High-spectral image classification method base on space spectral locality low-rank hypergraph learning

Abstract

The invention, which belongs to the technical field of image information processing, discloses a high-spectral image classification method base on space spectral locality low-rank hypergraph learning. The method comprises: high-spectral data set is inputted and a spectral feature matrix X is formed; a correlated parameter is set and is fused into a space spectral locality constraint to construct a low-rank expression model of the pace spectral locality constraint; a correlated parameter is set and iterative solution is carried out by using an alternating direction method of multipliers; according to a coefficient matrix Z in the low-rank expression model, a space spectral locality low-rank hypergraph is constructed; a correlated parameter is set and a semi-supervised hypergraph learning model is established; and a correlated parameter is set, iterative solving of a semi-supervised hypergraph model is carried out, and a classification result matrix F <*> of a spectral feature data set X is outputted. According to the invention, a semi-supervised hypergraph learning algorithm is applied to final high-spectral image classification. Compared with other advanced methods, the provided method has advantages of good classification effect and high robustness for noises and image degradation.

Description

technical field [0001] The invention relates to the technical field of image information processing, in particular to a hyperspectral image classification method based on spatial-spectral locality and low-rank hypergraph learning. Background technique [0002] Remote sensing imaging has been widely used in various fields, including environmental monitoring, urban planning, major disaster management, and precision agriculture applications. In most applications, remote sensing image classification based on pixel level is the first step. However, automatic classification of remote sensing images is still a very challenging problem. First, during the image acquisition process, there are various imaging degradation factors, such as noise interference, fringe erosion, and data loss caused by sensors, photon effects, and calibration errors. Second, due to the dense spectral sampling in the narrow bands of the electromagnetic spectrum, there will be a high degree of correlation be...

AI Technical Summary

Problems solved by technology

However, the automatic classification of remote sensing images is still a very challenging problem.

First, during the image acquisition process, there are various imaging degradation factors, such as noise interference, fringe corrosion, and data loss caused by sensors, photon effects, and calibration errors

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