value and is a hyperspectral image lossy and nearly lossless compression scheme good in compression performance.

Description

technical field [0001] The invention belongs to the field of hyperspectral remote sensing image processing, in particular to a multi-level codebook vector quantization scheme for compressing and encoding hyperspectral remote sensing images. Background technique [0002] In recent years, traditional two-dimensional color images have been far from meeting people's needs, and hyperspectral images have been replaced by more and more applications. A hyperspectral image is a spectral image formed by continuous spectral imaging of a target on hundreds of spectral bands by a hyperspectral imaging spectrometer with a nanoscale band width. It is defined as a three-dimensional image composed of a two-dimensional spatial domain and a one-dimensional spectral domain. Stereo data. This special image is different from ordinary two-dimensional grayscale images and color images, and it has the following characteristics: 1) Rich details and complex textures. Each ground target at the same s...

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Problems solved by technology

[0006] The present invention is aimed at various compression methods of existing hyperspectral images, or the compression quality is high but the calculation complexity is large, or the calculation amount is small but the image recovery quality is low, and its vector quantization scheme uses a unified codebook to quantify the components of each dimension, which lacks To solve the problem of data adaptability, a multi-level codebook vector quantization scheme for compressing and encoding hyperspectral remote sensing images is proposed

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