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Sparse-spectrum-dictionary hyperspectral image reconstruction method by using compressed sensing

A hyperspectral image and sparse spectrum technology, which is applied in the field of compressive sensing hyperspectral image reconstruction, achieves wide application, high precision, and good sparse effect

Active Publication Date: 2013-08-14
ACAD OF OPTO ELECTRONICS CHINESE ACAD OF SCI
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Problems solved by technology

Since the information of the target is unknown before imaging, the construction of sparse basis is a challenge, and the construction of spectral sparse dictionary in compressive sensing hyperspectral imaging has not been reported at home and abroad.

Method used

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  • Sparse-spectrum-dictionary hyperspectral image reconstruction method by using compressed sensing
  • Sparse-spectrum-dictionary hyperspectral image reconstruction method by using compressed sensing
  • Sparse-spectrum-dictionary hyperspectral image reconstruction method by using compressed sensing

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Embodiment

[0044] Below in conjunction with an example the present invention will be further described.

[0045] Step 1. Extract different types of object spectral curves from the ASTER spectral library as a training sample library. Assume that the spectral range of the spectral line used for training is 450nm-958nm, corresponding to 128 spectral segments with an average spectral resolution of 4nm, that is, the wavelengths are 450, 454, 458, ... 958nm, from visible light to some near-infrared bands, training The number of samples is 611, and the training dictionary size is 128. The number of spectral lines of different types of ground objects in the training samples is shown in the table below.

[0046] Feature type man-made material Water body plant rock mineral total number of training samples 45 11 5 193 357 611

[0047] Since the wavelength ranges of the spectral line data in the ASTER spectral library are inconsistent, the spectral line data co...

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Abstract

The invention relates to a sparse-spectrum-dictionary hyperspectral image reconstruction method by using compressed sensing, belongs to the technical field of remote sensing, and aims to solve the problems of large data amount, complex system and high cost in a current hyperspectral imaging system. The method is based on a current ground-object spectrum library, and comprises the following steps: selecting curves of typical spectrums to form a sampling library in a classified manner, adopting related algorithms in a signal sparse decomposition field to train the sampling library to obtain a sparse dictionary, combining a compressed measured value and a random measurement matrix to perform high-spectrum reconstruction, and adjusting related parameters of the algorithm according to a reconstruction effect till to be the best. The sparse dictionary obtained by the method has a better sparsification effect on ground-object spectrums; the precision of spectrum reconstruction is higher; and unlike decomposition and reconstruction of a conventional signal under the sparse dictionary, the method does not need priori information of a target, and has a wide application range.

Description

technical field [0001] The invention relates to a compressive sensing hyperspectral image reconstruction method based on a sparse spectral dictionary, which belongs to the technical field of remote sensing. Background technique [0002] Hyperspectral remote sensing has higher spectral resolution and more bands. It can quantitatively analyze the physical and chemical properties of the earth's surface by using the diagnostic spectral features of ground objects, and distinguish the properties of ground objects that cannot be distinguished by multispectral data. Therefore, in recent years To become a remote sensing imaging technology that countries are competing to develop. Hyperspectral remote sensing images can be vividly represented as "image cubes" that describe the two-dimensional spatial information and one-dimensional spectral information of the target. With the improvement of spatial resolution and spectral resolution, the amount of hyperspectral data also increases rapi...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 李传荣马灵玲汪琪唐伶俐胡坚李子扬王宁周勇胜李峰
Owner ACAD OF OPTO ELECTRONICS CHINESE ACAD OF SCI
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