Hyperspectral compressed sensing method, device and system based on modified linear hybrid model

Abstract

The invention discloses a hyperspectral compressed sensing method, device and system based on a modified linear hybrid model. The method comprises the steps: S1, acquiring observation data Y = AX after spectral dimension compression sampling of an original hyperspectral image are acquired, wherein A is an observation matrix, and X is the original hyperspectral image to be reconstructed; s2, constructing a modified linear hybrid model: X = ES + BEH, wherein E is an end member matrix, S is an abundance matrix, B is a correction matrix of the end member E, H is an abundance matrix corresponding to the corrected end member; respectively estimating optimal values of B, S and H based on the observation data Y; and S3, substituting the estimated values of B, S and H into the modified linear hybrid model to reconstruct an original hyperspectral image. A correction item BEH is introduced into the correction linear hybrid model, self-adaptive correction can be carried out according to the disturbance condition of each point on the spectrum, the hyperspectral image characterization capability of the model is improved, and the reconstruction quality of hyperspectral compressed sensing is further improved.

Description

technical field [0001] The invention relates to the field of hyperspectral compressed sensing, in particular to a hyperspectral compressed sensing method, device and system based on a modified linear mixed model. Background technique [0002] Hyperspectral imagery (Hyperspectral Imagery, HSI) can provide rich spectral information of surface features, and has been widely used in mineral exploration, agricultural production, environment and disaster monitoring and other fields. However, as the resolution continues to improve, the amount of data acquired by imaging spectrometers increases exponentially. Massive hyperspectral data has brought enormous pressure on the power consumption, computing power and real-time transmission of data of airborne or spaceborne imaging systems. Compressed sensing (Compressed Sensing, CS) technology can collect data at a lower sampling rate (far lower than the Nyquist sampling rate), integrate data collection and compression, and complete data c...

AI Technical Summary

Problems solved by technology

The existing reconstruction algorithms based on spectral unmixing are all based on the traditional LMM; however, due to the influence of factors such as illumination conditions, terrain changes, and atmosphere, the obtained ground object imaging spectrum will be disturbed, thus deviating from the ground image. the true spectrum of the object, reducing the applicability of the LMM

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