A method for joint restoration of hyperspectral data based on compressed sensing in spatial and spectral domains
A compressed sensing and spatial spectral domain technology, applied in image data processing, instrumentation, computing, etc., can solve the problem that Gaussian random matrix is difficult to implement with hardware, high cost, and does not jointly consider the spatial domain correlation and spectral domain correlation of hyperspectral images. And other issues
Active Publication Date: 2016-06-29
XIDIAN UNIV
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Existing hyperspectral compressive sensing sampling methods are carried out in the spatial domain or spectral domain alone, without joint consideration of the spatial domain correlation and spectral domain correlation of hyperspectral images; on the other hand, the existing hyperspectral compressive sensing sampling methods Among them, the sampling matrix adopts Gaussian random matrix, which is difficult to implement with hardware, and the cost is high
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[0039] refer to figure 1 , the specific implementation steps of the present invention are as follows:
[0040] Step 1: Design the sampling matrix according to the principle of spectral imaging:
[0041] 1.1) Given a spectral imaging system such as figure 2 As shown, where L1 is the telescopic objective lens, which is used to receive the reflected light from the ground; S is the slit, which is used to control the reflected light in a certain area of the ground to enter the imaging system; It becomes parallel light; Prism is a spectroscopic device, which is used to divide the incident light into different spectral segments; L3 is an imaging objective lens, which is used to image the incident light of different spectral bands on the CCD;
[0042] 1.2) The operating wavelength range of a given spectral imaging system is λ 1 ~λ 2 , and the number of imaging bands is K, then the spectral resolution of the spectral imaging system is Δλ=(λ 1 -λ 2 ) / K, the sampling value obtai...
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The invention discloses a method for recovering hyperspectral data by combining space and spectral domains based on compressive sensing, which mainly solves problems that the resource utilization rate in the prior art is low, resolution ratios of the space and spectral domains of the hyperspectral data are mutually restricted, and the resolution ratios of the space and spectral domains are difficult to improve and high in cost. The implementation steps are that a measurement matrix easy for hardware implementation is designed according to principles of a spectral imaging system; the hyperspectral data is divided into high-density sampling data and low-density sampling data, and space and spectral domain combined sampling is carried out on the high-density data and the low-density sampling data by using the measurement matrix; and space and spectral combined recovery is carried out on the hyperspectral data by using the space domain correlation and the spectral domain correlation of the hyperspectral data. The method disclosed by the invention can recover the hyperspectral data under a very low sampling rate, reduces the sampling rate greatly, improves the space and spectral domain resolution ratios of the hyperspectral data, and can be used for hyperspectral remote sensing imaging.
Description
technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for jointly restoring hyperspectral data based on compressed sensing, which can be used for low-rate sampling and restoration of hyperspectral images. Background technique [0002] Hyperspectral remote sensing technology has become an important means of remote sensing observation, widely used in meteorology, environmental detection, geological exploration, agricultural production, aerospace and military detection and other fields. The hyperspectral imaging system can obtain the geometric, radiation and spectral information of the ground objects in the imaging area, and is the core technology of hyperspectral remote sensing. [0003] Remote sensing hyperspectral imaging systems are generally used on satellites or airborne, making it very costly to increase spectral domain sampling to improve spectral resolution, or to increase airspace sampling to improve spatial r...
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IPC IPC(8): G06T5/00G06T9/00
Inventor 杨淑媛焦李成马永刚刘芳侯彪缑水平张向荣马文萍金鹏磊黄春海
Owner XIDIAN UNIV