Two-channel remote sensing light spectrum imaging system based on compressed sensing and imaging method

Abstract

The invention discloses a two-channel remote sensing light spectrum imaging system based on compressed sensing, and an imaging method, and mainly solves the problems, in the prior art, that the utilization ratio of light spectrum information is low, the difficulty in manufacturing technique of a detector is high, high spatial resolution and high spectral resolution of spectrogram image can not be obtained at the same time. The imaging system comprises a beamsplitter module (1), a first observing passage module (2), a second observing passage module (3), and an image reconstructing and processing module (4). Original spectrogram images are divided into two paths of light beams which are the same in information and different in directions, the first observing passage module (2) and the second observing passage module (3) achieve complementary coding observation to the light spectrum image, observed results are output to the image reconstructing and processing module (4), a reconstruction model of the spectrogram image is built, and the original spectrogram images are reconstructed by using a nonlinear optimized method. The invention has the advantages that the utilization ratio of the light spectrum information is high, the manufacturing technique is simple, and the calculation complex rate is low, thereby being used for acquiring and reconstructing remote sensing spectrogram images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a spectral encoding imaging system and imaging method based on compressed sensing in the field of remote sensing, which can be used to realize the acquisition and reconstruction of remote sensing spectral images. Background technique [0002] A spectral image is defined as a three-dimensional data cube composed of spectral dimension data added to the traditional two-dimensional space domain, that is, a spectral image is composed of images of different spectral segments under the same field of view. Traditional panchromatic and color images have long been far from meeting people's application needs, and various remote sensing spectral imaging technologies have been widely developed. Spectral imaging technology uses several or dozens of bands to image the target at the same time, which can realize the simultaneous acquisition of target spatial information and spectra...

AI Technical Summary

Problems solved by technology

After data compression, a large amount of non-important data is discarded. This high-speed sampling and recompression process increases the complexity of the system and wastes a lot of sampling resources.

[0006] This naturally leads to a question: Can other transformation spaces be used to describe signals, and a new theoretical framework for signal description and processing can be established, so that it can be far lower than the requirements of the Nyquist sampling theorem while ensuring no loss of information? Sampling the signal at a rate that can fully restore the signal at the same time, that is, whether the sampling of the signal can be converted into the sampling of information

[0013] According to the above theory, scholars M.E.Gehm, R.Johm, D.J.Brady, R.M.Willet and T.J.Schualz of Duke University in the United States wrote in the paper "Single-shot compressive spectral imaging with a dual-disperser architecture" OPTICS EXPRESS, Vol.15, No. 21, pp.14013-14027, 2007 proposed to use random coding templates and two dispersion elements to realize the observation of spectral images, and finally reconstruct the original image through compressive sensing theory. The disadvantage of this method is that only single-channel system is used According to the observation, the spectral image will lose half of the effective information after passing through the encoding template, which reduces the spectral information utilization rate of the image, thereby reducing the reconstruction accuracy of the image

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