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Sparse matrix based compressed sensing processing method for hyperspectral remote sensing images

A hyperspectral remote sensing and sparse matrix technology, applied in the field of compressed sensing processing, can solve the problem of complex hardware implementation of Gaussian random matrix

Inactive Publication Date: 2013-04-03
SHANDONG UNIV
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Problems solved by technology

However, the image reconstruction technology described in this article only deals with ordinary 8-bit images, and the Gaussian random matrix hardware used is complex

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  • Sparse matrix based compressed sensing processing method for hyperspectral remote sensing images
  • Sparse matrix based compressed sensing processing method for hyperspectral remote sensing images
  • Sparse matrix based compressed sensing processing method for hyperspectral remote sensing images

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[0048] A compressive sensing processing method for hyperspectral remote sensing images based on sparse matrices, including wavelet transform, data type conversion, quantization, sparse matrix compression coding, Orthogonal Matching Pursuit (OMP) decoding, data type inverse transformation, and inverse quantization And the eight steps of wavelet inverse transform, in which wavelet transform, data type conversion, quantization and sparse matrix compression coding are collectively referred to as the encoding process, orthogonal pursuit matching (OMP) decoding, data type inverse transform, inverse quantization and wavelet inverse transform are collectively referred to as Decoding process, the steps of the method are as follows:

[0049] (1) Wavelet transform

[0050] The hyperspectral remote sensing image data is subjected to wavelet transform, and its coefficients in the wavelet domain are recorded as quantized input data; the discrete wavelet transform formula is as follows:

[...

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Abstract

The invention belongs to the field of mobile communication source coding and discloses a sparse matrix based compressed sensing processing method for hyperspectral remote sensing images. The method includes the steps of wavelet transform, data type transform, quantization, sparse matrix compressed encoding, OMP (orthogonal matching pursuit) decoding, data type inverse transform, inverse quantization and wavelet inverse transform, and the eight steps are executed sequentially. The method can be used for processing two types of remote sensing images, wherein the remote sensing images of the first type are not subjected to geometric correction, and the remote sensing images of the second type are subjected to geometric correction. Data type transform and data type inverse transform are added to the whole processing method aiming at the remote sensing images of the second type. The method for processing the hyperspectral remote sensing images has the advantages of high image compression proportion, saving of memory and computation space and high image restoration quality.

Description

technical field [0001] The invention relates to a compressive sensing processing method for a hyperspectral remote sensing image based on a sparse matrix, and belongs to the field of mobile communication information source coding. Background technique [0002] Remote sensing technology can fully identify the characteristics of these objects by recording the radiant energy emitted or reflected by the objects. Traditional remote sensors acquire images in several discrete bands with different band widths (usually 100-200nm), which loses a lot of useful information for object recognition. In contrast, hyperspectral remote sensing technology closely combines imaging technology and spectral technology. While imaging the spatial characteristics of the target, each spatial pixel is detected in the ultraviolet, visible, near-infrared, and short-wave infrared regions of the electromagnetic spectrum. Continuous spectral coverage is formed through dispersion and even hundreds of narrow...

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Application Information

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IPC IPC(8): H04N7/26H04N7/30H04N19/124H04N19/63
Inventor 马丕明李丹丹熊海良
Owner SHANDONG UNIV
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