High-spectral data noise reduction method and system based on spatial correlation

Abstract

The invention provides a high-spectral data noise reduction method and system based on spatial correlation. The high-spectral data noise reduction method comprises the steps of performing projection transformation, namely solving an average image of all images formed by all wave bands in high-spectral data, calculating a covariance matrix of the high-spectral data, performing eigen value decomposition to obtain a transformational matrix and an eigen value matrix, utilizing the transformational matrix to conduct linear projection on the high-spectral data so as to obtain three-dimensional data in a transformation domain; selecting a noise reduction threshold value according to the eigen value matrix; performing noise reduction, namely retaining the three-dimensional data smaller than the noise reduction threshold value in the transformation domain and performing noise reduction processing on the three-dimensional data out of a threshold value range in the transformation domain; performing inverse projection transformation, namely utilizing an inverse matrix of the transformational matrix to conduct linear projection on the three-dimensional data subjected to noise reduction in the transformation domain and obtaining the high-spectral data subjected to noise reduction through reconstruction. In the noise reduction process, the correlation of a spatial domain is fully protected, the noise magnitude of the high-spectral data is well reduced, and the signal to noise ratio of the high-spectral data is improved.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral data processing, and in particular relates to a hyperspectral data noise reduction method, which is used to reduce the noise level of hyperspectral data collected by sensors and improve the quality of hyperspectral data. Background technique [0002] Hyperspectral data is a multidimensional signal obtained using hyperspectral remote sensing technology, including two-dimensional image data and one-dimensional spectral data of the target area. Hyperspectral data generally includes hundreds of spectral information, and the wavelength range includes ultraviolet, visible light, infrared, etc., so at each sampling point, continuous spectral information is formed. In recent years, hyperspectral data has been applied in fields such as target recognition, agricultural and forestry monitoring, and map filling. However, in the process of acquisition and transmission, the hyperspectral image is subject...

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Problems solved by technology

Therefore, the noise reduction effect has certain limitations and cannot achieve a higher signal-to-noise ratio.

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